Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach

This study delves into the optimization of PID control parameters for Multi-model Adaptive High-speed Rail Platform Door Control Systems (MMAHSR-PDCS) using the Individual-Based Model Dynamic Multi-Swarm Snow Goose Algorithm (IBM-Dy-SGA). The IBM-Dy-SGA effectively balances exploration and exploitation through a dynamic multi-swarm mechanism, combined with Latin Hypercube Sampling (LHS) initialization and K-means clustering strategies, thereby enhancing population diversity and convergence efficiency. Experimental validation demonstrates that, compared to the Snow Goose Algorithm (SGA), the IBM-Dy-SGA achieves a 10%–30% reduction in average fitness values and a 20% improvement in convergence speed. The IBM-Dy-SGA was further compared with seven other state-of-the-art algorithms on the CEC2017 benchmark test suite in both 10D and 30D, showcasing its significant advantages and validating these results through non-parametric tests. In the context of application for the control of MMAHSR-IPDCS, the IBM-Dy-SGA outperforms traditional methods in optimizing PID parameters, thereby significantly enhancing the precision and stability of platform door control systems. Specifically, the IBM-Dy-SGA reduces overshoot to 0% and shortens the settling time to 1.05 s, representing a 66.7% improvement over traditional methods. The practical application of the IBM-Dy-SGA at Zigong Station successfully resolved platform door alignment issues under varying train models and parking error conditions, demonstrating its effectiveness and reliability in complex and dynamic high-speed rail operations. For instance, in tests involving CRH380A, CRH380AL, CRH380AN, and CRH380D train models, the alignment rate of platform doors with train doors reached 100%, while for the CRH3A model, the alignment rate was 94.36%. This study not only enriches the theoretical application of metaheuristic algorithms in control systems but also provides a practical optimization method for high-speed rail platform door control, which is of great significance for enhancing the safety, efficiency, and reliability of high-speed rail operations.