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 servohydraulic 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 AutoRegressive 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.