https://orcid.org/0000-0001-8491-9943
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Abstract
In this paper, an adaptive finite-time impedance control strategy based on optimised backstepping (OB) technique is proposed for robotic manipulators subject to state constraints. The existing OB methods approximately solve the intractable Hamilton-Jacobi-Bellman equation by reinforcement learning (RL) with Bellman residual error, which has intricate actor-critic updating laws and persistence excitation requirement. To overcome this drawback, we construct the simplified RL updating laws by converting the problem to the solution of a positive-definite function, which is composed of actor-critic network weights. Then, the simplified RL updating laws can significantly reduce the controller complexity and relax the persistence excitation. Based on the barrier Lyapunov function, a barrier-type performance index function is constructed for the optimised controller under state constraints. The finite-time stability theory can guarantee the finite-time convergence property of the closed loop system without violating the prescribed constraints. Finally, we demonstrate the effectiveness of the proposed method in the simulation example with environment-robot interaction.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Data available on request due to privacy/ethical restrictions.
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Notes on contributors
Chengpeng Li
Chengpeng Li received the B.S. degree in Harbin Engineering University in 2018 and a Master's degree in Harbin Engineering University in 2021. He is currently pursuing the Ph.D. degree with the College of Control Science and Engineering, Zhejiang University, Hangzhou, China. His research interests include backstepping control and adaptive neural control.
Qinyuan Ren
Qinyuan Ren received the Ph.D. degree in control science and engineering from Zhejiang University (ZJU), Hangzhou, China, in 2008. He then joined the Department of Electrical and Computer Engineering, National University of Singapore, Singapore. In July 2015, he joined the Institute for Infocomm Research, Agency for Science, Technology and Research, as a Scientist. He is currently a Professor with the College of Control Science and Engineering, ZJU. His research interests include nonlinear systems, motion control and bionic engineering.
Zuhua Xu
Zuhua Xu received his B.S. degree from Zhejiang University of Technology in 1999 and his Ph.D. degree from Zhejiang University in 2004. He is a Professor in the College of Control Science and Engineering, Zhejiang University. His research interests include model predictive control, optimized backstepping, system identification and long-term prediction.
Jun Zhao
Jun Zhao received the Ph.D. degree in control science and engineering from the Zhejiang University, Hangzhou, China, in 2000. His current research interests include process modelingand control, and model predictive control.
Chunyue Song
Chunyue Song received the B.S.degree in automation of chemical engineering and Ph.D. degree in control science and engineering from Zhejiang University, Hangzhou,China, in 1994 and 2003, respectively. From 2003 to 2005, he was a postdoc-toral fellow in College of Computer Science at Zhejiang University. From 2006 to 2008, he was a visiting scholar with the Institute for Systems Research, University of Maryland (College Park), USA. Since 2005, he has been with the Zhejiang University, Hangzhou, China, currently as professor of control science and engineering in the College of Control Science and Engineering. His research interests include optimal control of nonlinear systems, stochastic control, and intelligent decision making.