Designing a Neuro-Fuzzy Controller for a Vehicle Suspension System, Using Feedback Error Learning

In the present research, a non-linear controller is designed for the control of an active suspension system for a half-model vehicle, using a Fuzzy Neural Network (FNN) along with Feedback error learning. The purpose in a vehicle suspension system is reduction of transmittance of vibrational effects from the road to the vehicle chassis, hence providing ride comfort. This requires a minimum reduction in road contact along rough roads. In addition, the role of the suspension system in vehicle control along a curved route and in accelerating and braking is quite evident. To accomplish this, one can first design a PD controller for the suspension system, using a classic control method and use it to train a fuzzy controller. This controller can be trained using the PD controller output error on an online manner. Once trained, the PD controller is removed from the control loop and the neuro-fuzzy controller takes on. In case of a change in the parameters of the system under control, the PD controller enters the control loop again and the neural network gets trained again for the new condition. Important characteristics of the proposed controller is that no mathematical model is needed for the system components, such as the non-linear actuator, spring, or shock absorber, and that no system Jacobian is needed. The performance of the proposed FNN controller is compared with that of the PD controller through simulations. The results show that the proposed controller is indeed capable of meeting the stated control requirements.