Tuning a fuzzy controller by particle swarm optimization for an active suspension system

Abstract

Active suspension systems are crucial to improve vehicle performance and passenger comfort. Fuzzy logic control can cope with suspension system nonlinearities using heuristic knowledge rules, but tuning the control parameters is not straightforward. In this paper, we propose tuning fuzzy scaling factors for active suspension control by particle swarm optimization (PSO). PSO performs an off-line stochastic global search for input and output scaling factors of a PD-type fuzzy controller. The objective function of the PSO algorithm has been defined to minimize sprung mass acceleration, and is evaluated by simulating the transient response to road perturbations. The paper offers a case study based on the quarter car suspension model. Results offer a good performance of chassis acceleration against bumps and road roughness.