Neural Network Based Sliding Mode Control for Uncertain Discrete-Time Nonlinear Systems with Time-Varying Delay
نویسندگان
چکیده
The present paper discusses the stability analysis of discrete-time uncertain nonlinear systems with time-varying delay, uncertainties related to parametric uncertainty and unknown nonlinearity. The time-varying delay considered has minimum and maximum bounds. The novelty of Chebyshev Neural Network (CNN) is that it requires much less computation time as compared to multi layer neural network (MLNN) and radial basis function network (RBFN). It is preferred to approximate the unknown nonlinearities. Furthermore, results for robust stabilization of discrete-time uncertain nonlinear systems with timevarying delay are given on the basis of linear matrix inequalities (LMI). The sufficient condition is derived for the asymptotic stability of the defined systems. The proposed controller guarantees the system state trajectory to the designed sliding surface in the presence of uncertainties and time-varying delay. Simulation results illustrate the validity of the proposed approach. Key-words: Chebyshev Neural Network; Sliding Mode Control; Linear Matrix Inequalities; Lyapunov-Krasovskii Function; Time-varying Delay
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