Robust Adaptive Fixed-Time Sliding-Mode Control for Uncertain Robotic Systems With Input Saturation

• Published in: IEEE transactions on cybernetics Vol. 53; no. 4; pp. 2636 - 2646
• Main Authors: Hu, Yunsong, Yan, Huaicheng, Zhang, Hao, Wang, Meng, Zeng, Lu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Artificial neural networks; Control methods; Control systems design; Convergence; Dynamical systems; Fixed-time control; input saturation; Manipulator dynamics; Neural networks; neural networks (NNs); nonsingular fast terminal sliding mode (NFTSM); Parameter uncertainty; Radial basis function; Robot arms; Robot control; Robots; Robust control; Sliding mode control; Uncertainty
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2022.3164739

In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.