Direct adaptive neural control of a quarter-car active suspension system

This paper presents the design and implementation of a direct adaptive neural network (DANN) based feedback linearization controller for a two degree of freedom (2DOF), quarter-car active vehicle suspension system (AVSS). The main objective is to improve ride comfort and handling quality. The constant gain PID controller (based on Ziegler-Nichols tuning method) is used to benchmark the DANN controller during a suspension travel sinusoidal set point tracking in the presence of deterministic road disturbance. The maximum sprung mass acceleration of the DANN controller was about 2.723ms^-2, for the PID, it was 2.676ms^-2. The tire deflection was approximately 5mm and 4mm for the DANN and the PID controllers. The performance of the DANN controller was achieved at a marginally higher cost of the control input.