An Investigation to Controller Design for Active Vehicle Suspension by Using GA-Based PID and Fuzzy Logic 2002-01-0983

Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, a new control strategy is proposed for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. The PID controller is used to minimize vehicle body vertical acceleration and the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to achieve optimal vehicle performances and adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, gain values and the membership function of fuzzy logic controller on-line. By a four degree-of-freedom nonlinear vehicle model, the proposed control scheme is implemented and simulations are carried out in different road disturbance input conditions. Simulation results show that the present control scheme is very effective in reducing peak values of vehicle body accelerations, especially within the most sensitive frequency range of human response, and attenuating the excessive tire deflection to enhance road holding performance. It also shows good stability and adaptability even if the system is subject to adverse road conditions, such as a pothole, an obstacle or a step input. Compared with conventional passive suspensions and active vehicle suspension systems by using different control schemes, i.e., a linear and fuzzy logic control, the combined PID and fuzzy control without parameters self-tuning, the new proposed control system with GA-based self-learning ability in this paper can improve vehicle ride comfort performance significantly and offer better system robustness.