Adaptive Fuzzy Dual PID Controller Optimized Genetic Algorithm for Enhanced Vehicle Suspension Performance

Abstract

This paper presents a method for improving the performance of a vehicle suspension system using an adaptive fuzzy dual PID controller optimized with a genetic algorithm. The fuzzy dual PID controller utilizes fuzzy logic to adapt to changing conditions and improve control, while the genetic algorithm optimizes the controller parameters to further enhance performance. The study uses velocity and position PID controllers because velocity PID controls acceleration well and position PID controls position well, and the incorporation of an adaptive fuzzy combination of two controllers ensures optimal performance of the suspension system under all operating conditions. To avoid the issue of suspension distance narrowing and to prevent instability in the controller, the low-pass filtered displacement response of unsprung mass is utilized as the reference for the position PID controller. Quantitatively, according to the ISO-8608 road entry for the goal function, the dual PID achieved a 53.35% improvement over the passive state, 6.57% better than dual PD, 33.06% over the Velocity PID, and 32.93% over the Position PID. These significant, quantifiable results confirm that the proposed adaptive fuzzy dual PID structure offers a robust and highly effective solution for advancing active vehicle suspension control technology.