Neural Controller Design for Suspension Systems

The main problem of vehicle vibration comes from road roughness. An active suspension system possesses the ability to reduce acceleration of sprung mass continuously as well as to minimize suspension deflection, which results in improvement of tire grip with the road surface. Thus, brake traction control and vehicle maneuverability can be improved consider ably .This study developed a new active suspension system for a quarter-car model. The designed system is based on neural network controller with an input as a regressor and it provided through a lag network that includes reference input , system output and control signal system to the previous sate. In this paper, the system is based on neural network controller that is a regressor input provided through a lag network, including reference input, system output and control signal to previous state. The neural network outputs are the same control signals applied to the suspension system. Feedback system is taken as the output of the displacement body and is applied to lag network. Roughness of the road surface is considered as a reference input. To train, the neural network uses different idea by introducing a cost function for the system and optimizing it, the best coefficients are selected for the neural network.