Adaptive sliding-mode control method and simulation method for nonlinear system

Abstract

The divisional application relates an adaptive sliding-mode control method and simulation method for a nonlinear system, belongs to the field of artificial intelligence and control, and solves the asymptotic stability of a closed-loop control system. The adaptive sliding-mode control method is characterized by applying an LS-SVM structure to the nonlinear system to approximate an ideal state feedback controller to construct a new feedback controller; applying sliding-mode control to the nonlinear system to compensate the approximate errors and / or uncertain external disturbance of the LS-SVM regression, and determining a weight parameter vector according to an adaptive rate. The method designs a feedback linearization controller by fully utilizing the nonlinear function approximation capability of the LS-SVM regression, introduces the sliding-mode control to compensate the influences of the approximate errors and the uncertain external disturbance of the LS-SVM regression on system output so as to adjust the LS-SVM weight parameter.

Description

[0001] This application is a divisional application with application number 2017111048243, application date 2017-11-10, and invention name "Adaptive sliding mode control method for nonlinear systems" technical field [0002] The invention belongs to the field of artificial intelligence and control, and relates to an adaptive sliding mode control method of a nonlinear system. Background technique [0003] Sliding mode variable structure control of nonlinear uncertain systems has always been a hot spot in the control field, and many scholars have achieved research results in this field. Since the sliding mode control of the nonlinear system requires a rough mathematical model of the known system, the dependence of the sliding mode control on the system model is increased. With the development of artificial intelligence theory, fuzzy logic and neural network are introduced into the design of sliding mode control, which can effectively reduce the dependence of sliding mode contr...

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Problems solved by technology

[0004] In order to solve the problem that the closed-loop control system is asymptotically stable, the present invention proposes the following scheme:

This method makes full use of the nonlinear function approximation ability of LS-SVM regression to design a feedback linearization controller, introduces sliding mode control to compensate the approximation error of LS-SVM regression and the influence of uncertain external disturbance on the system output, and performs LS-SVM weight parameter adjustment, and finally verified the design scheme through a simulation example, indicating that the present invention can solve the asymptotically stable problem of the closed-loop control system

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