Robust Real-Time Model Predictive Control for High Degree of Freedom Soft Robots Freedom Soft Robots. Phillip Edmond Hyatt Ph.D. Thesis, Brigham Young University. ![Robust Real-Time Model Predictive Control for High Degree of Freedom Soft Robots Freedom Soft Robots [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex This dissertation is focused on the modeling and robust model-based control of high degree-of-freedom (DoF) systems. While most of the contributions are applicable to any difficult-to-model system, this dissertation focuses specifically on applications to large-scale soft robots because their many joints and pressures constitute a high-DoF system and their inherit softness makes them difficult to model accurately. First a joint-angle estimation and kinematic calibration method for soft robots is developed which is shown to decrease the pose prediction error at the end of a 1.5 m robot arm by about 85%. A novel dynamic modelling approach which can be evaluated within microseconds is then formulated for continuum type soft robots. We show that deep neural networks(DNNs) can be used to approximate soft robot dynamics given training examples from physics-based models like the ones described above. We demonstrate how these machine-learning-based models can be evaluated quickly to perform a form of optimal control called model predictive control (MPC). We describe a method of control trajectory parameterization that enables MPC to be applied to systems with more DoF and with longer prediction horizons than previously possible.We show that this parameterization decreases MPC’s sensitivity to model error and drastically reduces MPC solve times. A novel form of MPC is developed based on an evolutionary optimization algorithm that allows the optimization to be parallelized on a computer’s graphics processing unit(GPU). We show that this evolutionary MPC (EMPC) can greatly decrease MPC solve times for high DoF systems without large performance losses, especially given a large GPU. We combine the ideas of machine learned DNN models of robot dynamics, with parameterized and parallelizedMPC to obtain a nonlinear version of EMPC which can be run at higher rates and find better solutions than many state-of-the-art optimal control methods. Finally we demonstrate an adaptive form of MPC that can compensate for model error or changes in the system to be controlled. This adaptive form of MPC is shown to inherit MPC’s robustness to completely unmodeled disturbances and adaptive control’s ability to decrease trajectory tracking errors over time.
@phdthesis{phillip_edmond_hyatt_robust_nodate,
type = {{PhD}},
title = {Robust {Real}-{Time} {Model} {Predictive} {Control} for {High} {Degree} of {Freedom} {Soft} {Robots} {Freedom} {Soft} {Robots}},
url = {https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=9453&context=etd},
abstract = {This dissertation is focused on the modeling and robust model-based control of high degree-of-freedom (DoF) systems. While most of the contributions are applicable to any difficult-to-model system, this dissertation focuses specifically on applications to large-scale soft robots because their many joints and pressures constitute a high-DoF system and their inherit softness makes them difficult to model accurately. First a joint-angle estimation and kinematic calibration method for soft robots is developed which is shown to decrease the pose prediction error at the end of a 1.5 m robot arm by about 85\%. A novel dynamic modelling approach which can be evaluated within microseconds is then formulated for continuum type soft robots. We show that deep neural networks(DNNs) can be used to approximate soft robot dynamics given training examples from physics-based models like the ones described above. We demonstrate how these machine-learning-based models can be evaluated quickly to perform a form of optimal control called model predictive control (MPC). We describe a method of control trajectory parameterization that enables MPC to be applied to systems with more DoF and with longer prediction horizons than previously possible.We show that this parameterization decreases MPC’s sensitivity to model error and drastically reduces MPC solve times. A novel form of MPC is developed based on an evolutionary optimization algorithm that allows the optimization to be parallelized on a computer’s graphics processing unit(GPU). We show that this evolutionary MPC (EMPC) can greatly decrease MPC solve times for high DoF systems without large performance losses, especially given a large GPU. We combine the ideas of machine learned DNN models of robot dynamics, with parameterized and parallelizedMPC to obtain a nonlinear version of EMPC which can be run at higher rates and find better solutions than many state-of-the-art optimal control methods. Finally we demonstrate an adaptive form of MPC that can compensate for model error or changes in the system to be controlled. This adaptive form of MPC is shown to inherit MPC’s robustness to completely unmodeled disturbances and adaptive control’s ability to decrease trajectory tracking errors over time.},
school = {Brigham Young University},
author = {{Phillip Edmond Hyatt}},
keywords = {GPU, high degrees of freedom, soft robotics, uses sympy}
}
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First a joint-angle estimation and kinematic calibration method for soft robots is developed which is shown to decrease the pose prediction error at the end of a 1.5 m robot arm by about 85%. A novel dynamic modelling approach which can be evaluated within microseconds is then formulated for continuum type soft robots. We show that deep neural networks(DNNs) can be used to approximate soft robot dynamics given training examples from physics-based models like the ones described above. We demonstrate how these machine-learning-based models can be evaluated quickly to perform a form of optimal control called model predictive control (MPC). We describe a method of control trajectory parameterization that enables MPC to be applied to systems with more DoF and with longer prediction horizons than previously possible.We show that this parameterization decreases MPC’s sensitivity to model error and drastically reduces MPC solve times. A novel form of MPC is developed based on an evolutionary optimization algorithm that allows the optimization to be parallelized on a computer’s graphics processing unit(GPU). We show that this evolutionary MPC (EMPC) can greatly decrease MPC solve times for high DoF systems without large performance losses, especially given a large GPU. We combine the ideas of machine learned DNN models of robot dynamics, with parameterized and parallelizedMPC to obtain a nonlinear version of EMPC which can be run at higher rates and find better solutions than many state-of-the-art optimal control methods. Finally we demonstrate an adaptive form of MPC that can compensate for model error or changes in the system to be controlled. 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