A Neural Network Sliding Controller for Active Vehicle Suspension

Abstract

The hydraulic active suspension systems have certain nonlinear and time-varying behaviors. It is difficult to establish an appropriate dynamic model for model-based controller design. Here a novel neural network based sliding mode control is proposed by combining the advantages of the adaptive, radial basis function neural network and sliding mode control strategies to release the model information requirement. It has on-line learning ability for handling the system time-varying and nonlinear uncertainty behaviors by adjusting the neural network weightings and/or radial basis function parameters. It is implemented on a quarter-car hydraulic active suspension system. The experimental results show that this intelligent control approach effectively suppresses the oscillation amplitude of sprung mass in response to road surface disturbances.