Event-Based Nonsingular Fixed-Time Tracking Control of an Uncertain Manipulator System Subject to Full-State Static Constraints

• Published in: IEEE transactions on cybernetics Vol. 54; no. 7; pp. 3980 - 3993
• Main Authors: Zhang, Zhongcai, Gao, Yang, Sun, Wei, Wu, Yuqiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Convergence; Error analysis; Event detection; Event triggered control; fixed-time tracking control; Liapunov functions; Manipulator dynamics; manipulator systems; Manipulators; neural network; Neural networks; Parameter uncertainty; Radial basis function; Robot arms; state constraints; Tracking; Tracking control; Tracking errors; Trajectory; Uncertainty
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2023.3289947

This article centers around investigating the event-triggered nonsingular fixed-time tracking issue for an <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula>-link rigid robot manipulator with full-state constraints, external disturbances, and model uncertainties. We propose the definition of the constrainedly practically fixed-time stability (CPFTS) and provide a sufficient condition for CPFTS. A novel auxiliary function is developed to address the singularity issue caused by repeated differentiation in achieving the fixed-time tracking control. The uncertain parameters are approximated using the radial basis function neural network (RBFNN). This study proposes the model-based and the neutral network-based tracking control approaches, designed using the scaling function technique and the barrier Lyapunov function, respectively, to ensure that the tracking error systems are CPFTS and the full-state constraints comply. Moreover, the communication transmission load is reduced using the relative threshold event-triggered control strategy. Simulation results demonstrate the effectiveness of the proposed tracking control algorithms.