Decoupled sliding-mode with fuzzy-neural network controller for nonlinear systems

• Published in: International journal of approximate reasoning Vol. 46; no. 1; pp. 74 - 97
• Main Authors: Hung, Lon-Chen, Chung, Hung-Yuan
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.09.2007; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Fuzzy; Learning and adaptive systems; Neural; Sliding-mode control; Fuzzy; Sliding-mode control; Neural; Input output; Sliding mode; Variable structure system; Non linear control; Lyapunov method; Control synthesis; Updating; Neural network; Adaptive method; Fuzzy neural nets; Fuzzy logic; Fuzzy control; Asymptotic stability; Decoupled control; Distributed control; Artificial intelligence; Lyapunov function
• ISSN: 0888-613X; 1873-4731
• DOI: 10.1016/j.ijar.2006.08.002

In this paper, a decoupled sliding-mode with fuzzy-neural network controller for nonlinear systems is presented. To divided into two subsystems to achieve asymptotic stability by decoupled method for a class of fourth-order nonlinear system. The fuzzy-neural network (FNN) is the main regulator controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the ideal computational controller and the FNN controller. A tuning methodology is derived to update weight parts of the FNN. Using Lyapunov law, we derive the decoupled sliding-mode control law and the related parameters adaptive law of FNN. Finally, the decoupled sliding-mode with fuzzy-neural network control (DSMFNNC) is used to control three highly nonlinear systems and confirms the validity of the proposed approach. The method can control one-input and multi-output nonlinear systems efficiently. Using this approach, the response of system will converge faster than that of previous reports.