Neural Network-Based Finite-Time Command Filtering Control for Switched Nonlinear Systems With Backlash-Like Hysteresis

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 7; pp. 3268 - 3273
• Main Authors: Fu, Cheng, Wang, Qing-Guo, Yu, Jinpeng, Lin, Chong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive neural control; arbitrary switching; backlash-like hysteresis; Backstepping; command filtering; Control stability; Error compensation; finite-time; Hysteresis; Neural networks; Nonlinear control; Nonlinear systems; Stability analysis; Switched systems; Switches; Tracking control; Tracking errors
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.3009871

This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed. Compared with the traditional command filter technique, the main advantage is that the improved error compensation signals are designed to remove the filtered error and the Levant differentiators are introduced to approximate the derivative of the virtual control signal. The finite-time adaptive neural controller is proposed via the new command filter backstepping technique, and the tracking error converges to a small neighborhood of the origin in finite time. Finally, the simulation results are provided to testify the validity of the proposed method.