Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/97549
Title: Optimal tracking control for robot manipulators with asymmetric saturation torques based on reinforcement learning
Authors: Nguyen, Duc Dien
Nguyen, Tan Luy
Lai, Khac Lai
Keywords: Robot manipulators
Reinforcement learning
Optimal control
Competitive learning
Asymmetry saturation inputs
Issue Date: 2023
Series/Report no.: Journal of Computer Science and Cybernetics;Vol.39, No.01 .- P.61-77
Abstract: This paper introduces an optimal tracking controller for robot manipulators with asymmetrically saturated torques and partially - unknown dynamics based on a reinforcement learning method using a neural network. Firstly, the feedforward control inputs are designed based on the backstepping technique to convert the tracking control problem into the optimal tracking control problem. Secondly, a cost function of the system with asymmetrically saturated input is defined, and the constrained Hamilton-Jacobi-Bellman equation is built, which is solved by the online reinforcement learning algorithm using only a single neural network. Then, the asymmetric saturation optimal control rule is determined. Additionally, the concurrent learning technique is used to relax the demand for the persistence of excitation conditions. The built algorithm ensures that the closed-loop system is asymptotically stable, the approximation error is uniformly ultimately bounded (UUB), and the cost function converges to the near-optimal value. Finally, the effectiveness of the proposed algorithm is shown through comparative simulations.
URI: https://dspace.ctu.edu.vn/jspui/handle/123456789/97549
ISSN: 1813-9663
Appears in Collections:Tin học và Điều khiển học (Journal of Computer Science and Cybernetics)

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