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Reproduction and generalization of robot trajectory tracking control method using reinforcement learning and neural network techniques
- 1 Guangdong University of Technology
* Author to whom correspondence should be addressed.
Abstract
This paper introduces a method for controlling the trajectory of a manipulator robot with uncertainty, using reinforcement learning. The control is designed to work even when there are limitations on the inputs to the system. Reinforcement learning and neural networks are employed alongside standard robust control techniques to enhance the fixed-time convergence of the system state. A novel algorithm is proposed to develop a reinforcement learning-based approach. This approach utilizes radial basis function neural networks and nonsingular fast terminal sliding mode control to ensure error convergence within a predetermined time. This paper presents the task of monitoring the intended path followed by robotic arms in the presence of uncertain and unfamiliar disruptions. The experimental results validate that the suggested approach substantially improves both the stability and accuracy of trajectory tracking, making it more feasible for real-world applications in robotic systems.
Keywords
Fixed-time control, reinforcement learning, neural networks, nonsingular fast terminal sliding mode control, extended applications.
[1]. Zuo, Z., Defoort, M., Tian, B., & Ding, Z. (2020). Distributed consensus observer for multiagent systems with high-order integrator dynamics.IEEE Transactions on Automatic Control, 65(4), 1771–1778. https://doi.org/10.1109/TAC.2019.2936555
[2]. Hirai, K., Hirose, M., Haikawa, Y., & Takenaka, T. (1998). The development of Honda humanoid robot.In Proceedings of the 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146), 1321–1326, Leuven, Belgium: IEEE. https://doi.org/10.1109/ROBOT.1998.677288
[3]. Yang, C., Li, Z., Cui, R., & Xu, B. (2014). Neural network-based motion control of an underactuated wheeled inverted pendulum model.IEEE Transactions on Neural Networks and Learning Systems, 25(11), 2004–2016. https://doi.org/10.1109/TNNLS.2014.2302475
[4]. Zhang, P., Wu, Z., Dong, H., Tan, M., & Yu, J. (2020). Reaction-wheel-based roll stabilization for a robotic fish using neural network sliding mode control.IEEE/ASME Transactions on Mechatronics, 25(4), 1904–1911. https://doi.org/10.1109/TMECH.2020.2992038
[5]. He, W., Ge, S. S., Li, Y., Chew, E., & Ng, Y. S. (2015). Neural network control of a rehabilitation robot by state and output feedback.Journal of Intelligent & Robotic Systems, 80(1), 15–31. https://doi.org/10.1007/s10846-014-0150-6
[6]. Yang, C., Jiang, Y., Li, Z., He, W., & Su, C.-Y. (2017). Neural control of bimanual robots with guaranteed global stability and motion precision.IEEE Transactions on Industrial Informatics, 13(3), 1162–1171. https://doi.org/10.1109/TII.2016.2612646
[7]. Sun, L., & Liu, Y. (2020). Extended state observer augmented finite-time trajectory tracking control of uncertain mechanical systems.Mechanical Systems and Signal Processing, 139, 106374. https://doi.org/10.1016/j.ymssp.2019.106374
[8]. Zhao, L., Zhang, B., Yang, H., & Wang, Y. (2017). Finite-time tracking control for pneumatic servo system via extended state observer.IET Control Theory & Applications, 11(16), 2808–2816. https://doi.org/10.1049/iet-cta.2017.0327
[9]. Cao, S., Sun, L., Jiang, J., & Zuo, Z. (2023). Reinforcement learning-based fixed-time trajectory tracking control for uncertain robotic manipulators with input saturation.IEEE Transactions on Neural Networks and Learning Systems, 34(8), 4584–4595. https://doi.org/10.1109/TNNLS.2021.3116713
[10]. Bhat, S. P., & Bernstein, D. S. (2000). Finite-time stability of continuous autonomous systems.SIAM Journal on Control and Optimization, 38(3), 751–766. https://doi.org/10.1137/S0363012997321358
Cite this article
Zhang,Z. (2024). Reproduction and generalization of robot trajectory tracking control method using reinforcement learning and neural network techniques. Theoretical and Natural Science,56,19-24.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Applied Physics and Mathematical Modeling
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