Intelligent Decentralized Adaptive Controller Design for a Class of Large Scale Nonlinear Non-affine Systems: Nonlinear Observer-based Approach

Abstract: In this study, an observer based decentralized Fuzzy Adaptive Controller (FAC) is designed for a class of large scale non-affine nonlinear systems with unknown functions of the subsystems and interactions. The proposed controller has the following main characteristics: 1) On-line adaptation of both controller and observer parameters, 2) stabilization of the closed loop system, 3) convergence of both tracking and observer errors to zero, 4) boundedness of all signals involved, 5) being prone to employing experts' knowledge in controller design procedure, 6) chattering avoidance. An illustrative example is given to show the promising performance of the proposed method

Improved Polynomial Fuzzy Modeling and Controller with Stability Analysis for Nonlinear Dynamical Systems

This study presents an improved model and controller for nonlinear plants using polynomial fuzzy model-based (FMB) systems. To minimize mismatch between the polynomial fuzzy model and nonlinear plant, the suitable parameters of membership functions are determined in a systematic way. Defining an appropriate fitness function and utilizing Taylor series expansion, a genetic algorithm (GA) is used to form the shape of membership functions in polynomial forms, which are afterwards used in fuzzy modeling. To validate the model, a controller based on proposed polynomial fuzzy systems is designed and then applied to both original nonlinear plant and fuzzy model for comparison. Additionally, stability analysis for the proposed polynomial FMB control system is investigated employing Lyapunov theory and a sum of squares (SOS) approach. Moreover, the form of the membership functions is considered in stability analysis. The SOS-based stability conditions are attained using SOSTOOLS. Simulation results are also given to demonstrate the effectiveness of the proposed method