GA-based real-time adaptive fuzzy control of semi-active automotive suspensions
Today's vehicles employ a large number of systems and sensors aimed at enhancing the performance and ride comfort of the vehicle. With the advancements in vehicle designs and technology, the performance enhancement that the vehicle can exhibit is limited by the performance of the suspension system. The suspension system in a vehicle acts as the essential link between the vehicle body and the road pavement. Due to the lack of a suspension system that is capable of adapting to its environment, the vehicle is unable to fully use its engine capabilities without causing problems in ride comfort, and handling. This thesis examines a possible implementation of a real-time adaptive control system for the automotive vehicle suspension system. The unknown conditions of the vehicle operation environment make it difficult to develop a control system capable of providing significant extended enhancement to both the vehicle ride quality, and handling. In this thesis a genetic algorithm-based control system capable of tuning its performance to the current operational environment of the vehicle, is introduced. Understanding the effectiveness of the control system at stabilizing the vehicle is achieved by comparing a simulation of a quarter vehicle semi-active suspension system to that of a standard passive suspension system. The benefits of using the proposed adaptive control system are examined using various road conditions based on the road standards defined by the International Organization for Standardization (ISO).