A Differential Evolution-Enhanced Computational Optimization Framework for PID Tuning of Vehicle Cruise Control Systems Under Disturbance and Uncertainty

Simulation-based controller tuning for dynamic engineering systems requires computational optimization methods capable of maintaining search stability, solution consistency, and robust performance under uncertain operating conditions. In vehicle cruise control systems, accurate tuning of proportional–integral–derivative (PID) controllers remains challenging due to nonlinear dynamics, parameter variations, external disturbances, and changing operating conditions. Although metaheuristic optimization methods have been widely used for PID parameter tuning, their performance may be limited by an imbalanced exploration–exploitation trade-off, which can lead to premature convergence and inconsistent solution quality. To address these issues, this study proposes a hybrid centered collision optimizer with differential evolution, termed h-CCODE. The proposed framework incorporates differential evolution-based mutation and crossover mechanisms into the centered collision optimizer to enhance population diversity, convergence stability, and solution refinement while preserving the original collision-based search dynamics. The computational performance of h-CCODE is first evaluated using the CEC2022 benchmark suite, where the proposed method demonstrates improved convergence characteristics and competitive solution quality compared with the original optimizer and with four further metaheuristics. The method is then applied to simulation-based PID tuning of a vehicle cruise control system. Comparative results show that the h-CCODE-tuned PID controller provides a balanced transient response with reduced settling time and overshoot while maintaining a comparable rise time. Disturbance rejection, reference tracking, frequency-domain, and parameter uncertainty analyses further confirm the stable and consistent performance of the proposed control framework under non-ideal operating conditions. Overall, the results indicate that h-CCODE provides an effective computational optimization approach for simulation-based controller tuning in dynamic engineering systems.