Robust Vehicle Stability Based on Non-Linear Model Predictive Control and Environmental Characterization. Stamenov, V., Geiger, S., Bevly, D., & Balas, C. In 2017. abstract bibtex A Non-linear Model Predictive Controller (NMPC) was developed for an unmanned ground vehicle (UGV). The NMPC uses a particle swarm pattern search algorithm to optimize the control input, which contains a desired steer angle and a desired longitudinal velocity. The NMPC is designed to approach a target whilst avoiding obstacles that are detected using a light detection and ranging sensor (lidar). Since not all obstacles are stationary, an obstacle tracking algorithm is employed to track obstacles. Two point cluster detection algorithms were reviewed, and a constant velocity Kalman filter-based tracking loop was developed. The tracked obstacles’ positions are predicted using a constant velocity model in the NMPC; this allows for avoidance of both stationary and dynamic obstacles.
@inproceedings{stamenov_robust_2017,
title = {Robust {Vehicle} {Stability} {Based} on {Non}-{Linear} {Model} {Predictive} {Control} and {Environmental} {Characterization}},
abstract = {A Non-linear Model Predictive Controller (NMPC) was developed for an unmanned ground vehicle (UGV). The NMPC uses a particle swarm pattern search algorithm to optimize the control input, which contains a desired steer angle and a desired longitudinal velocity. The NMPC is designed to approach a target whilst avoiding obstacles that are detected using a light detection and ranging sensor (lidar). Since not all obstacles are stationary, an obstacle tracking algorithm is employed to track obstacles. Two point cluster detection algorithms were reviewed, and a constant velocity Kalman filter-based tracking loop was developed. The tracked obstacles’ positions are predicted using a constant velocity model in the NMPC; this allows for avoidance of both stationary and dynamic obstacles.},
author = {Stamenov, Velislav and Geiger, Stephen and Bevly, David and Balas, Christian},
year = {2017},
}
Downloads: 0
{"_id":"MXKMxRiyWchGd2dsE","bibbaseid":"stamenov-geiger-bevly-balas-robustvehiclestabilitybasedonnonlinearmodelpredictivecontrolandenvironmentalcharacterization-2017","author_short":["Stamenov, V.","Geiger, S.","Bevly, D.","Balas, C."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","title":"Robust Vehicle Stability Based on Non-Linear Model Predictive Control and Environmental Characterization","abstract":"A Non-linear Model Predictive Controller (NMPC) was developed for an unmanned ground vehicle (UGV). The NMPC uses a particle swarm pattern search algorithm to optimize the control input, which contains a desired steer angle and a desired longitudinal velocity. The NMPC is designed to approach a target whilst avoiding obstacles that are detected using a light detection and ranging sensor (lidar). Since not all obstacles are stationary, an obstacle tracking algorithm is employed to track obstacles. Two point cluster detection algorithms were reviewed, and a constant velocity Kalman filter-based tracking loop was developed. The tracked obstacles’ positions are predicted using a constant velocity model in the NMPC; this allows for avoidance of both stationary and dynamic obstacles.","author":[{"propositions":[],"lastnames":["Stamenov"],"firstnames":["Velislav"],"suffixes":[]},{"propositions":[],"lastnames":["Geiger"],"firstnames":["Stephen"],"suffixes":[]},{"propositions":[],"lastnames":["Bevly"],"firstnames":["David"],"suffixes":[]},{"propositions":[],"lastnames":["Balas"],"firstnames":["Christian"],"suffixes":[]}],"year":"2017","bibtex":"@inproceedings{stamenov_robust_2017,\n\ttitle = {Robust {Vehicle} {Stability} {Based} on {Non}-{Linear} {Model} {Predictive} {Control} and {Environmental} {Characterization}},\n\tabstract = {A Non-linear Model Predictive Controller (NMPC) was developed for an unmanned ground vehicle (UGV). The NMPC uses a particle swarm pattern search algorithm to optimize the control input, which contains a desired steer angle and a desired longitudinal velocity. The NMPC is designed to approach a target whilst avoiding obstacles that are detected using a light detection and ranging sensor (lidar). Since not all obstacles are stationary, an obstacle tracking algorithm is employed to track obstacles. Two point cluster detection algorithms were reviewed, and a constant velocity Kalman filter-based tracking loop was developed. The tracked obstacles’ positions are predicted using a constant velocity model in the NMPC; this allows for avoidance of both stationary and dynamic obstacles.},\n\tauthor = {Stamenov, Velislav and Geiger, Stephen and Bevly, David and Balas, Christian},\n\tyear = {2017},\n}\n\n\n\n\n\n\n\n","author_short":["Stamenov, V.","Geiger, S.","Bevly, D.","Balas, C."],"key":"stamenov_robust_2017","id":"stamenov_robust_2017","bibbaseid":"stamenov-geiger-bevly-balas-robustvehiclestabilitybasedonnonlinearmodelpredictivecontrolandenvironmentalcharacterization-2017","role":"author","urls":{},"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"inproceedings","biburl":"https://bibbase.org/zotero/keb0115","dataSources":["oHndJaZA7ydhNbpvR"],"keywords":[],"search_terms":["robust","vehicle","stability","based","non","linear","model","predictive","control","environmental","characterization","stamenov","geiger","bevly","balas"],"title":"Robust Vehicle Stability Based on Non-Linear Model Predictive Control and Environmental Characterization","year":2017}