Adaptive neural/fuzzy control for interpolated nonlinear systems

被引:46
作者
Diao, YX [1 ]
Passino, KM
机构
[1] IBM Corp, Thomas J Watson Res Ctr, Hawthorne, NY 10532 USA
[2] Ohio State Univ, Dept Elect Engn, Columbus, OH 43210 USA
关键词
adaptive control; nonlinear systems; online approximation; stability analysis;
D O I
10.1109/TFUZZ.2002.803493
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Adaptive control for nonlinear time-varying systems is of both theoretical and practical importance. In this paper, we propose an adaptive control methodology for a class of nonlinear systems with a time-varying structure. This class of systems is composed of interpolations of nonlinear subsystems which are input-output feedback linearizable. Both indirect and direct adaptive control methods are developed, where the spatially localized models (in the form of Takagi-Sugeno fuzzy systems or radial basis function neural networks) are used as online approximators to learn the unknown dynamics of the system. Without assumptions on rate of change of system dynamics, the proposed adaptive control methods guarantee that all internal signals of the system are bounded and the tracking error is asymptotically stable. The performance of the adaptive controller is demonstrated using a jet engine control problem.
引用
收藏
页码:583 / 595
页数:13
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