Tracking Control of Magnetic Levitation System Using Model-Free RBF Neural Network Design

This paper focuses on the tracking control problem of Magnetic Levitation System (MLS). MLS is highly unstable nonlinear system, in the process of operation, MLS needs to have strong robustness and anti-interference ability. Due to disturbance and parameter uncertainties in MLS, it is complex to obtain the exact dynamics of MLS, and it is difficult to design a suitable controller. The dynamic equation of the system is established by Lagrange equation, and then we propose an adaptive Sliding Mode Control (SMC) based on Radial Basis Function Neural Network (RBFNN). Because of the parameter uncertainties and disturbance in MLS, RBFNN is used to approximate the unknown dynamics in MLS. The stability of the closed-loop system is strictly proved by using the Lyapunov stability theory, which can achieve the uniform ultimate boundedness (UUB) of the signals of the closed-loop MLS. MATLAB environment is used to verify the performance of the proposed controller. Considering disturbance, parameter change, or unmodeled dynamics in MLS, proposed controller is compared with other nonlinear controllers, simulation results verify the effectiveness of the proposed approach.