Metaheuristic Optimization of Fractional-Order PID Controllers for Vehicle Cruise Control Systems
Abstract
In recent years, the automotive industry has increasingly focused on developing advanced cruise control systems (CCS) to enhance vehicle safety, comfort, and efficiency. However, the dynamic and nonlinear nature of vehicle motion—affected by factors such as road slope, aerodynamic drag, and load variations—makes it difficult for conventional controllers to maintain stable performance under varying conditions. Traditional Proportional–Integral–Derivative (PID) controllers, despite their simplicity and popularity, often exhibit limited robustness and adaptability when dealing with such nonlinearities. To overcome these limitations, this study investigates the implementation of a Fractional-Order PID (FOPID) controller, which generalizes the classical PID by introducing fractional differentiation and integration orders. These additional parameters increase the controller’s flexibility and allow for more accurate tuning of system dynamics. The fractional operators are implemented using Oustaloup Recursive Approximation and Continued Fraction Expansion (CFE) techniques to achieve precise realization in the frequency domain. A comparative analysis is conducted between classical PID and FOPID controllers, where the parameters of each are optimized using the Genetic Algorithm (GA). Simulation results, based on a linearized Vehicle Cruise Control System (VCCS) model, show that the FOPID controller achieves significant performance improvements—particularly in reducing maximum overshoot—while demonstrating enhanced robustness and adaptability to variations in vehicle parameters and environmental conditions.