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

Abstract

This paper presents the design of a multi-layer 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 multi-layer 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 sub-loop 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.