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|Title:||Robust Adaptive Controller Design for a Class of Uncertain Nonlinear Systems Using Online T–S Fuzzy-Neural Modeling Approach|
|Publisher:||IEEE Systems, Man, and Cybernetics Society|
|Abstract:||This paper proposes a novel method of online modeling and control via the Takagi–Sugeno (T–S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T–S fuzzy-neural controllers have been made on some nonafﬁne nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T–S fuzzy control methods can model and control them only with great difﬁculty, if at all. Instead of modeling these uncertain functions directly, we propose that a T–S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identiﬁcation algorithm for the VLS and put signiﬁcant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper|
|Appears in Collections:||教師著作|
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