Adaptive Fractional-Order PID Controller Design and Parameter Tuning for Vehicle Longitudinal Control

To enhance traffic efficiency and ensure passenger comfort, an Adaptive Fractional-Order PID (AFOPID) controller is proposed for longitudinal control in automated vehicles. Initially, the traditional PID controller is improved by incorporating fractional-order calculus, enhancing the overall control performance. The gain parameters of the fractional-order PID controller are updated using the gradient descent method according to sliding mode surfaces to further enhance the robustness of the control system. To ensure accurate tuning of controller parameters, a hybrid strategy is employed to improve the Grey Wolf Optimizer (GWO). Simulation results indicate that the hybrid strategy-enhanced grey wolf optimizer exhibits faster convergence in the early stages and superior global search capabilities. Additionally, the proposed AFOPID controller exhibits reduced control output fluctuations and achieves higher average speeds, and more gentle speed changes in car-following scenarios. Multi-vehicle platoon experiments further confirm the controller's robust queue stability.