ROBUST ADAPTIVE NN CONTROL OF A CLASS OF SEMI-STRICT FEEDBACK NONLINEAR SYSTEMS
S. S. Ge1, J. Wang and T. H. Lee
Department of Electrical and Computer Engineering National University of Singapore Singapore 117576

This paper presents a robust adaptive control approach for a class of semi-strict feedback nonlinear system with both unknown high-frequency gain and unknown nonlinearities. The unknown nonlinearities comprise two types of nonlinear functions: one naturally satisfies the triangularity condition and will be approximated by linearly parameterized approximators; while the other is assumed to be partially known and consists of parametric uncertainties and known bounding functions which also satisfy the triangularity condition. With the utilization of adaptive backstepping and tuning functions, the proposed design method expands the class of nonlinear systems for which robust adaptive control approaches have been studied. It has been proven that the proposed robust adaptive scheme can guarantee the uniform ultimate boundedness of the closed-loop system signals. Simulation studies are included to illustrate the effectiveness of the proposed approach.
Keywords: Robust adaptive control, Neural networks, Semi-strict feedback nonlinear systems
Session slot T-Fr-A21: Posters of Learning, Stochastic, Fuzzy and Nerural Systems/Area code 3e : Fuzzy and Neural Systems

|