RBF Neural Network Sliding Mode Control for Passification of Nonlinear Time-Varying Delay Systems with Application to
Baoping Jiang1, Dongyu Liu1, Hamid Reza Karimi2
1School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
Abstract:
This paper is devoted to studying the passivity-based sliding mode control for nonlinear systems and its application to dock cranes through an adaptive neural network approach, where the system suffers from time-varying delay, external disturbance and unknown nonlinearity. First, relying on the generalized Lagrange formula, the mathematical model for the crane system is established. Second, by virtue of an integral-type sliding surface function and the equivalent control theory, a sliding mode dynamic system can be obtained with a satisfactory dynamic property. Third, based on the RBF neural network approach, an adaptive control law is designed to ensure the finite-time existence of sliding motion in the face of unknown nonlinearity. Fourth, feasible easy-checking linear matrix inequality conditions are developed to analyze passification performance of the resulting sliding motion. Finally, a simulation study is provided to confirm the validity of the proposed method.
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