Red Panda Optimization Algorithm-Based PID Controller Design for Automobile Cruise Control System

The integration of cruise control into vehicles plays a crucial role in the domain of advanced driver assistance systems. Functionally, cruise control relies on a Proportional-Integral-Derivative (PID) controller to maintain the vehicle's velocity at a predetermined setpoint. Traditional parameter tuning of the PID controller often results in oscillatory output, prolonged rise time, and extended settling duration. To address these issues, we introduce the Red Panda Optimization (RPO) algorithm to optimize the PID controller parameters, thereby enhancing both the accuracy and stability of the vehicle's cruise control mechanism. By accounting for the inherent nonlinearities and dynamic characteristics of the system, the RPO algorithm determines the controller gain while simultaneously identifying the optimal value through an evaluation of the integral time square error. To validate and verify the performance of our proposed system, we conducted simulations using a Bode plot within the MATLAB R2023a framework. The implemented algorithm for the adaptive cruise control system demonstrates improved responsiveness and heightened system stability, as evidenced by reduced rise time, minimized latency, and diminished overshoot.