Self-tuning of PID Control Parameters for the ATO System Based on the Improved Golden Jackal Optimization Algorithm

To improve the speed tracking accuracy of trains in the Automatic Train Operation (ATO) system, this paper proposes an Improved Golden Jackal Optimization algorithm (IGJO) and applies it to the self-tuning of the train speed PID controller. The algorithm enhances its optimization capability and population diversity by optimizing the initial population through chaotic mapping, precomputing Levy steps, adopting a fast boundary reflection mechanism, and designing a dynamic search strategy based on energy and diversity. In the experiments, IGJO is compared with the other three algorithms. The results show that the PID controller tuned by IGJO achieves the optimal performance in precise tracking of train speed and exhibits the best control effect on the regulation fluctuations of the power system during train operating condition transitions. This study verifies the effectiveness of the improvements made to IGJO and its excellent control performance in the train speed control system.