Design of Fuzzy PID Controller with Big Bang Big Crunch Optimization Algorithm and Application to Quarter Car Suspension System

This study focuses on enhancing passenger comfort in vehicles through the design and application of an advanced active suspension control system. The primary objective was to develop a direct-action type fuzzy Proportional-Integral-Derivative (PID) controller for a quarter-car suspension model and optimize its performance using the Big Bang Big Crunch (BBBC) algorithm. The methodology includes mathematical modeling of the quarter car suspension system and comprehensive tuning of all parameters of the fuzzy PID controller, including error and error derivative scaling factors, fuzzy logic membership function parameters, and proportional, integral, and derivative gains, using the BBBC optimization technique. The system’s performance was evaluated through simulations using five distinct road disturbance profiles (single bump, double bump, sinusoidal, random, and combined single bump with random road) and assessed based on Integral Square Error (ISE) and Integral Absolute Error (IAE) criteria. The simulation results consistently demonstrated that the proposed BBBC-optimized fuzzy PID controller performed better than the passive suspension system and a conventionally tuned PID controller across all tested road profiles, achieving lower ISE and IAE values indicative of superior vibration damping. The findings conclude that the BBBC optimization approach provides an effective method for tuning fuzzy PID controllers in active suspension applications, yielding improvements in ride comfort compared to traditional methods.