Observer-based neural control for MIMO pure-feedback non-linear systems with input saturation and disturbances

• Published in: IET control theory & applications Vol. 10; no. 17; pp. 2314 - 2324
• Main Authors: Liu, Wenhui, Lu, Junwei, Zhang, Zhengqiang, Xu, Shengyuan
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 21.11.2016
• Subjects: adaptive control; adaptive neural backstepping control; control system synthesis; Control systems; Controllers; coordinate transform; Disturbances; Dynamical systems; feedback; function approximation; input disturbances; input saturation; MIMO pure‐feedback nonlinear systems; MIMO systems; multiple‐input multiple‐output pure‐feedback systems; Neural networks; neurocontrollers; nonlinear control systems; nonlinear disturbance observer; Nonlinear dynamics; nonlinear functions; Nonlinearity; observers; observer‐based neural control; output‐feedback neural controller design; Research Article; Saturation; state observer; state trajectories; uncertain nonlinear function approximation; output-feedback neural controller design; adaptive neural backstepping control; nonlinear functions; state trajectories; state observer; multiple-input multiple-output pure-feedback systems; neural networks; input disturbances; adaptive control; control system synthesis; coordinate transform; MIMO pure-feedback nonlinear systems; feedback; MIMO systems; neurocontrollers; function approximation; nonlinear control systems; input saturation; uncertain nonlinear function approximation; nonlinear disturbance observer; observer-based neural control; observers
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2016.0789

This study considers the adaptive neural backstepping control for multiple-input and multiple-output pure-feedback systems subject to input saturation and disturbances. Neural networks are used to approximate the uncertain non-linear functions without any prior limited conditions. A non-linear disturbance observer and a state observer are constructed to design the output-feedback neural controller. A new coordinate transform is defined to handle the pure-feedback systems in the backstepping procedure. The proposed controller can make sure that all the state trajectories are ultimately bounded in the pure-feedback non-linear systems. An illustrative example is given to show the usefulness of the authors' designed new control method.