Dynamic Performance of the PID Controller with Flower Pollination Algorithm for Magnetorheological Suspension System

The investigation into Magnetorheological (MR) damper control for semi-active systems has attracted interest in enhancing vehicle ride comfort and stability. Previous researchers have explored various control methods for semi-active MR dampers. Still, the findings show that the designed control schemes are very limited in providing good performance and may lead to unpredictable optimal force targets. Consequently, this study explores the effectiveness of an intelligent optimization technique known as the flower pollination algorithm (FPA) in determining the parameters for a proportional-integral-derivative (PID) controller for semi-active suspension systems. The study assesses the performance of the PID controller with FPA tuning. It compares it to traditional and intelligent optimization methods, including a non-PID heuristic approach and particle swarm optimization (PSO). To conduct these assessments, a MATLAB simulation environment was employed to create a comprehensive model of the semi-active suspension system, complete with all control components. The results of the study demonstrate a significant improvement, with the proposed PID-FPA controller reducing the amplitude of body acceleration and displacement responses by up to 70.4 % and 84.5 %, respectively, when compared to PID-HEURISTIC, PID-PSO, and passive systems. The FPA optimization integrated with the PID controller should be considered to achieve the optimum target in the MR damper control strategy for the suspension system.