Optimal Control of Active Quarter-Car Suspensions Using PSO/TLBO-based Optimization and LQR/LQG Strategies under ISO Road Excitations

This paper presents a systematic comparison of PID, LQR, LQG and SMC controllers for active quarter-car suspension, focusing on balancing ride comfort, handling stability, and control effort. PID gains and LQR weighting matrices are optimized using Particle Swarm Optimization (PSO) and Teaching-Learning-Based Optimization (TLBO) algorithms. Performance is evaluated on half-sine bump and ISO 8608 random road profiles, with key metrics including RMS body acceleration, suspension and tire defections, actuator effort, and transient response features such as overshoot and settling time. Statistical analysis over 20 randomized runs reveals that LQR controllers tuned via TLBO and PSO maintain ride comfort comparable to passive suspension while significantly reducing suspension defection and control effort. PID controllers achieve moderate improvements but incur higher actuator effort and suspension travel. LQG offers minor gains over passive suspension but does not outperform optimized LQR controllers. These findings provide practical guidance for designing active suspension controllers that efficiently trade of comfort, stability, and actuator usage in realistic driving conditions.