Adaptive Fuzzy Fault Tolerant Control for Robot Manipulators with Fixed-Time Convergence

• Published in: IEEE transactions on fuzzy systems Vol. 31; no. 9; pp. 1 - 10
• Main Authors: Van, Mien, Sun, Yuzhu, Mcllvanna, Stephen, Nguyen, Minh-Nhat, Khyam, Mohammad Omar, Ceglarek, Dariusz
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Artificial neural networks; backsepping control; Backstepping; Control of robots; Controllers; Convergence; Dynamic models; Fault tolerance; fault tolerant control; fixed-time convergence; Fuzzy control; Fuzzy logic; fuzzy logic system; Manipulator dynamics; Manipulators; Robot arms; Robot control; Robot dynamics; Robots; Robustness; Robustness (mathematics); Tracking errors
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2023.3247693

This paper aims to resolve the three major issues of fault tolerant control (FTC) for robot manipulators: (i) the faster response, lower tracking errors, lower chattering and higher robustness of the FTC, (ii) the requirement of partial or full knowledge of robot dynamics for the design of model-based FTC, and (iii) the global fixed-time convergence of the system. First, a fixed-time controller based on a backstepping control is designed and its disadvantages are analysed. Then, an adaptive fuzzy backstepping control is developed to enhance the tracking performance of the system. The proposed approach does not require the full prior knowledge of robot dynamic model, thus facilitating implementation of the controller in practical applications. In addition, the tracking errors of the system will be practically convergent within a fixed-time, which provides additional system information in advance. The fixed time convergence of the system is mathematically proved and the performance of the system is demonstrated for FTC of a PUMA560 robot.