Intelligent Sliding-Mode Control Using RBFN for Magnetic Levitation System

An intelligent sliding-mode control system using a radial basis function network (SMCRBFN) is proposed to control the position of a levitated object of a magnetic levitation system to compensate the uncertainties including the friction force in this study. First, the dynamic model of the magnetic levitation system is derived. Then, a sliding-mode approach is proposed to compensate the uncertainties that occurred in the magnetic levitation system. Moreover, to relax the requirement of uncertainty bound in the design of a traditional sliding-mode control system and further increase the robustness of the magnetic levitation system, a radial basis function network estimator is proposed to estimate the uncertainties of the system dynamics online. The effectiveness of the proposed control scheme is verified by some experimental results. With the proposed SMCRBFN system, the position of the levitated object of the magnetic levitation system possesses the advantages of good transient control performance and robustness to uncertainties for tracking periodic trajectories