Neural network-based model predictive control of a servo-hydraulic vehicle suspension system

This paper presents the design of a multilayer feedforward neural network based model predictive controller (NNMPC) for a two degree-of-freedom (DOF), quarter car servo hydraulic vehicle suspension system. The nonlinear dynamics of the servo hydraulic actuator is incorporated in the suspension model and thus a suspension travel controller is developed to indirectly improve the ride comfort and handling quality of the suspension system. A SISO feedforward multilayer perceptron (MLP) neural network (NN) model is developed using input-output data sets obtained from the mathematical model simulation. Levenberg-Marquandt algorithm was employed in training the NN model. The NNMPC was used to predict the future responses that are optimized in a subloop of the plant for cost minimization. The proposed controller is compared with an optimally tuned constant-gain PID controller (based on Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road input disturbance. Simulation results demonstrate the superior performance of the NNMPC over the generic PID based in adapting to the deterministic road disturbance.