var bibbase_data = {"data":"\"Loading..\"\n\n
\n\n \n\n \n\n \n \n\n \n\n \n \n\n \n\n \n
\n generated by\n \n \"bibbase.org\"\n\n \n
\n \n\n
\n\n \n\n\n
\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n
\n JavaScript\n (easiest)\n
\n \n <script src=\"https://bibbase.org/show?bib=https%3A%2F%2Fapi.zotero.org%2Fusers%2F890488%2Fcollections%2FCDTAG95F%2Fitems%3Fkey%3D5YrquAQTXNaw6czlZI1jyKfe%26format%3Dbibtex%26limit%3D100&jsonp=1&authorFirst=1&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/show?bib=https%3A%2F%2Fapi.zotero.org%2Fusers%2F890488%2Fcollections%2FCDTAG95F%2Fitems%3Fkey%3D5YrquAQTXNaw6czlZI1jyKfe%26format%3Dbibtex%26limit%3D100&jsonp=1&authorFirst=1\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n (not recommended)\n
\n \n <iframe src=\"https://bibbase.org/show?bib=https%3A%2F%2Fapi.zotero.org%2Fusers%2F890488%2Fcollections%2FCDTAG95F%2Fitems%3Fkey%3D5YrquAQTXNaw6czlZI1jyKfe%26format%3Dbibtex%26limit%3D100&jsonp=1&authorFirst=1\"></iframe>\n \n
\n\n

\n For more details see the documention.\n

\n
\n
\n\n
\n\n This is a preview! To use this list on your own web site\n or create a new web site from it,\n create a free account. The file will be added\n and you will be able to edit it in the File Manager.\n We will show you instructions once you've created your account.\n
\n\n
\n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

    \n
  1. renew the authorization for BibBase on Mendeley, and
    2. \n
  2. update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n
    3. \n
\n

\n\n

\n \n \n Fix it now\n

\n
\n\n
\n\n\n
\n \n \n
\n
\n  \n 2022\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n Sadri-Moshkenani, Z.; Bradley, J.; and Rothermel, G.\n\n\n \n \n \n \n \n Survey on test case generation, selection and prioritization for cyber-physical systems.\n \n \n \n \n\n\n \n\n\n\n Software Testing, Verification and Reliability, 32(1): e1794. 2022.\n tex.eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/stvr.1794\n\n\n\n
\n\n\n\n \n \n \"SurveyPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{sadrimoshkenani2021survey,\n\ttitle = {Survey on test case generation, selection and prioritization for cyber-physical systems},\n\tvolume = {32},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/stvr.1794},\n\tdoi = {10.1002/stvr.1794},\n\tabstract = {Summary A cyber-physical system (CPS) is a collection of computing devices that communicate with each other, operate in the target environment via actuators and interact with the physical world through sensors in a feedback loop. CPSs need to be safe and reliable and function in accordance with their requirements. Testing, focusing on a CPS model and/or its code, is the primary approach used by engineers to achieve this. Generating, selecting and prioritizing test cases that can reveal faults in CPSs, from the wide range of possible input values and stimuli that affect their operation, are of central importance in this process. To date, however, in our search of the literature, we have found no comprehensive survey of research on test case generation, selection and prioritization for CPSs. In this article, therefore, we report the results of a survey of approaches for generating, selecting and prioritizing test cases for CPSs; the results illustrate the progress that has been made on these approaches to date, the properties that characterize the approaches and the challenges that remain open in these areas of research.},\n\tnumber = {1},\n\tjournal = {Software Testing, Verification and Reliability},\n\tauthor = {Sadri-Moshkenani, Zahra and Bradley, Justin and Rothermel, Gregg},\n\tyear = {2022},\n\tnote = {tex.eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/stvr.1794},\n\tkeywords = {cyber-physical system, embedded-control systems, test case generation, test case prioritization, test case selection, testing},\n\tpages = {e1794},\n}\n\n
\n
\n\n\n
\n Summary A cyber-physical system (CPS) is a collection of computing devices that communicate with each other, operate in the target environment via actuators and interact with the physical world through sensors in a feedback loop. CPSs need to be safe and reliable and function in accordance with their requirements. Testing, focusing on a CPS model and/or its code, is the primary approach used by engineers to achieve this. Generating, selecting and prioritizing test cases that can reveal faults in CPSs, from the wide range of possible input values and stimuli that affect their operation, are of central importance in this process. To date, however, in our search of the literature, we have found no comprehensive survey of research on test case generation, selection and prioritization for CPSs. In this article, therefore, we report the results of a survey of approaches for generating, selecting and prioritizing test cases for CPSs; the results illustrate the progress that has been made on these approaches to date, the properties that characterize the approaches and the challenges that remain open in these areas of research.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2021\n \n \n (3)\n \n \n
\n
\n \n \n
\n \n\n \n \n Zhang, X.; and Bradley, J.\n\n\n \n \n \n \n \n Computing for Control and Control for Computing.\n \n \n \n \n\n\n \n\n\n\n In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), pages 469–474, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"ComputingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{zhang2021computing,\n\ttitle = {Computing for {Control} and {Control} for {Computing}},\n\turl = {https://10.23919/DATE51398.2021.9474058},\n\tdoi = {10.23919/DATE51398.2021.9474058},\n\tbooktitle = {2021 {Design}, {Automation} \\& {Test} in {Europe} {Conference} \\& {Exhibition} ({DATE})},\n\tauthor = {Zhang, Xinkai and Bradley, Justin},\n\tyear = {2021},\n\tpages = {469--474},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Manyam, S. G.; Casbeer, D.; Weintraub, I. E.; Tran, D. M.; Bradley, J. M.; and Darbha, S.\n\n\n \n \n \n \n \n Quadratic Bezier Curves for Multi-Agent Coordinated Arrival in the Presence of Obstacles.\n \n \n \n \n\n\n \n\n\n\n In AIAA Scitech 2021 Forum, 2021. \n tex.eprint: https://arc.aiaa.org/doi/pdf/10.2514/6.2021-1879\n\n\n\n
\n\n\n\n \n \n \"QuadraticPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{manyam2021quadratic,\n\ttitle = {Quadratic {Bezier} {Curves} for {Multi}-{Agent} {Coordinated} {Arrival} in the {Presence} of {Obstacles}},\n\turl = {https://arc.aiaa.org/doi/abs/10.2514/6.2021-1879},\n\tdoi = {10.2514/6.2021-1879},\n\tabstract = {We consider a multiple vehicle path planning problem with curvature constraints in the presence of obstacles, where multiple vehicles need to arrive at a given final location simultaneously. We aim to find the paths using a simplex framework that tessellates the feasible regions into hexagonal grids from which a graph is abstracted with nodes at the mid-point of every hexagon edge. The graph edges are defined over the adjacent nodes of the hexagons and each edge corresponds to a quadratic Bezier curve. We present an algorithm that finds the shortest path for each vehicle, and a path perturbation technique to make the path lengths equal with in a given error tolerance. We test the proposed approach using simulated scenarios and present the results.},\n\tbooktitle = {{AIAA} {Scitech} 2021 {Forum}},\n\tauthor = {Manyam, Satyanarayana G. and Casbeer, David and Weintraub, Isaac E. and Tran, Dzung M. and Bradley, Justin M. and Darbha, Swaroop},\n\tyear = {2021},\n\tnote = {tex.eprint: https://arc.aiaa.org/doi/pdf/10.2514/6.2021-1879},\n}\n\n
\n
\n\n\n
\n We consider a multiple vehicle path planning problem with curvature constraints in the presence of obstacles, where multiple vehicles need to arrive at a given final location simultaneously. We aim to find the paths using a simplex framework that tessellates the feasible regions into hexagonal grids from which a graph is abstracted with nodes at the mid-point of every hexagon edge. The graph edges are defined over the adjacent nodes of the hexagons and each edge corresponds to a quadratic Bezier curve. We present an algorithm that finds the shortest path for each vehicle, and a path perturbation technique to make the path lengths equal with in a given error tolerance. We test the proposed approach using simulated scenarios and present the results.\n
\n\n\n
\n\n\n
\n \n\n \n \n Muvva, K.; Bradley, J. M.; Wolf, M.; and Johnson, T.\n\n\n \n \n \n \n \n Assuring Learning-Enabled Components in Small Unmanned Aircraft Systems.\n \n \n \n \n\n\n \n\n\n\n In AIAA Scitech 2021 Forum, 2021. \n tex.eprint: https://arc.aiaa.org/doi/pdf/10.2514/6.2021-0994\n\n\n\n
\n\n\n\n \n \n \"AssuringPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{muvva2021assuring,\n\ttitle = {Assuring {Learning}-{Enabled} {Components} in {Small} {Unmanned} {Aircraft} {Systems}},\n\turl = {https://arc.aiaa.org/doi/abs/10.2514/6.2021-0994},\n\tdoi = {10.2514/6.2021-0994},\n\tabstract = {Aviation has a remarkable safety record ensured by strict processes, rules, certifications, and regulations, in which formal methods have played a role in large companies developing commercial aerospace vehicles and related cyber-physical systems (CPS). This has not been the case for small Unmanned Aircraft Systems (UAS) that are still largely unregulated, uncertified, and not fully integrated into the national airspace. However, emerging UAS missions interact closely with the environment and utilize learning-enabled components (LECs), such as neural networks (NNs) for many tasks. Applying formal methods in this context will enable improved safety and ease the immersion of UASs into the national airspace. We develop UAS that interact closely with the environment, interact with human users, and require precise plans, navigation, and controllers. They also generally leverage LECs for perception and data collection. However, the impact of ML-based LECs on UAS performance is still an area of research. We have developed an advanced simulator incorporating ML-based perception in highly dynamic situations requiring advanced control strategies to study the impacts of ML-based perception on holistic UAS performance. In other work, we have developed a WebGME-based software framework called the Assurance-based Learning-enabled CPS (ALC) toolchain for designing CPS that incorporate LECs, including the Neural Network Verification (NNV) formal verification tool. In this paper, we present two key developments: 1) a quantification of the impact of ML-based perception on holistic (physical and cyber) UAS performance, and 2) a discussion of challenges in applying these methods in this environment to guarantee UAS performance under various Neural Net (NN) strategies, executed at various computational rates, and with vehicles moving at various speeds. We demonstrate that vehicle dynamics, rate of perception execution, the design of the controller, and the design of the NN all contributed to total vehicle performance.},\n\tbooktitle = {{AIAA} {Scitech} 2021 {Forum}},\n\tauthor = {Muvva, Krishna and Bradley, Justin M. and Wolf, Marilyn and Johnson, Taylor},\n\tyear = {2021},\n\tnote = {tex.eprint: https://arc.aiaa.org/doi/pdf/10.2514/6.2021-0994},\n}\n\n
\n
\n\n\n
\n Aviation has a remarkable safety record ensured by strict processes, rules, certifications, and regulations, in which formal methods have played a role in large companies developing commercial aerospace vehicles and related cyber-physical systems (CPS). This has not been the case for small Unmanned Aircraft Systems (UAS) that are still largely unregulated, uncertified, and not fully integrated into the national airspace. However, emerging UAS missions interact closely with the environment and utilize learning-enabled components (LECs), such as neural networks (NNs) for many tasks. Applying formal methods in this context will enable improved safety and ease the immersion of UASs into the national airspace. We develop UAS that interact closely with the environment, interact with human users, and require precise plans, navigation, and controllers. They also generally leverage LECs for perception and data collection. However, the impact of ML-based LECs on UAS performance is still an area of research. We have developed an advanced simulator incorporating ML-based perception in highly dynamic situations requiring advanced control strategies to study the impacts of ML-based perception on holistic UAS performance. In other work, we have developed a WebGME-based software framework called the Assurance-based Learning-enabled CPS (ALC) toolchain for designing CPS that incorporate LECs, including the Neural Network Verification (NNV) formal verification tool. In this paper, we present two key developments: 1) a quantification of the impact of ML-based perception on holistic (physical and cyber) UAS performance, and 2) a discussion of challenges in applying these methods in this environment to guarantee UAS performance under various Neural Net (NN) strategies, executed at various computational rates, and with vehicles moving at various speeds. We demonstrate that vehicle dynamics, rate of perception execution, the design of the controller, and the design of the NN all contributed to total vehicle performance.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2020\n \n \n (6)\n \n \n
\n
\n \n \n
\n \n\n \n \n Shang, Z.; Bradley, J.; and Shen, Z.\n\n\n \n \n \n \n \n A Co-optimal Coverage Path Planning Method for Aerial Scanning of Complex Structures.\n \n \n \n \n\n\n \n\n\n\n Expert Systems with Applications. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{shang2020cooptimal,\n\ttitle = {A {Co}-optimal {Coverage} {Path} {Planning} {Method} for {Aerial} {Scanning} of {Complex} {Structures}},\n\turl = {https://doi.org/10.1016/j.eswa.2020.113535},\n\tdoi = {10.1016/j.eswa.2020.113535},\n\tjournal = {Expert Systems with Applications},\n\tauthor = {Shang, Zhexiong and Bradley, Justin and Shen, Zhigang},\n\tyear = {2020},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Plowcha, A.; Hogberg, J.; Detweiler, C.; and Bradley, J.\n\n\n \n \n \n \n \n Online Soil Classification using a UAS Sensor Emplacement System.\n \n \n \n \n\n\n \n\n\n\n In International Symposium on Experimental Robotics (ISER), pages 174–184, 2020. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n \n \"OnlinePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{plowcha2021online,\n\ttitle = {Online {Soil} {Classification} using a {UAS} {Sensor} {Emplacement} {System}},\n\turl = {https://doi.org/10.1007/978-3-030-71151-1_16},\n\tdoi = {10.1007/978-3-030-71151-1_16},\n\tbooktitle = {International {Symposium} on {Experimental} {Robotics} ({ISER})},\n\tpublisher = {Springer International Publishing},\n\tauthor = {Plowcha, Adam and Hogberg, Jacob and Detweiler, Carrick and Bradley, Justin},\n\tyear = {2020},\n\tpages = {174--184},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Balasubramaniam, B.; Bagheri, H.; Elbaum, S.; and Bradley, J.\n\n\n \n \n \n \n Investigating Controller Evolution and Divergence through Mining and Mutation*.\n \n \n \n\n\n \n\n\n\n In 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), pages 151–161, April 2020. \n ISSN: 2642-9500\n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{balasubramaniam2020investigating,\n\ttitle = {Investigating {Controller} {Evolution} and {Divergence} through {Mining} and {Mutation}*},\n\tdoi = {10.1109/ICCPS48487.2020.00022},\n\tabstract = {Successful cyber-physical system controllers evolve as they are refined, extended, and adapted to new systems and contexts. This evolution occurs in the controller design and also in its software implementation. Model-based design and controller synthesis can help to synchronize this evolution of design and software, but such synchronization is rarely complete as software tends to also evolve in response to elements rarely present in a control model, leading to mismatches between the control design and the software. In this paper we perform a first-of-it-skind study on the evolution of two popular open-source safety-critical autopilot control software – ArduPilot, and Paparazzi, to better understand how controllers evolve and the space of potential mismatches between control design and their software implementation. We then use that understanding to prototype a technique that can generate mutated versions of code to mimic evolution to assess its impact on a controller’s behavior.We find that 1) control software evolves quickly and controllers are rewritten in their entirety over their lifetime, implying that the design, synthesis, and implementation of controllers should also support incremental evolution, 2) many software changes stem from an inherent mismatch between continuous physical models and their corresponding discrete software implementation, but also from the mishandling of exceptional conditions, and limitations and distinct data representation of the underlying computing architecture, 3) small code changes can have a dramatic effect in a controller’s behavior, implying that further support is needed to bridge these mismatches as carefully verified model properties may not necessarily translate to its software implementation.},\n\tbooktitle = {2020 {ACM}/{IEEE} 11th {International} {Conference} on {Cyber}-{Physical} {Systems} ({ICCPS})},\n\tauthor = {Balasubramaniam, Balaji and Bagheri, Hamid and Elbaum, Sebastian and Bradley, Justin},\n\tmonth = apr,\n\tyear = {2020},\n\tnote = {ISSN: 2642-9500},\n\tkeywords = {Atmospheric modeling, Computational modeling, Control design, Mathematical model, Software, Tools},\n\tpages = {151--161},\n}\n\n
\n
\n\n\n
\n Successful cyber-physical system controllers evolve as they are refined, extended, and adapted to new systems and contexts. This evolution occurs in the controller design and also in its software implementation. Model-based design and controller synthesis can help to synchronize this evolution of design and software, but such synchronization is rarely complete as software tends to also evolve in response to elements rarely present in a control model, leading to mismatches between the control design and the software. In this paper we perform a first-of-it-skind study on the evolution of two popular open-source safety-critical autopilot control software – ArduPilot, and Paparazzi, to better understand how controllers evolve and the space of potential mismatches between control design and their software implementation. We then use that understanding to prototype a technique that can generate mutated versions of code to mimic evolution to assess its impact on a controller’s behavior.We find that 1) control software evolves quickly and controllers are rewritten in their entirety over their lifetime, implying that the design, synthesis, and implementation of controllers should also support incremental evolution, 2) many software changes stem from an inherent mismatch between continuous physical models and their corresponding discrete software implementation, but also from the mishandling of exceptional conditions, and limitations and distinct data representation of the underlying computing architecture, 3) small code changes can have a dramatic effect in a controller’s behavior, implying that further support is needed to bridge these mismatches as carefully verified model properties may not necessarily translate to its software implementation.\n
\n\n\n
\n\n\n
\n \n\n \n \n Fernando, C.; Detweiler, C.; and Bradley, J.\n\n\n \n \n \n \n \n Co-Regulated Information Consensus with Delays for Multi-Agent UAS.\n \n \n \n \n\n\n \n\n\n\n IEEE Conference on Decision and Control. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"Co-RegulatedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{fernando2020coregulated,\n\ttitle = {Co-{Regulated} {Information} {Consensus} with {Delays} for {Multi}-{Agent} {UAS}},\n\turl = {https://doi.org/10.1109/CDC42340.2020.9304068},\n\tdoi = {10.1109/CDC42340.2020.9304068},\n\tjournal = {IEEE Conference on Decision and Control},\n\tauthor = {Fernando, Chandima and Detweiler, Carrick and Bradley, Justin},\n\tyear = {2020},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Zhang, X.; and Bradley, J.\n\n\n \n \n \n \n Controller Design for Time-varying Sampling, Co-Regulated Systems.\n \n \n \n\n\n \n\n\n\n IEEE Control Systems Letters,1–1. 2020.\n Conference Name: IEEE Control Systems Letters\n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{zhang_controller_2020,\n\ttitle = {Controller {Design} for {Time}-varying {Sampling}, {Co}-{Regulated} {Systems}},\n\tissn = {2475-1456},\n\tdoi = {10.1109/LCSYS.2020.3044261},\n\tabstract = {“Co-regulation” is a time-varying periodic sampling strategy wherein the sampling rate is dynamically adjusted in response to the performance of the controlled system. The controller for co-regulated system needs to adjust control outputs corresponding to the current (changing) sampling rate. This makes performance guarantees such as stability difficult to obtain. In this paper we develop two stability guaranteed control algorithms for co-regulated systems. First is a correct-by-construction stabilizing controller where the control gain matrices are pre-computed offline for a set of sampling rates. This method allows for arbitrary switching of the sampling rates but as a result can be overly conservative. Then a hybrid Model Predictive Control (MPC) algorithm is tailored for co-regulated systems where both the system state trajectory and the sampling rate (scheduling parameter) trajectory are predicted within the receding horizon. The performances of the proposed controllers are demonstrated and discussed for a co-regulated multicopter Unmanned Aircraft System (UAS). The results show co-regulation can efficiently reallocate computational resources based on control performance by varying the sampling rate at runtime, while the proposed control strategies can guarantee co-regulated system stability when working under a time-varying sampling rate.},\n\tjournal = {IEEE Control Systems Letters},\n\tauthor = {Zhang, X. and Bradley, J.},\n\tyear = {2020},\n\tnote = {Conference Name: IEEE Control Systems Letters},\n\tkeywords = {Control applications., Lyapunov methods, Sampled-data control, Time-varying systems},\n\tpages = {1--1},\n}\n\n
\n
\n\n\n
\n “Co-regulation” is a time-varying periodic sampling strategy wherein the sampling rate is dynamically adjusted in response to the performance of the controlled system. The controller for co-regulated system needs to adjust control outputs corresponding to the current (changing) sampling rate. This makes performance guarantees such as stability difficult to obtain. In this paper we develop two stability guaranteed control algorithms for co-regulated systems. First is a correct-by-construction stabilizing controller where the control gain matrices are pre-computed offline for a set of sampling rates. This method allows for arbitrary switching of the sampling rates but as a result can be overly conservative. Then a hybrid Model Predictive Control (MPC) algorithm is tailored for co-regulated systems where both the system state trajectory and the sampling rate (scheduling parameter) trajectory are predicted within the receding horizon. The performances of the proposed controllers are demonstrated and discussed for a co-regulated multicopter Unmanned Aircraft System (UAS). The results show co-regulation can efficiently reallocate computational resources based on control performance by varying the sampling rate at runtime, while the proposed control strategies can guarantee co-regulated system stability when working under a time-varying sampling rate.\n
\n\n\n
\n\n\n
\n \n\n \n \n Bradley, J. M; and Bagheri, H.\n\n\n \n \n \n \n \n Control Software: Research Directions in the Intersection of Control Theory and Software Engineering.\n \n \n \n \n\n\n \n\n\n\n In AIAA Scitech 2020 Forum, pages 2102, 2020. \n \n\n\n\n
\n\n\n\n \n \n \"ControlPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{bradley2020control,\n\ttitle = {Control {Software}: {Research} {Directions} in the {Intersection} of {Control} {Theory} and {Software} {Engineering}},\n\turl = {https://doi.org/10.2514/6.2020-2102},\n\tdoi = {10.2514/6.2020-2102},\n\tbooktitle = {{AIAA} {Scitech} 2020 {Forum}},\n\tauthor = {Bradley, Justin M and Bagheri, Hamid},\n\tyear = {2020},\n\tpages = {2102},\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2019\n \n \n (6)\n \n \n
\n
\n \n \n
\n \n\n \n \n Zhang, X.; and Bradley, J.\n\n\n \n \n \n \n \n Stability Analysis for a Class of Resource-Aware, Co-Regulated Systems.\n \n \n \n \n\n\n \n\n\n\n In 2019 IEEE 58th Conference on Decision and Control (CDC), pages 193–200, December 2019. \n ISSN: 2576-2370\n\n\n\n
\n\n\n\n \n \n \"StabilityPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{zhang2019stability,\n\ttitle = {Stability {Analysis} for a {Class} of {Resource}-{Aware}, {Co}-{Regulated} {Systems}},\n\turl = {https://doi.org/10.1109/CDC40024.2019.9029267},\n\tdoi = {10.1109/CDC40024.2019.9029267},\n\tabstract = {In this paper we develop four methods for proving stability for a subclass of co-regulated systems - finite-state, co-regulated systems with restrictions on possible sampling rates. "Co-regulation" is a control strategy we previously developed wherein cyber and physical effectors are dynamically adjusted in response to holistic system performance. The cyber effector, sampling rate, is adjusted in response to off-nominal conditions in the controlled system, and the physical effector adjusts control outputs corresponding to the current (changing) sampling rate. The resulting computer-control system is a discrete-time-varying system with changing zero-order holds and sampling periods, and unknown delays over discrete intervals. This makes performance guarantees such as stability difficult to obtain.We address this difficulty by drawing from specialized results in the control community to develop four methods for proving asymptotic stability of finite-state, co-regulated systems. Each successive method relaxes the assumptions needed to guarantee stability. This lays the groundwork for a more all-encompassing analytical framework for co-regulated systems. We use the results to demonstrate stability for a co-regulated multicopter unmanned aircraft system.},\n\tbooktitle = {2019 {IEEE} 58th {Conference} on {Decision} and {Control} ({CDC})},\n\tauthor = {Zhang, X. and Bradley, J.},\n\tmonth = dec,\n\tyear = {2019},\n\tnote = {ISSN: 2576-2370},\n\tkeywords = {Asymptotic stability, Computational modeling, Control systems, Mathematical model, Real-time systems, Stability analysis, Task analysis, asymptotic stability, co-regulated systems, computer-control system, control community, controlled system, cyber-physical effector adjusts, delays, discrete time systems, discrete time varying system, holistic system performance, multicopter unmanned aircraft system, sampling rate, time-varying systems},\n\tpages = {193--200},\n}\n\n
\n
\n\n\n
\n In this paper we develop four methods for proving stability for a subclass of co-regulated systems - finite-state, co-regulated systems with restrictions on possible sampling rates. \"Co-regulation\" is a control strategy we previously developed wherein cyber and physical effectors are dynamically adjusted in response to holistic system performance. The cyber effector, sampling rate, is adjusted in response to off-nominal conditions in the controlled system, and the physical effector adjusts control outputs corresponding to the current (changing) sampling rate. The resulting computer-control system is a discrete-time-varying system with changing zero-order holds and sampling periods, and unknown delays over discrete intervals. This makes performance guarantees such as stability difficult to obtain.We address this difficulty by drawing from specialized results in the control community to develop four methods for proving asymptotic stability of finite-state, co-regulated systems. Each successive method relaxes the assumptions needed to guarantee stability. This lays the groundwork for a more all-encompassing analytical framework for co-regulated systems. We use the results to demonstrate stability for a co-regulated multicopter unmanned aircraft system.\n
\n\n\n
\n\n\n
\n \n\n \n \n Kruse, L.; Bradley, J.; and Wolf, M.\n\n\n \n \n \n \n \n A Control Authority Switching System for Avoiding Multicopter Loss of Control Using a Markov Decision Process.\n \n \n \n \n\n\n \n\n\n\n In 2019 AIAA SciTech Forum, San Diego, CA, January 2019. American Institute of Aeronautics and Astronautics\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{kruse2019control,\n\taddress = {San Diego, CA},\n\ttitle = {A {Control} {Authority} {Switching} {System} for {Avoiding} {Multicopter} {Loss} of {Control} {Using} a {Markov} {Decision} {Process}},\n\turl = {https://doi.org/10.2514/6.2019-1688},\n\tdoi = {10.2514/6.2019-1688},\n\tbooktitle = {2019 {AIAA} {SciTech} {Forum}},\n\tpublisher = {American Institute of Aeronautics and Astronautics},\n\tauthor = {Kruse, Liam and Bradley, Justin and Wolf, Marilyn},\n\tmonth = jan,\n\tyear = {2019},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Yeo, D.; Rehm, N.; Bradley, J.; and Chopra, I.\n\n\n \n \n \n \n \n Flow-Aware Computational Wings for Improved Gust Mitigation on Fixed-Wing Unmanned Aerial Systems.\n \n \n \n \n\n\n \n\n\n\n In 2019 AIAA SciTech Forum, San Diego, CA, January 2019. American Institute of Aeronautics and Astronautics\n \n\n\n\n
\n\n\n\n \n \n \"Flow-AwarePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{yeo2019flowaware,\n\taddress = {San Diego, CA},\n\ttitle = {Flow-{Aware} {Computational} {Wings} for {Improved} {Gust} {Mitigation} on {Fixed}-{Wing} {Unmanned} {Aerial} {Systems}},\n\turl = {https://doi.org/10.2514/6.2019-1689},\n\tdoi = {10.2514/6.2019-1689},\n\tbooktitle = {2019 {AIAA} {SciTech} {Forum}},\n\tpublisher = {American Institute of Aeronautics and Astronautics},\n\tauthor = {Yeo, Derrick and Rehm, Nicholas and Bradley, Justin and Chopra, Inderjit},\n\tmonth = jan,\n\tyear = {2019},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Li, H.; Qian, Y.; Asgarpoor, S.; and Bradley, J.\n\n\n \n \n \n \n \n PMSM Current Management with Overcurrent Regulation.\n \n \n \n \n\n\n \n\n\n\n In 2019 IEEE Applied Power Electronics Conference and Exposition (APEC), Anaheim, CA, March 2019. 2019 IEEE Applied Power Electronics Conference and Exposition (APEC)\n \n\n\n\n
\n\n\n\n \n \n \"PMSMPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{li2019pmsm,\n\taddress = {Anaheim, CA},\n\ttitle = {{PMSM} {Current} {Management} with {Overcurrent} {Regulation}},\n\turl = {https://ieeexplore.ieee.org/abstract/document/8722290},\n\tdoi = {10.1109/APEC.2019.8722290},\n\tabstract = {In many vehicle systems, the permanent magnet synchronous machines (PMSMs) are critical components which provide system propulsion. This work proposes a PMSM current management method with overcurrent regulation. The proposed current management method aims at achieving online machine current trajectory tracking and overcurrent regulation. The current trajectory tracking is to explore optimal current commands which ensure maximum system efficiency or maximum torque under machine voltage capability, and the overcurrent regulation is to limit machine current and thereby enhance the system reliability by reducing overcurrent risk of machine and inverter. The proposed method is easy to implement, capable of achieving online overcurrent regulation while maintaining maximum torque per ampere (MTPA) and maximum torque per voltage (MTPV) control without requiring offline calibration, and flexible to tune under changing machine current constraint and system parameter variations.},\n\tbooktitle = {2019 {IEEE} {Applied} {Power} {Electronics} {Conference} and {Exposition} ({APEC})},\n\tpublisher = {2019 IEEE Applied Power Electronics Conference and Exposition (APEC)},\n\tauthor = {Li, Haibo and Qian, Yi and Asgarpoor, Sohrab and Bradley, Justin},\n\tmonth = mar,\n\tyear = {2019},\n}\n\n
\n
\n\n\n
\n In many vehicle systems, the permanent magnet synchronous machines (PMSMs) are critical components which provide system propulsion. This work proposes a PMSM current management method with overcurrent regulation. The proposed current management method aims at achieving online machine current trajectory tracking and overcurrent regulation. The current trajectory tracking is to explore optimal current commands which ensure maximum system efficiency or maximum torque under machine voltage capability, and the overcurrent regulation is to limit machine current and thereby enhance the system reliability by reducing overcurrent risk of machine and inverter. The proposed method is easy to implement, capable of achieving online overcurrent regulation while maintaining maximum torque per ampere (MTPA) and maximum torque per voltage (MTPV) control without requiring offline calibration, and flexible to tune under changing machine current constraint and system parameter variations.\n
\n\n\n
\n\n\n
\n \n\n \n \n Lussier, M.; Bradley, J. M.; and Detweiler, C.\n\n\n \n \n \n \n \n Extending Endurance of Multicopters: The Current State-of-the-Art.\n \n \n \n \n\n\n \n\n\n\n In 2019 AIAA SciTech Forum, San Diego, CA, January 2019. American Institute of Aeronautics and Astronautics\n \n\n\n\n
\n\n\n\n \n \n \"ExtendingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{lussier2019extending,\n\taddress = {San Diego, CA},\n\ttitle = {Extending {Endurance} of {Multicopters}: {The} {Current} {State}-of-the-{Art}},\n\turl = {https://doi.org/10.2514/6.2019-1790},\n\tdoi = {10.2514/6.2019-1790},\n\tbooktitle = {2019 {AIAA} {SciTech} {Forum}},\n\tpublisher = {American Institute of Aeronautics and Astronautics},\n\tauthor = {Lussier, Marc and Bradley, Justin M. and Detweiler, Carrick},\n\tmonth = jan,\n\tyear = {2019},\n\tkeywords = {NIFA 2017-67021-25924, NSF 1638099},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Fernando, C.; Detweiler, C.; and Bradley, J.\n\n\n \n \n \n \n \n Co-Regulated Consensus of Cyber-Physical Resources in Multi-Agent Unmanned Aircraft Systems.\n \n \n \n \n\n\n \n\n\n\n Electronics, 8(5). 2019.\n tex.article-number: 569\n\n\n\n
\n\n\n\n \n \n \"Co-RegulatedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{fernando2019coregulated,\n\ttitle = {Co-{Regulated} {Consensus} of {Cyber}-{Physical} {Resources} in {Multi}-{Agent} {Unmanned} {Aircraft} {Systems}},\n\tvolume = {8},\n\tissn = {2079-9292},\n\turl = {http://www.mdpi.com/2079-9292/8/5/569},\n\tdoi = {10.3390/electronics8050569},\n\tabstract = {Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system.},\n\tnumber = {5},\n\tjournal = {Electronics},\n\tauthor = {Fernando, Chandima and Detweiler, Carrick and Bradley, Justin},\n\tyear = {2019},\n\tnote = {tex.article-number: 569},\n\tkeywords = {NIFA 2017-67021-25924, NSF 1638099},\n}\n\n
\n
\n\n\n
\n Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2018\n \n \n (8)\n \n \n
\n
\n \n \n
\n \n\n \n \n Acharya, U.; Kunde, S.; Hall, L.; Duncan, B.; and Bradley, J.\n\n\n \n \n \n \n Inference of User Qualities in Shared Control.\n \n \n \n\n\n \n\n\n\n In 2018 IEEE International Conference on Robotics and Automation, pages 588–595, Brisbane, Australia, May 2018. 2018 IEEE International Conference on Robotics and Automation\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{acharya2018inference,\n\taddress = {Brisbane, Australia},\n\ttitle = {Inference of {User} {Qualities} in {Shared} {Control}},\n\tdoi = {10.1109/ICRA.2018.8461193},\n\tabstract = {Users play an integral role in the performance of many robotic systems, and robotic systems must account for differences in users to improve collaborative performance. Much of the work in adapting to users has focused on designing teleoperation controllers that adjust to extrinsic user indicators such as force, or intent, but do not adjust to intrinsic user qualities. In contrast, the Human-Robot Interaction community has extensively studied intrinsic user qualities, but results may not rapidly be fed back into autonomy design. Here we provide foundational evidence for a new strategy that augments current shared control, and provide a mechanism to directly feed back results from the HRI community into autonomy design. Our evidence is based on a study examining the impact of the user quality “locus of control” on telepresence robot performance. Our results support our hypothesis that key user qualities can be inferred from human-robot interactions (such as through path deviation or time to completion) and that switching or adaptive autonomies might improve shared control performance.},\n\tbooktitle = {2018 {IEEE} {International} {Conference} on {Robotics} and {Automation}},\n\tpublisher = {2018 IEEE International Conference on Robotics and Automation},\n\tauthor = {Acharya, Urja and Kunde, Siya and Hall, Lucas and Duncan, Brittany and Bradley, Justin},\n\tmonth = may,\n\tyear = {2018},\n\tkeywords = {Collision avoidance, Force, Human-Robot Interaction, Human-robot interaction, NSF 1638099, Robots, System performance, Task analysis, Telepresence, collaborative performance, groupware, human-robot interaction, locus of control, robotic systems, shared control, teleoperation controllers, telepresence robot, telerobotics},\n\tpages = {588--595},\n}\n\n
\n
\n\n\n
\n Users play an integral role in the performance of many robotic systems, and robotic systems must account for differences in users to improve collaborative performance. Much of the work in adapting to users has focused on designing teleoperation controllers that adjust to extrinsic user indicators such as force, or intent, but do not adjust to intrinsic user qualities. In contrast, the Human-Robot Interaction community has extensively studied intrinsic user qualities, but results may not rapidly be fed back into autonomy design. Here we provide foundational evidence for a new strategy that augments current shared control, and provide a mechanism to directly feed back results from the HRI community into autonomy design. Our evidence is based on a study examining the impact of the user quality “locus of control” on telepresence robot performance. Our results support our hypothesis that key user qualities can be inferred from human-robot interactions (such as through path deviation or time to completion) and that switching or adaptive autonomies might improve shared control performance.\n
\n\n\n
\n\n\n
\n \n\n \n \n Zhang, X.; Doebbeling, S.; and Bradley, J.\n\n\n \n \n \n \n \n Co-regulation of Computational and Physical Effectors in a Quadrotor Unmanned Aircraft System.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems, pages 119–129, Porto, Portugal, 2018. Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems\n \n\n\n\n
\n\n\n\n \n \n \"Co-regulationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{zhang2018coregulation,\n\taddress = {Porto, Portugal},\n\ttitle = {Co-regulation of {Computational} and {Physical} {Effectors} in a {Quadrotor} {Unmanned} {Aircraft} {System}},\n\tisbn = {978-1-5386-5301-2},\n\turl = {https://doi.org/10.1109/ICCPS.2018.00020},\n\tdoi = {10.1109/ICCPS.2018.00020},\n\tbooktitle = {Proceedings of the 9th {ACM}/{IEEE} {International} {Conference} on {Cyber}-{Physical} {Systems}},\n\tpublisher = {Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems},\n\tauthor = {Zhang, Xinkai and Doebbeling, Seth and Bradley, Justin},\n\tyear = {2018},\n\tkeywords = {NIFA 2017-67021-25924, NSF 1638099},\n\tpages = {119--129},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Fernando, C.; Detweiler, C.; and Bradley, J.\n\n\n \n \n \n \n \n Co-Regulating Communication for Asynchronous Information Consensus.\n \n \n \n \n\n\n \n\n\n\n In 2018 IEEE Conference on Decision and Control (CDC), pages 6994–7001, Miami Beach, FL, December 2018. 2018 IEEE Conference on Decision and Control (CDC)\n \n\n\n\n
\n\n\n\n \n \n \"Co-RegulatingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{fernando2018coregulating,\n\taddress = {Miami Beach, FL},\n\ttitle = {Co-{Regulating} {Communication} for {Asynchronous} {Information} {Consensus}},\n\turl = {https://ieeexplore.ieee.org/document/8619787},\n\tdoi = {10.1109/CDC.2018.8619787},\n\tabstract = {Muti-agentconsensus controllers typically use discrete communication and hence are restricted to fixed-rate or event-triggered communication. Fixed-rate communication suffers from inefficient use of communication and computational resources but is easy to implement, while event-triggered communication conserves resources but suffers from the ambiguity of all event-triggered systems-inability to distinguish failure from lack of new information. We propose a novel hybrid strategy of co-regulating communication with state disagreement amongst the agents obtaining the benefits of discrete fixed-rate and event-triggered consensus while mitigating the associated disadvantages. Our approach dynamically adjusts the communication rate in response to disagreement in the shared state variable, resulting in a discrete-time-varying, asynchronous network topology. We prove convergence properties of the proposed consensus algorithm, develop metrics to evaluate similar dynamic approaches, and demonstrate the results in simulation, showing our algorithm reduces communication resources, while maintaining fast convergence time.},\n\tbooktitle = {2018 {IEEE} {Conference} on {Decision} and {Control} ({CDC})},\n\tpublisher = {2018 IEEE Conference on Decision and Control (CDC)},\n\tauthor = {Fernando, C. and Detweiler, C. and Bradley, J.},\n\tmonth = dec,\n\tyear = {2018},\n\tkeywords = {NIFA 2017-67021-25924, NSF 1638099},\n\tpages = {6994--7001},\n}\n\n
\n
\n\n\n
\n Muti-agentconsensus controllers typically use discrete communication and hence are restricted to fixed-rate or event-triggered communication. Fixed-rate communication suffers from inefficient use of communication and computational resources but is easy to implement, while event-triggered communication conserves resources but suffers from the ambiguity of all event-triggered systems-inability to distinguish failure from lack of new information. We propose a novel hybrid strategy of co-regulating communication with state disagreement amongst the agents obtaining the benefits of discrete fixed-rate and event-triggered consensus while mitigating the associated disadvantages. Our approach dynamically adjusts the communication rate in response to disagreement in the shared state variable, resulting in a discrete-time-varying, asynchronous network topology. We prove convergence properties of the proposed consensus algorithm, develop metrics to evaluate similar dynamic approaches, and demonstrate the results in simulation, showing our algorithm reduces communication resources, while maintaining fast convergence time.\n
\n\n\n
\n\n\n
\n \n\n \n \n Hall, L.; Acharya, U.; Bradley, J.; and Duncan, B.\n\n\n \n \n \n \n \n Inference of User Qualities in Shared Control of CPHS: A Contrast in Users.\n \n \n \n \n\n\n \n\n\n\n In 2018 IFAC Cyber-Physical Human Systems, Miami, FL, December 2018. 2018 IFAC Cyber-Physical Human Systems\n \n\n\n\n
\n\n\n\n \n \n \"InferencePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{hall2018inference,\n\taddress = {Miami, FL},\n\ttitle = {Inference of {User} {Qualities} in {Shared} {Control} of {CPHS}: {A} {Contrast} in {Users}},\n\turl = {https://doi.org/10.1016/j.ifacol.2019.01.047},\n\tdoi = {10.1016/j.ifacol.2019.01.047},\n\tabstract = {Most cyber-physical human systems (CPHS) rely on users learning how to interact with the system. Rather, a collaborative CPHS should learn from the user and adapt to them in a way that improves holistic system performance. Accomplishing this requires collaboration between the human-robot/human-computer interaction and the cyber-physical system communities in order to feed back knowledge about users into the design of the CPHS. The requisite user studies, however, are difficult, time consuming, and must be carefully designed. Furthermore, as humans are complex in their interactions with autonomy it is difficult to know, a priori, how many users must participate to attain conclusive results.\n\nIn this paper we elaborate on our work to infer intrinsic user qualities through human-robot interactions correlated with robot performance in order to adapt the autonomy and improve holistic CPHS performance. We first demonstrate through a study that this idea is feasible. Next, we demonstrate that significant differences between groups of users can impact conclusions particularly where different autonomies are involved. Finally, we also provide our rich, extensive corpus of user study data to the wider community to aid researchers in designing better CPHS.},\n\tbooktitle = {2018 {IFAC} {Cyber}-{Physical} {Human} {Systems}},\n\tpublisher = {2018 IFAC Cyber-Physical Human Systems},\n\tauthor = {Hall, Lucas and Acharya, Urja and Bradley, Justin and Duncan, Brittany},\n\tmonth = dec,\n\tyear = {2018},\n\tkeywords = {NSF 1638099, Shared control, autonomous mobile robots, human robot interaction, telerobotics},\n}\n\n
\n
\n\n\n
\n Most cyber-physical human systems (CPHS) rely on users learning how to interact with the system. Rather, a collaborative CPHS should learn from the user and adapt to them in a way that improves holistic system performance. Accomplishing this requires collaboration between the human-robot/human-computer interaction and the cyber-physical system communities in order to feed back knowledge about users into the design of the CPHS. The requisite user studies, however, are difficult, time consuming, and must be carefully designed. Furthermore, as humans are complex in their interactions with autonomy it is difficult to know, a priori, how many users must participate to attain conclusive results. In this paper we elaborate on our work to infer intrinsic user qualities through human-robot interactions correlated with robot performance in order to adapt the autonomy and improve holistic CPHS performance. We first demonstrate through a study that this idea is feasible. Next, we demonstrate that significant differences between groups of users can impact conclusions particularly where different autonomies are involved. Finally, we also provide our rich, extensive corpus of user study data to the wider community to aid researchers in designing better CPHS.\n
\n\n\n
\n\n\n
\n \n\n \n \n Plowcha, A.; Sun, Y.; Detweiler, C.; and Bradley, J.\n\n\n \n \n \n \n Predicting Digging Success for Unmanned Aircraft System Sensor Emplacement.\n \n \n \n\n\n \n\n\n\n In 2018 International Symposium on Experimental Robotics, Buenos Aires, Argentina, November 2018. 2018 International Symposium on Experimental Robotics\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{plowcha2018predicting,\n\taddress = {Buenos Aires, Argentina},\n\ttitle = {Predicting {Digging} {Success} for {Unmanned} {Aircraft} {System} {Sensor} {Emplacement}},\n\tbooktitle = {2018 {International} {Symposium} on {Experimental} {Robotics}},\n\tpublisher = {2018 International Symposium on Experimental Robotics},\n\tauthor = {Plowcha, Adam and Sun, Yue and Detweiler, Carrick and Bradley, Justin},\n\tmonth = nov,\n\tyear = {2018},\n\tkeywords = {NIFA 2017-67021-25924, NSF 1638099},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Kruse, L.; and Bradley, J.\n\n\n \n \n \n \n A Hybrid, Actively Compliant Manipulator/Gripper for Aerial Manipulation with a Multicopter.\n \n \n \n\n\n \n\n\n\n In 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages 1–8, Philadelphia, PA, August 2018. 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{kruse2018hybrid,\n\taddress = {Philadelphia, PA},\n\ttitle = {A {Hybrid}, {Actively} {Compliant} {Manipulator}/{Gripper} for {Aerial} {Manipulation} with a {Multicopter}},\n\tdoi = {10.1109/SSRR.2018.8468651},\n\tabstract = {Abstract- Unmanned Multicopters provide access to dangerous or difficult to reach areas and can accomplish tasks normally reserved for people. Aerial manipulation in this scenario could provide enhanced capabilities but is a challenging problem where design often comes down to choosing between arm-like manipulation or claw-like grippers - each of which has associated benefits and drawbacks. We present a novel, lightweight, low-cost hybrid manipulator/gripper combining characteristics of both arm-like manipulators and claw-like grippers to enable aerial manipulation during search and rescue missions using a multicopter unmanned air vehicle. The gripper is composed of tandem two degree-of-freedom fingers wherein each finger can be individually controlled, allowing for the manipulation of irregular geometric shapes and compensation for the drone's drift. We describe the design, manufacturing, and prototyping of our design and conduct a series of experiments grasping objects of different size, shape, and weight to demonstrate the versatility and benefits of our hybrid design.},\n\tbooktitle = {2018 {IEEE} {International} {Symposium} on {Safety}, {Security}, and {Rescue} {Robotics} ({SSRR})},\n\tpublisher = {2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)},\n\tauthor = {Kruse, Liam and Bradley, Justin},\n\tmonth = aug,\n\tyear = {2018},\n\tkeywords = {Drones, End effectors, Grasping, Grippers, NIFA 2017-67021-25924, Payloads, Shape, Unmanned Multicopters, aerial manipulation, arm-like manipulators, autonomous aerial vehicles, claw-like grippers, compliant manipulator/gripper, dexterous manipulators, grippers, low-cost hybrid manipulator/gripper, manipulators, mobile robots, multicopter unmanned air vehicle, service robots},\n\tpages = {1--8},\n}\n\n
\n
\n\n\n
\n Abstract- Unmanned Multicopters provide access to dangerous or difficult to reach areas and can accomplish tasks normally reserved for people. Aerial manipulation in this scenario could provide enhanced capabilities but is a challenging problem where design often comes down to choosing between arm-like manipulation or claw-like grippers - each of which has associated benefits and drawbacks. We present a novel, lightweight, low-cost hybrid manipulator/gripper combining characteristics of both arm-like manipulators and claw-like grippers to enable aerial manipulation during search and rescue missions using a multicopter unmanned air vehicle. The gripper is composed of tandem two degree-of-freedom fingers wherein each finger can be individually controlled, allowing for the manipulation of irregular geometric shapes and compensation for the drone's drift. We describe the design, manufacturing, and prototyping of our design and conduct a series of experiments grasping objects of different size, shape, and weight to demonstrate the versatility and benefits of our hybrid design.\n
\n\n\n
\n\n\n
\n \n\n \n \n Kruse, L.; Plowcha, A.; and Bradley, J.\n\n\n \n \n \n \n \n Experimental Testing and Validation of Cyber-Physical Coregulation of a CubeSat.\n \n \n \n \n\n\n \n\n\n\n In 2018 AIAA SPACE and Astronautics Forum and Exposition, of AIAA SPACE Forum, Orlando, FL, September 2018. 2018 AIAA SPACE and Astronautics Forum and Exposition\n \n\n\n\n
\n\n\n\n \n \n \"ExperimentalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{kruse2018experimental,\n\taddress = {Orlando, FL},\n\tseries = {{AIAA} {SPACE} {Forum}},\n\ttitle = {Experimental {Testing} and {Validation} of {Cyber}-{Physical} {Coregulation} of a {CubeSat}},\n\turl = {https://doi.org/10.2514/6.2018-5212},\n\tdoi = {10.2514/6.2018-5212},\n\tbooktitle = {2018 {AIAA} {SPACE} and {Astronautics} {Forum} and {Exposition}},\n\tpublisher = {2018 AIAA SPACE and Astronautics Forum and Exposition},\n\tauthor = {Kruse, Liam and Plowcha, Adam and Bradley, Justin},\n\tmonth = sep,\n\tyear = {2018},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Shen, T.; Nelson, C. A.; and Bradley, J.\n\n\n \n \n \n \n \n Design of a Model-Free Cross-Coupled Controller with Application to Robotic NOTES.\n \n \n \n \n\n\n \n\n\n\n Journal of Intelligent & Robotic Systems. June 2018.\n \n\n\n\n
\n\n\n\n \n \n \"DesignPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{shen2018design,\n\ttitle = {Design of a {Model}-{Free} {Cross}-{Coupled} {Controller} with {Application} to {Robotic} {NOTES}},\n\tissn = {1573-0409},\n\turl = {https://doi.org/10.1007/s10846-018-0836-2},\n\tdoi = {10.1007/s10846-018-0836-2},\n\tabstract = {Cross-coupled synchronization is an effective method of controlling an articulated robot especially in applications with restrictive requirements and low tolerance to error. Model-free methods of cross-coupled synchronization provide similar performance in cases where models are difficult or impossible to obtain. Here a novel model-free cross-coupled adaptive synchronization method is developed and applied to a Natural Orifice Transluminal Endoscopic Surgery (NOTES) robot - where reducing contour error has the important benefit of reducing the risk of surgical error and improving patient outcomes. To accomplish this, a baseline model-free cross coupled strategy is used, and an adaptive control gain and a balance scaling factor are used to improve the performance. Experiments are then performed validating the functionality and effectiveness of the controller using a NOTES robot. The results show significant improvement in decreasing contour error when compared with similar methods.},\n\tjournal = {Journal of Intelligent \\& Robotic Systems},\n\tauthor = {Shen, Tao and Nelson, Carl A. and Bradley, Justin},\n\tmonth = jun,\n\tyear = {2018},\n}\n\n
\n
\n\n\n
\n Cross-coupled synchronization is an effective method of controlling an articulated robot especially in applications with restrictive requirements and low tolerance to error. Model-free methods of cross-coupled synchronization provide similar performance in cases where models are difficult or impossible to obtain. Here a novel model-free cross-coupled adaptive synchronization method is developed and applied to a Natural Orifice Transluminal Endoscopic Surgery (NOTES) robot - where reducing contour error has the important benefit of reducing the risk of surgical error and improving patient outcomes. To accomplish this, a baseline model-free cross coupled strategy is used, and an adaptive control gain and a balance scaling factor are used to improve the performance. Experiments are then performed validating the functionality and effectiveness of the controller using a NOTES robot. The results show significant improvement in decreasing contour error when compared with similar methods.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2017\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n Shankar, A.; Doebbeling, S.; and Bradley, J.\n\n\n \n \n \n \n Toward a cyber-physical quadrotor: Characterizing trajectory following performance.\n \n \n \n\n\n \n\n\n\n In 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pages 133–142, Miami, FL, June 2017. 2017 International Conference on Unmanned Aircraft Systems (ICUAS)\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{shankar2017cyberphysical,\n\taddress = {Miami, FL},\n\ttitle = {Toward a cyber-physical quadrotor: {Characterizing} trajectory following performance},\n\tshorttitle = {Toward a cyber-physical quadrotor},\n\tdoi = {10.1109/ICUAS.2017.7991394},\n\tabstract = {An Unmanned Aircraft System (UAS) is a CyberPhysical System (CPS) in which a host of real-time computational tasks contending for shared resources must be cooperatively managed to provide actuation input for control of the locomotion necessary to obtain mission objectives. Traditionally, control of the UAS is designed assuming a fixed, high sampling rate in order to maintain reliable performance and margins of stability. But emerging methods challenge this design by dynamically allocating resources to computational tasks, thereby affecting control and mission performance. To apply these emerging strategies, a characterization and understanding of the effects of timing on control and trajectory following performance is required. Going beyond traditional control evaluation techniques, in this paper, we characterize the trajectory following performance, timing, and control of a quadrotor UAS under discrete linear quadratic regulator control designed at various sampling rates. We develop a direct relationship between trajectory following performance and the real-time task period (i.e. sampling rate) of the real-time control task allowing future designs to trade off UAS performance and cyber resources at the planning and/or guidance layer. We also introduce new metrics for characterizing cyber-physical quadrotor performance, and lay the groundwork for the application of CPS control methods to quadrotor UASs.},\n\tbooktitle = {2017 {International} {Conference} on {Unmanned} {Aircraft} {Systems} ({ICUAS})},\n\tpublisher = {2017 International Conference on Unmanned Aircraft Systems (ICUAS)},\n\tauthor = {Shankar, A. and Doebbeling, S. and Bradley, J.},\n\tmonth = jun,\n\tyear = {2017},\n\tkeywords = {Computer architecture, NSF 1638099, Planning, Real-time systems, Software, Timing, Trajectory, UAS control, autonomous aerial vehicles, control evaluation techniques, control performance, cyber-physical quadrotor, cyber-physical system, discrete linear quadratic regulator control, discrete systems, guidance layer, helicopters, linear quadratic control, mission performance, path planning, planning layer, sampling rates, stability, stability margin, trajectory control, trajectory following performance, unmanned aircraft system},\n\tpages = {133--142},\n}\n\n
\n
\n\n\n
\n An Unmanned Aircraft System (UAS) is a CyberPhysical System (CPS) in which a host of real-time computational tasks contending for shared resources must be cooperatively managed to provide actuation input for control of the locomotion necessary to obtain mission objectives. Traditionally, control of the UAS is designed assuming a fixed, high sampling rate in order to maintain reliable performance and margins of stability. But emerging methods challenge this design by dynamically allocating resources to computational tasks, thereby affecting control and mission performance. To apply these emerging strategies, a characterization and understanding of the effects of timing on control and trajectory following performance is required. Going beyond traditional control evaluation techniques, in this paper, we characterize the trajectory following performance, timing, and control of a quadrotor UAS under discrete linear quadratic regulator control designed at various sampling rates. We develop a direct relationship between trajectory following performance and the real-time task period (i.e. sampling rate) of the real-time control task allowing future designs to trade off UAS performance and cyber resources at the planning and/or guidance layer. We also introduce new metrics for characterizing cyber-physical quadrotor performance, and lay the groundwork for the application of CPS control methods to quadrotor UASs.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2016\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n Eubank, R. D.; Bradley, J.; and Atkins, E.\n\n\n \n \n \n \n \n Energy-Aware Multiflight Planning for an Unattended Seaplane: Flying Fish.\n \n \n \n \n\n\n \n\n\n\n Journal of Aerospace Information Systems,1–19. December 2016.\n \n\n\n\n
\n\n\n\n \n \n \"Energy-AwarePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{eubank2016energyaware,\n\ttitle = {Energy-{Aware} {Multiflight} {Planning} for an {Unattended} {Seaplane}: {Flying} {Fish}},\n\turl = {http://dx.doi.org/10.2514/1.I010484},\n\tdoi = {10.2514/1.I010484},\n\turldate = {2017-01-18},\n\tjournal = {Journal of Aerospace Information Systems},\n\tauthor = {Eubank, Ryan D. and Bradley, Justin and Atkins, Ella},\n\tmonth = dec,\n\tyear = {2016},\n\tpages = {1--19},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Foruzan, E.; Asgarpoor, S.; and Bradley, J.\n\n\n \n \n \n \n \n Hybrid system modeling and supervisory control of a microgrid.\n \n \n \n \n\n\n \n\n\n\n In IEEE 2016 North American Power Symposium (NAPS), pages 1–6, Denver, CO, September 2016. IEEE 2016 North American Power Symposium (NAPS)\n \n\n\n\n
\n\n\n\n \n \n \"HybridPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{foruzan2016hybrid,\n\taddress = {Denver, CO},\n\ttitle = {Hybrid system modeling and supervisory control of a microgrid},\n\tisbn = {978-1-5090-3270-9},\n\turl = {http://ieeexplore.ieee.org/document/7747840/},\n\tdoi = {10.1109/NAPS.2016.7747840},\n\turldate = {2016-11-27},\n\tbooktitle = {{IEEE} 2016 {North} {American} {Power} {Symposium} ({NAPS})},\n\tpublisher = {IEEE 2016 North American Power Symposium (NAPS)},\n\tauthor = {Foruzan, Elham and Asgarpoor, Sohrab and Bradley, Justin},\n\tmonth = sep,\n\tyear = {2016},\n\tpages = {1--6},\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2015\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n Bradley, J.; and Atkins, E.\n\n\n \n \n \n \n \n Optimization and Control of Cyber-Physical Vehicle Systems.\n \n \n \n \n\n\n \n\n\n\n Sensors, 15(9): 23020. 2015.\n \n\n\n\n
\n\n\n\n \n \n \"OptimizationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{bradley2015optimization,\n\ttitle = {Optimization and {Control} of {Cyber}-{Physical} {Vehicle} {Systems}},\n\tvolume = {15},\n\tissn = {1424-8220},\n\turl = {http://www.mdpi.com/1424-8220/15/9/23020},\n\tdoi = {10.3390/s150923020},\n\tnumber = {9},\n\tjournal = {Sensors},\n\tauthor = {Bradley, Justin and Atkins, Ella},\n\tyear = {2015},\n\tkeywords = {control},\n\tpages = {23020},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Bradley, J.; and Atkins, E.\n\n\n \n \n \n \n Coupled Cyber-Physical System Modeling and Coregulation of a CubeSat.\n \n \n \n\n\n \n\n\n\n IEEE Transactions on Robotics, 31(2): 443–456. April 2015.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{bradley2015coupled,\n\ttitle = {Coupled {Cyber}-{Physical} {System} {Modeling} and {Coregulation} of a {CubeSat}},\n\tvolume = {31},\n\tdoi = {10.1109/TRO.2015.2409431},\n\tnumber = {2},\n\tjournal = {IEEE Transactions on Robotics},\n\tauthor = {Bradley, Justin and Atkins, Ella},\n\tmonth = apr,\n\tyear = {2015},\n\tpages = {443--456},\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2014\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n Bradley, J.; and Atkins, E.\n\n\n \n \n \n \n \n A Cyber-Physical Optimization Approach to Mission Success for Unmanned Aircraft Systems.\n \n \n \n \n\n\n \n\n\n\n Journal of Aerospace Information Systems, 11(1): 48–60. January 2014.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{bradley2014cyberphysical,\n\ttitle = {A {Cyber}-{Physical} {Optimization} {Approach} to {Mission} {Success} for {Unmanned} {Aircraft} {Systems}},\n\tvolume = {11},\n\turl = {http://dx.doi.org/10.2514/1.I010105},\n\tdoi = {10.2514/1.I010105},\n\tnumber = {1},\n\tjournal = {Journal of Aerospace Information Systems},\n\tauthor = {Bradley, Justin and Atkins, Ella},\n\tmonth = jan,\n\tyear = {2014},\n\tpages = {48--60},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Bradley, J.\n\n\n \n \n \n \n Toward Co-Design of Autonomous Aerospace Cyber-Physical Systems.\n \n \n \n\n\n \n\n\n\n Ph.D. Thesis, University of Michigan, 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{bradley2014codesign,\n\ttitle = {Toward {Co}-{Design} of {Autonomous} {Aerospace} {Cyber}-{Physical} {Systems}},\n\tschool = {University of Michigan},\n\tauthor = {Bradley, Justin},\n\tyear = {2014},\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2013\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n Atkins, E.; and Bradley, J.\n\n\n \n \n \n \n \n Aerospace Cyber-Physical Systems Education.\n \n \n \n \n\n\n \n\n\n\n In AIAA Infotech@Aerospace, Boston, MA, August 2013. AIAA Infotech@Aerospace\n \n\n\n\n
\n\n\n\n \n \n \"AerospacePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{atkins2013aerospace,\n\taddress = {Boston, MA},\n\ttitle = {Aerospace {Cyber}-{Physical} {Systems} {Education}},\n\turl = {https://doi.org/10.2514/6.2013-4809},\n\tdoi = {10.2514/6.2013-4809},\n\tbooktitle = {{AIAA} {Infotech}@{Aerospace}},\n\tpublisher = {AIAA Infotech@Aerospace},\n\tauthor = {Atkins, Ella and Bradley, Justin},\n\tmonth = aug,\n\tyear = {2013},\n\tkeywords = {education},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Bradley, J.; Clark, M.; Atkins, E.; and Shin, K.\n\n\n \n \n \n \n \n Mission-Aware Cyber-Physical Optimization on a Tabletop Satellite.\n \n \n \n \n\n\n \n\n\n\n In AIAA Infotech@Aerospace, Boston, MA, August 2013. AIAA Infotech@Aerospace\n \n\n\n\n
\n\n\n\n \n \n \"Mission-AwarePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{bradley2013missionaware,\n\taddress = {Boston, MA},\n\ttitle = {Mission-{Aware} {Cyber}-{Physical} {Optimization} on a {Tabletop} {Satellite}},\n\turl = {https://doi.org/10.2514/6.2013-4807},\n\tdoi = {10.2514/6.2013-4807},\n\tbooktitle = {{AIAA} {Infotech}@{Aerospace}},\n\tpublisher = {AIAA Infotech@Aerospace},\n\tauthor = {Bradley, Justin and Clark, Meghan and Atkins, Ella and Shin, Kang},\n\tmonth = aug,\n\tyear = {2013},\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2012\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n Bradley, J.; and Atkins, E.\n\n\n \n \n \n \n \n Multi-Disciplinary Cyber-Physical Optimization for Unmanned Aircraft Systems.\n \n \n \n \n\n\n \n\n\n\n In AIAA Infotech@Aerospace, Garden Grove, CA, June 2012. AIAA Infotech@Aerospace\n \n\n\n\n
\n\n\n\n \n \n \"Multi-DisciplinaryPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{bradley2012multidisciplinary,\n\taddress = {Garden Grove, CA},\n\ttitle = {Multi-{Disciplinary} {Cyber}-{Physical} {Optimization} for {Unmanned} {Aircraft} {Systems}},\n\turl = {https://doi.org/10.2514/6.2012-2447},\n\tdoi = {10.2514/6.2012-2447},\n\tbooktitle = {{AIAA} {Infotech}@{Aerospace}},\n\tpublisher = {AIAA Infotech@Aerospace},\n\tauthor = {Bradley, Justin and Atkins, Ella},\n\tmonth = jun,\n\tyear = {2012},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Bradley, J.; and Atkins, E.\n\n\n \n \n \n \n Toward Continuous State-Space Regulation of Coupled Cyber-Physical Systems.\n \n \n \n\n\n \n\n\n\n Proceedings of the IEEE, 100(1): 60–74. January 2012.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{bradley2012toward,\n\ttitle = {Toward {Continuous} {State}-{Space} {Regulation} of {Coupled} {Cyber}-{Physical} {Systems}},\n\tvolume = {100},\n\tissn = {0018-9219},\n\tdoi = {10.1109/JPROC.2011.2161239},\n\tnumber = {1},\n\tjournal = {Proceedings of the IEEE},\n\tauthor = {Bradley, Justin and Atkins, Ella},\n\tmonth = jan,\n\tyear = {2012},\n\tpages = {60--74},\n}\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2011\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n Bradley, J.; and Atkins, E.\n\n\n \n \n \n \n \n Computational-Physical State Co-Regulation in Cyber-Physical Systems.\n \n \n \n \n\n\n \n\n\n\n In Proceedings of the 2011 IEEE/ACM Second International Conference on Cyber-Physical Systems, Chicago, IL, April 2011. Proceedings of the 2011 IEEE/ACM Second International Conference on Cyber-Physical Systems\n \n\n\n\n
\n\n\n\n \n \n \"Computational-PhysicalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{bradley2011computationalphysical,\n\taddress = {Chicago, IL},\n\ttitle = {Computational-{Physical} {State} {Co}-{Regulation} in {Cyber}-{Physical} {Systems}},\n\turl = {https://doi.org/10.1109/ICCPS.2011.27},\n\tdoi = {10.1109/ICCPS.2011.27},\n\tbooktitle = {Proceedings of the 2011 {IEEE}/{ACM} {Second} {International} {Conference} on {Cyber}-{Physical} {Systems}},\n\tpublisher = {Proceedings of the 2011 IEEE/ACM Second International Conference on Cyber-Physical Systems},\n\tauthor = {Bradley, Justin and Atkins, Ella},\n\tmonth = apr,\n\tyear = {2011},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Bradley, J.; and Taylor, C.\n\n\n \n \n \n \n \n Georeferenced Mosaics for Tracking Fires Using Unmanned Miniature Air Vehicles.\n \n \n \n \n\n\n \n\n\n\n Journal of Aerospace Computing, Information, and Communication, 8(10): 295–309. October 2011.\n \n\n\n\n
\n\n\n\n \n \n \"GeoreferencedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{bradley2011georeferenced,\n\ttitle = {Georeferenced {Mosaics} for {Tracking} {Fires} {Using} {Unmanned} {Miniature} {Air} {Vehicles}},\n\tvolume = {8},\n\turl = {https://doi.org/10.2514/1.45342},\n\tdoi = {10.2514/1.45342},\n\tnumber = {10},\n\tjournal = {Journal of Aerospace Computing, Information, and Communication},\n\tauthor = {Bradley, Justin and Taylor, Clark},\n\tmonth = oct,\n\tyear = {2011},\n\tpages = {295--309},\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2007\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n Rodriguez, A.; Andersen, E.; Bradley, J.; and Taylor, C.\n\n\n \n \n \n \n \n Wind Estimation Using an Optical Flow Sensor on a Miniature Air Vehicle.\n \n \n \n \n\n\n \n\n\n\n In AIAA Conference on Guidance, Navigation, and Control, volume 6614, Hilton Head, SC, August 2007. AIAA Conference on Guidance, Navigation, and Control\n \n\n\n\n
\n\n\n\n \n \n \"WindPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{rodriguez2007wind,\n\taddress = {Hilton Head, SC},\n\ttitle = {Wind {Estimation} {Using} an {Optical} {Flow} {Sensor} on a {Miniature} {Air} {Vehicle}},\n\tvolume = {6614},\n\turl = {http://dx.doi.org/10.2514/6.2007-6614},\n\tdoi = {10.2514/6.2007-6614},\n\tbooktitle = {{AIAA} {Conference} on {Guidance}, {Navigation}, and {Control}},\n\tpublisher = {AIAA Conference on Guidance, Navigation, and Control},\n\tauthor = {Rodriguez, Andres and Andersen, Evan and Bradley, Justin and Taylor, Clark},\n\tmonth = aug,\n\tyear = {2007},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Bradley, J.; and Taylor, C.\n\n\n \n \n \n \n \n Particle Filter Based Mosaicking for Tracking Forest Fires.\n \n \n \n \n\n\n \n\n\n\n In AIAA Conference on Guidance, Navigation, and Control, Hilton Head, SC, August 2007. AIAA Conference on Guidance, Navigation, and Control\n \n\n\n\n
\n\n\n\n \n \n \"ParticlePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{bradley2007particle,\n\taddress = {Hilton Head, SC},\n\ttitle = {Particle {Filter} {Based} {Mosaicking} for {Tracking} {Forest} {Fires}},\n\turl = {https://doi.org/10.2514/6.2007-6757},\n\tdoi = {10.2514/6.2007-6757},\n\tbooktitle = {{AIAA} {Conference} on {Guidance}, {Navigation}, and {Control}},\n\tpublisher = {AIAA Conference on Guidance, Navigation, and Control},\n\tauthor = {Bradley, Justin and Taylor, Clark},\n\tmonth = aug,\n\tyear = {2007},\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2006\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n Bradley, J.; Prall, B.; Beard, R.; and Taylor, C.\n\n\n \n \n \n \n An Unmanned Aerial Vehicle Project For Undergraduates.\n \n \n \n\n\n \n\n\n\n In The Forty-Second Annual International Telemetering Conference and Technical Exhibition, volume 42, pages 563–572, San Diego, CA, October 2006. The Forty-Second Annual International Telemetering Conference and Technical Exhibition\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{bradley2006unmanned,\n\taddress = {San Diego, CA},\n\ttitle = {An {Unmanned} {Aerial} {Vehicle} {Project} {For} {Undergraduates}},\n\tvolume = {42},\n\tbooktitle = {The {Forty}-{Second} {Annual} {International} {Telemetering} {Conference} and {Technical} {Exhibition}},\n\tpublisher = {The Forty-Second Annual International Telemetering Conference and Technical Exhibition},\n\tauthor = {Bradley, Justin and Prall, Breton and Beard, Randal and Taylor, Clark},\n\tmonth = oct,\n\tyear = {2006},\n\tpages = {563--572},\n}\n\n
\n
\n\n\n\n
\n\n\n
\n \n\n \n \n Holt, R.; Egbert, J.; Bradley, J.; Beard, R.; Taylor, C.; and McLain, T.\n\n\n \n \n \n \n Forest Fire Monitoring Using Multiple Unmanned Air Vehicles.\n \n \n \n\n\n \n\n\n\n In The Eleventh Forest Service Remote Sensing Applications Conference, volume 1, pages 54–66, Salt Lake City, UT, April 2006. The Eleventh Forest Service Remote Sensing Applications Conference\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{holt2006forest,\n\taddress = {Salt Lake City, UT},\n\ttitle = {Forest {Fire} {Monitoring} {Using} {Multiple} {Unmanned} {Air} {Vehicles}},\n\tvolume = {1},\n\tbooktitle = {The {Eleventh} {Forest} {Service} {Remote} {Sensing} {Applications} {Conference}},\n\tpublisher = {The Eleventh Forest Service Remote Sensing Applications Conference},\n\tauthor = {Holt, Ryan and Egbert, Joseph and Bradley, Justin and Beard, Randal and Taylor, Clark and McLain, Timothy},\n\tmonth = apr,\n\tyear = {2006},\n\tpages = {54--66},\n}\n\n
\n
\n\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n\n\n \n\n \n \n \n \n\n
\n"}; document.write(bibbase_data.data);