PSO-Fuzzy Gain Scheduling of PID Controllers for a Nonlinear Half-Vehicle Suspension System

The present article addresses the gain scheduling of proportional-integral-differential (PID) controllers using fuzzy set theory coupled with a metaheuristic optimization technique to control the vehicle nonlinear suspension system. The nonlinearities of the vehicle suspension system are due to the asymmetric piecewise dampers, quadratic tire stiffness, and the cubical spring stiffness. Conventional PID controller suffers from the low performance subject to modeling nonlinearities, while fuzzy logic controller (FLC), as a universal approximator, has the capacity to deal with the nonlinear, stochastic, and complex models. However, finding the optimal Mamdani FLC rules is still a challenging task in addition to a proper architecture of the membership functions (MFs). As a remedy to this drawback, particle swarm optimization (PSO) technique is employed in this article to improve the efficiency of the FLC-based PID controllers. The proposed nonlinear controller is suggestive of the decreased overshoot and reduced settling time for the heave and pitch motions of the half-vehicle model. The satisfactory performance of the controller, when tires are subject to random excitations, to reduce the peak magnitude is observable in a relatively less computational time.