Design Neural Control System for Full Vehicle Nonlinear Active Suspension with Hydraulic Actuators

The artificial neural network is an intelligent device which is wildly used to design a robust controller for nonlinear processes in engineering problem. In many control architectures, multilayer perceptron neural networks can be used as basic building blocks, such as model reference adaptive control, model predictive control, nonlinear internal model control, adaptive inverse control system and neural adaptive feedback linearization. In all of these methods minimum two neural networks to design the controller are used (in the adaptive inverse control system it must be used three neural networks to design the controller) one as identifier and other one as controller. In this paper a neural network has been used to design the controller for a full vehicle nonlinear active suspension system eight nonlinear dampers. This controller has been used to improve vehicle performance: riding comfort and road handling. The robustness of proposed controller is investigated and its results have been compared with the PID controllers.