Robust adaptive multiple models based fuzzy control of nonlinear systems

A new robust adaptive multiple models based fuzzy control scheme for a class of unknown nonlinear systems is proposed in this paper. The nonlinear system is expressed by using the Takagi–Sugeno (T–S) method, and some identification adaptive T–S models along with their corresponding controllers, are used in order to control efficiently the unknown system. The modeling error that is produced due to the rules. In this paper, in order to solve this problem, we design a control scheme that is based on updating rules that utilize the σ-modification method. Every T–S controller is updated indirectly by using the robust updating rules and the final control signal is determined by using a performance index and a switching rule. By using the Lyapunov stability theory it is shown that σ-modification based rules can ensure the robustness of the system and define a bound for the steady state identification error. The main objectives of the robust controller are: (i) to ensure that the real plant system will remain stable despite the existence of modeling errors and (ii) to ensure that the real plant will track with a high accuracy the state trajectory of a given reference model. The effectiveness of the proposed method is demonstrated by computer simulations on a well known benchmark problem. & 2015 Elsevier B.V. All rights reserved.