Optimization of Semi-active Suspension System Using Particle Swarm Optimization Algorithm

The paper involves modeling of quarter car models of passive and various semi-active suspension systems. Semi-active suspension is better than the passive and active suspensions based on optimum performance within the constraints of weight and operational cost. Fuzzy logic controllers are incorporated in the design scheme of semi-active models. Fuzzy logic based systems can handle non-linearities through heuristic rules. Particle swarm optimization technique is applied in order to determine the optimized scaling factors keeping the normalized ranges of inputs and output of fuzzy logic controller. The performance of resulting optimized system is compared with different systems based on various control algorithms (including passive system). The models are compared for attributes of road handling and ride comfort. The results clearly manifest supremacy of fuzzy logic based particle swarm optimized semi-active suspension system in relation to all other systems. The research presents an innovative approach of utilizing relative displacement and relative velocity as the input parameters resulting in no overshoot of the suspension displacement.