Robust Controller Considering Road Disturbances for a Vehicular Flywheel Battery System

Abstract

At present, the stability study of the flywheel battery system under the influence of road conditions is only limited to the analysis of dynamic characteristics but also lacks effective controller considering road disturbances. In order to solve this defect and further improve the robust performance of vehicular flywheel battery systems under road disturbances, a robust controller considering interference of road surface roughness is proposed. Firstly, on the basis of a brief introduction to flywheel battery system structure, the influence degree of the flywheel by road condition is compared to find the key areas most affected by road disturbance factors. Then the nonlinear dynamic model of the axial suspension system is constructed, and the actual road surface roughness is regarded as the unmodeled dynamics of the external disturbance emphatically. Following, the established unknown system dynamics is approximated by radial basis function (RBF) neural network based on the minimum parameter learning method, the control input is generated by sliding-mode control law, and the weight adjustment of neural network is replaced by the designed adaptive law of parameter estimation. The effects of different levels of road surface roughness on the system are simulated, and the robustness of the proposed controller is verified based on the Lyapunov method. Finally, the experimental platform to simulate road disturbances is designed ingeniously. Experimental results show that the proposed controller can make the flywheel battery system have good robustness under different road conditions.