Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 4579263 - 9
• Main Authors: Fu, Zhijun, Lu, Yan, Zhou, Fang, Guo, Yaohua, Guo, Pengyan, Feng, Heyang
• Format: Journal Article
• Language: English
• Published: United States Hindawi 06.01.2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Adaptive control; Algorithms; Approximation; Back propagation; Computer Simulation; Control methods; Controllers; Design; Feedback; Feedback control; Identification; Laws, regulations and rules; Methods; Neural networks; Neural Networks, Computer; Nonlinear Dynamics; Nonlinear systems; Nonlinearity; Parameter identification; Research Design; Synthesis; System dynamics; System effectiveness; Tracking control; Tracking problem
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/4579263

This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.