| The objective of this thesis is to develop a realistic suspension system model that is used to design a robust intelligent suspension system controller. The purpose of a suspension system is to improve riding quality while maintaining good handling characteristics subject to different road profiles. The objective of designing the controller for vehicle suspension systems is to reduce the traditional design compromise between ride and handling by directly controlling the suspension forces to suit the road and driving conditions. The vehicle suspension systems are inherently complex and nonlinear with uncertain parameters. The complex nonlinear model has made the use of conventional linear and nonlinear control techniques very difficult. A realistic nonlinear suspension model is developed by using kinematics and kinetics analysis. This model has the advantage of taking into account the essential system dynamics, model uncertainties, parameters variations, and disturbances. In their current form, the widely used modeling and control techniques are not suitable for dealing with rough road profiles and driving conditions. This thesis addresses the combination of sliding mode and fuzzy logic control methodologies that have emerged as promising techniques for dealing with such complex uncertain systems. In addition, neural networks are shown to be very efficient in providing a systematic procedure as compensation or tuning factors. In order to achieve the desired ride and comfort, a sliding mode neural network inference fuzzy logic control will be used in this thesis. Extensive simulations are performed in the different road conditions and the results show that the proposed controller outperforms existing linear and nonlinear controllers in achieving the desired performance, i.e., good handling and ride quality. |
