Particle Swarm Optimization applied on Fuzzy Control: Comparative analysis for an Quarter-car Active Suspension Model

Active suspension systems are crucial to improve vehicle performance and passenger comfort. Fuzzy logic control performs excellently against the nonlinearities of the suspension system by means of heuristic knowledge rules, nevertheless, the adjustment of the control parameters is not straightforward. In this paper, we propose the adjustment of fuzzy scaling factors for active suspension control by particle swarm optimization (PSO). PSO makes a global stochastic search of the input and output scaling factors of a PD-type fuzzy controller. The objective function of the PSO algorithm has been defined to minimize the acceleration of the suspended mass, and is evaluated by simulating the transient response to road perturbations. The article provides a case study based on a quarter car suspension model. The results show good performance of chassis acceleration against bumps and road roughness.