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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2023.3299527