Neural Network-Based Identification and Approximate Predictive Control of a Servo-Hydraulic Vehicle Suspension System

This paper presents multi-layer feedforward neural network-based identification and approximate predictive controller (NNAPC) 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. A suspension travel controller is developed to improve the ride comfort and handling quality of the system. A SISO neural network (NN) model based on Nonlinear AutoRegres-sive with eXogenous input (NARX) is developed using input-output data sets obtained from mathematical model simulation. The NN model was trained using Levenberg-Marquardt algorithm. The NNAPC was used to predict the future responses that are optimized by cost minimization. The proposed controller is compared with a constant-gain PID controller (based on Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the NNAPC over the generic PID - controller in adapting to the deterministic road disturbance.