Comparison Study: Controlling 3 DOF Active Suspension System Using Different Intelligent Methods

Vehicles are considered the most widely applications related to the study of suspension system. Its main goals are isolating the disturbances coming from the roads to the body of the vehicle, handling the changeable roads’ profiles and providing a comfort ride for the passengers. Controlling suspension system is widely studied to achieve its goals optimally. Classical controllers such as classical PID and LQR have showed better responses compared to uncontrollable suspension. Soft computing methods such as fuzzy logic and neural networks are used to enhance the response of classical controllers. In order to illustrate the advantages of the intelligent methods, 3 DOF active suspension system has been modeled and controlled by using different approaches, i.e. Fuzzy Logic Controller (FLC), an optimized FLC using PSO and GA and finally a neural network controller. A comparison of these controllers has been shown by evaluating their performances depending on some predefined indices in order to facilitate choosing a specific controller for a desired objective.