A neurofuzzy controller for full vehicle active suspension systems

Abstract

The main objectives of designing the controller for vehicle suspension systems are to reduce the discomfort sensed by passengers that arises from road roughness and to increase the road handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as many control objectives as possible. This paper deals with an artificial intelligent neurofuzzy (NF) technique to design a robust controller. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibration on each corner of the vehicle by supplying control forces to the suspension system when travelling on a rough road. The other purpose of using the NF controller for the vehicle model is to reduce the body inclinations that are made during intensive maneuvers, including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparison with an optimal proportional-integral-derivative (PID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.