@article{Zhong2024, 
author = {Huihui Zhong and Weijian Wen and Jianjun Fan and Weijun Yang},
title = {Reinforcement learning-based adaptive tracking control for flexible-joint robotic manipulators},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {10},
pages = {27330-27360},
keywords = {optimal control, reinforcement learning, neural networks, flexible-joint robotic manipulator, Lyapunov function},
url = {https://www.sciopen.com/article/10.3934/math.20241328},
doi = {10.3934/math.20241328},
abstract = {In this paper, we investigated the optimal tracking control problem of flexible-joint robotic manipulators in order to achieve trajectory tracking, and at the same time reduced the energy consumption of the feedback controller. Technically, optimization strategies were well-integrated into backstepping recursive design so that a series of optimized controllers for each subsystem could be constructed to improve the closed-loop system performance, and, additionally, a reinforcement learning method strategy based on neural network actor-critic architecture was adopted to approximate unknown terms in control design, making that the Hamilton-Jacobi-Bellman equation solvable in the sense of optimal control. With our scheme, the closed-loop stability, the convergence of output tracking error can be proved rigorously. Besides theoretical analysis, the effectiveness of our scheme was also illustrated by simulation results.}
}