基于多智能体近端策略优化算法的电力机车受电弓随网压力研究OA

The pantograph-catenary system,as the key interface for traction power transmission in electric locomotives,determines current-collection quality and operational reliability through its coordination performance.At high speeds,the sliding of pantograph along the contact wire excites coupled vibrations in the pantograph-catenary system,undermining contact stability and reducing current-collection efficiency.Regulating the wire-following contact force(WFCF)is an effective remedy.A multi-agent proximal policy optimization(MAPPO)method augmented with a long short-term memory(LSTM)network is proposed to achieve dynamic WFCF control.Within a centralized-training,decentralized-execution(CTDE)framework,multiple agents are trained to learn speed-specific control policies,enabling precise force regulation across operating conditions.Experimental results show that the proposed approach substantially suppresses WFCF fluctuations and enhances contact wire-following performance(CWFP).