Comparative Performance Analysis of LQR Based PSO and Fuzzy Logic Control for Active Car Suspension

This study proposes a diffrent control strategy for active car suspension systems, comparing the performance of Proportional-Integral-Derivative (PID), Linear Quadratic Regulator (LQR), and fuzzy PD controller in optimizing ride comfort and handling. These methods were selected for their complementary strengths: PID for simplicity and industrial adoption, LQR for optimality in handling trade-offs between ride comfort and suspension travel, and fuzzy PD for adaptability to nonlinearities and road disturbances. A 4-DOF quarter-car model is employed to simulate vehicle dynamics, with road disturbances modeled as step and sinusoidal inputs. The PID controller is tuned using built-in tools such as the PID tuner app, while the LQR’s weighting matrices (Q and R) were optimized offline using PSO. The optimized weights were then substituted into the algebraic Riccati equation to derive the final feedback control gains, ensuring optimal performance while adhering to classical LQR theory. For the fuzzy PD controller, membership functions and rule bases are designed to adaptively adjust gains under varying road conditions. Simulation results demonstrate that the PSO-tuned LQR and fuzzy PD controllers outperform conventional PID by reducing body vertical displacement by 61% and 23%, respectively, and overshoot by 75% (fuzzy PD) and 60.2% (LQR) under step excitation. The LQR controller based PSO also shows superior adaptability to stochastic road inputs and minimizing the control signal by 83.3% compared to PID. By integrating PSO-based LQR gain optimization and adaptive fuzzy logic, this work advances active suspension control, offering a quantifiably superior alternative to classical approaches. This study contributes to the technological development of the automotive world in order to provide comfort and safety for the passenger under different conditions, which contributes to the design of more comfortable vehicles with better performance in the future.