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https://dspace.ctu.edu.vn/jspui/handle/123456789/97549
Nhan đề: | Optimal tracking control for robot manipulators with asymmetric saturation torques based on reinforcement learning |
Tác giả: | Nguyen, Duc Dien Nguyen, Tan Luy Lai, Khac Lai |
Từ khoá: | Robot manipulators Reinforcement learning Optimal control Competitive learning Asymmetry saturation inputs |
Năm xuất bản: | 2023 |
Tùng thư/Số báo cáo: | Journal of Computer Science and Cybernetics;Vol.39, No.01 .- P.61-77 |
Tóm tắt: | 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. |
Định danh: | https://dspace.ctu.edu.vn/jspui/handle/123456789/97549 |
ISSN: | 1813-9663 |
Bộ sưu tập: | Tin học và Điều khiển học (Journal of Computer Science and Cybernetics) |
Các tập tin trong tài liệu này:
Tập tin | Mô tả | Kích thước | Định dạng | |
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_file_ Giới hạn truy cập | 5.86 MB | Adobe PDF | ||
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