An Optimized Active Compensation Control Framework for High-Speed Railway Pantograph via Imitation-Guided Deep Reinforcement Learning

Extreme pantograph–catenary contact force (PCCF) oscillations pose a serious challenge to the stable coupling between pantograph and catenary in high-speed railway systems. This paper introduces an active compensation control framework CPO-LQR-BC-SAC, which combines optimized Linear Quadratic Regulator (LQR) baseline control with behavior cloning (BC) and Soft Actor-Critic (SAC) deep reinforcement learning. First, the Crowned Porcupine Optimization algorithm (CPO) is used to offline tune the LQR weighting matrix, producing a high-performance CPO-LQR controller that significantly reduces PCCF fluctuation. Next, a dual model-based offline control law provides “expert” adjustments that further suppress extreme contact force values. Observing that superimposing these offline-tuned actions onto real-time CPO-LQR outputs yields further suppression gains, we developed the BC-SAC compensatory controller to provide corrective control actions. In this scheme, expert actions guide the SAC policy via a behavior cloning loss term in its loss function, and a decaying imitation weight ensures a balance between imitation and exploration. Simulation results demonstrate that, compared to both CPO-LQR and the idealized offline control law, the proposed CPO-LQR-BC-SAC framework achieves over 77% reduction in PCCF standard deviation and exhibits the ability to generalize across different pantograph types, confirming its effectiveness and robustness as a practical solution for mitigating extreme PCCF oscillations.