Fixed-time adaptive neural tracking control for nonstrict-feedback nonlinear systems with mismatched disturbances using an event-triggered scheme

• Published in: Nonlinear dynamics Vol. 111; no. 6; pp. 5383 - 5400
• Main Authors: Mei, Yu, Li, Feng, Xia, Rongsheng, Park, Ju H., Shen, Hao
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.03.2023; Springer Nature B.V
• Subjects: Adaptive control; Automotive Engineering; Classical Mechanics; Closed loops; Control; Control methods; Control systems design; Convergence; Coordinate transformations; Disturbances; Dynamical Systems; Engineering; Feedback control; Fuzzy logic; Mechanical Engineering; Neural networks; Nonlinear control; Nonlinear systems; Nonlinearity; Original Paper; Radial basis function; Tracking control; Tracking errors; Vibration; Fixed-time convergence; Neural networks; Nonlinear systems; Adaptive event-triggered scheme
• ISSN: 0924-090X; 1573-269X
• DOI: 10.1007/s11071-022-08146-3

This paper investigates the adaptive fixed-time neural tracking control problem for the uncertain nonstrict-feedback nonlinear systems subject to mismatched disturbances via an event-triggered scheme. The radial basis function neural networks are utilized to approximate the unknown nonlinearities and tighten the systems accordingly to obtain fixed-time convergence form easily. A novel event-triggered control mechanism is utilized to switch alternately between relative threshold strategy and fixed threshold strategy and keep balance between the number of triggering and the tracking error through the comparison of numerical examples, and the Zeno behavior is also excluded. Then, an adaptive event-triggered controller is designed via the backstepping technique. The proposed control method can ensure that the tracking error converges to a small range of the origin and all the signals of the closed-loop system are bounded within a fixed time. Finally, a single-link manipulator example and a numerical example are provided to verify the validity and practicability of the proposed method.