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Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

Defence Technology（防务技术） 2024年第4期34卷 19-28页

作者： Xiang-long Liang Zhi-kai Yao Yao-wen Ge Jian-yong Yao School of mechanical Engineering Nanjing University of Science and TechnologyNanjing210094China College of Automation&College of Artificial Intelligence Nanjing University of Post and TelecommunicationNanjing210023China

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange *** considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and *** these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become *** by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical *** approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control *** from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control *** developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output ***,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

关键词： Adaptive control Reinforcement learning uncertain mechanical systems Asymptotic tracking

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