Robust neurooptimal control for a robot via adaptive dynamic programming

Kong, Linghuan; He, Wei; Yang, Chenguang; Sun, Changyin

Abstract

We aim at the optimization of the tracking control of a robot to improve the robustness, under the effect of unknown nonlinear perturbations. First, an auxiliary system is introduced, and optimal control of the auxiliary system can be seen as an approximate optimal control of the robot. Then, neural networks (NNs) are employed to approximate the solution of the Hamilton-Jacobi-Isaacs equation under the frame of adaptive dynamic programming. Next, based on the standard gradient attenuation algorithm and adaptive critic design, NNs are trained depending on the designed updating law with relaxing the requirement of initial stabilizing control. In light of the Lyapunov stability theory, all the error signals can be proved to be uniformly ultimately bounded. A series of simulation studies are carried out to show the effectiveness of the proposed control.

Citation

Kong, L., He, W., Yang, C., & Sun, C. (2021). Robust neurooptimal control for a robot via adaptive dynamic programming. IEEE Transactions on Neural Networks and Learning Systems, 32(6), 2584-2594. https://doi.org/10.1109/tnnls.2020.3006850

• Journal Article Type: Article
• Acceptance Date: Jul 1, 2020
• Online Publication Date: Sep 17, 2020
• Publication Date: Jun 1, 2021
• Deposit Date: Oct 14, 2020
• Publicly Available Date: Oct 15, 2020
• Journal: IEEE Transactions on Neural Networks and Learning Systems
• Print ISSN: 2162-237X
• Electronic ISSN: 2162-2388
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Not Peer Reviewed
• Volume: 32
• Issue: 6
• Pages: 2584-2594
• DOI: https://doi.org/10.1109/tnnls.2020.3006850
• Keywords: Computer Networks and Communications; Software; Artificial Intelligence; Computer Science Applications
• Public URL: https://uwe-repository.worktribe.com/output/6778797