Intelligent Feedback Linearization-based Control of Half-Car Active Suspension Systems

John E.D. Ekoru and Jimoh O. Pedro


Neural Networks, NARMA-L2, PID Control, Nonlinear Vehicle Suspension System, Hydraulic Actuator Dynamics


This paper presents the development of an indirect adaptive Neural Network-based Feedback Linearization controller (NARMA-L2) for a 4 degree-of-freedom, nonlinear, half-car active vehicle suspension system. An inner Proportional+Integral+Derivative-based hydraulic actuator force feedback control loop is incorporated in the design to ensure good force tracking. Performance of the active vehicle suspension system is compared with that of a passive vehicle suspension system, with similar model parameters, in the presence of model uncertainties in the form of variation in vehicle sprung mass loading. Vehicle output responses in the time domain show improved performance by the NARMA-L2 based active vehicle suspension system over the passive vehicle suspension system within the given constraints.

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