Robust Convex Model Predictive Control for Quadruped Locomotion Under Uncertainties

This article considers quadruped locomotion control in the presence of uncertainties. Two types of structured uncertainties are considered, namely, uncertain friction constraints and uncertain model dynamics. Then, a min-max optimization model is formulated based on robust optimization, and a robust...

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Vydané v:	IEEE transactions on robotics Ročník 39; číslo 6; s. 1 - 18
Hlavní autori:	Xu, Shaohang, Zhu, Lijun, Zhang, Hai-Tao, Ho, Chin Pang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Algorithms Constraint modelling Controllers Heuristic algorithms Legged robots Locomotion model predictive control (MPC) Optimization optimization and optimal control Optimization models Predictive control Predictive models Quadratic programming Quadrupedal robots Robots Robust control robust/adaptive control of robotic systems Uncertainty Unknown environments
ISSN:	1552-3098, 1941-0468
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Abstract	This article considers quadruped locomotion control in the presence of uncertainties. Two types of structured uncertainties are considered, namely, uncertain friction constraints and uncertain model dynamics. Then, a min-max optimization model is formulated based on robust optimization, and a robust min-max model predictive controller is proposed by recurrently solving the optimization model. We prove that the min-max optimization model is equivalent to a convex quadratic constrained quadratic program by exploiting the structure of uncertainties. Moreover, a two-stage optimization algorithm is proposed to solve the optimization problem efficiently, allowing for the deployment of the controller onto the real robot. The results show that the proposed optimization algorithm can improve solving frequency by <inline-formula><tex-math notation="LaTeX">\sim</tex-math></inline-formula>11× compared with Gurobi. The proposed controller is able to stabilize quadruped locomotion in challenging scenarios where the uncertainties are caused by significant disturbances and unknown environments.
AbstractList	This article considers quadruped locomotion control in the presence of uncertainties. Two types of structured uncertainties are considered, namely, uncertain friction constraints and uncertain model dynamics. Then, a min-max optimization model is formulated based on robust optimization, and a robust min-max model predictive controller is proposed by recurrently solving the optimization model. We prove that the min-max optimization model is equivalent to a convex quadratic constrained quadratic program by exploiting the structure of uncertainties. Moreover, a two-stage optimization algorithm is proposed to solve the optimization problem efficiently, allowing for the deployment of the controller onto the real robot. The results show that the proposed optimization algorithm can improve solving frequency by <inline-formula><tex-math notation="LaTeX">\sim</tex-math></inline-formula>11× compared with Gurobi. The proposed controller is able to stabilize quadruped locomotion in challenging scenarios where the uncertainties are caused by significant disturbances and unknown environments. This article considers quadruped locomotion control in the presence of uncertainties. Two types of structured uncertainties are considered, namely, uncertain friction constraints and uncertain model dynamics. Then, a min-max optimization model is formulated based on robust optimization, and a robust min-max model predictive controller is proposed by recurrently solving the optimization model. We prove that the min-max optimization model is equivalent to a convex quadratic constrained quadratic program by exploiting the structure of uncertainties. Moreover, a two-stage optimization algorithm is proposed to solve the optimization problem efficiently, allowing for the deployment of the controller onto the real robot. The results show that the proposed optimization algorithm can improve solving frequency by [Formula Omitted]11× compared with Gurobi. The proposed controller is able to stabilize quadruped locomotion in challenging scenarios where the uncertainties are caused by significant disturbances and unknown environments.
Author	Ho, Chin Pang Zhang, Hai-Tao Xu, Shaohang Zhu, Lijun
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SubjectTerms	Adaptation models Algorithms Constraint modelling Controllers Heuristic algorithms Legged robots Locomotion model predictive control (MPC) Optimization optimization and optimal control Optimization models Predictive control Predictive models Quadratic programming Quadrupedal robots Robots Robust control robust/adaptive control of robotic systems Uncertainty Unknown environments
Title	Robust Convex Model Predictive Control for Quadruped Locomotion Under Uncertainties
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