为了应对受电弓与接触网系统在列车高速运行情况下剧烈振动的问题，提高弓网的受流质量，本文提出了一种基于模型预测控制(Model Predictive Control，MPC)和扰动前馈补偿的复合控制器来降低弓网接触力波动。首先在线性化弓网系统的基础上设计了基于粒子群优化(Particle Swarm Optimization，PSO)的模型预测控制器，实现对弓网接触力的波动优化;其次构造了广义扩张状态观测器(Generalized Extended State Observer，GESO)，对于弓网系统中不可测量的状态进行估计，并对模型不确定性问题进行前馈补偿;最后，仿真验证所设计的控制器能够在考虑模型不确定性的条件下有效地降低接触力的波动。
Predictive Control of Pantograph-Catenary Based on Particle Swarm Optimization
In order to deal with the problem of severe vibration of pantograph and catenary system under high-speed train operation and improve the current collection quality of pantograph-catenary,a composite controller based on model predictive control(MPC)and disturbance feedforward compensation is pro-posed to reduce the fluctuation of pantograph-catenary contact force.Firstly,based on the linearized pantograph-catenary system,a model predictive controller based on particle swarm optimization(PSO)is designed to optimize the fluctuation of pantograph-catenary contact force.Secondly,a generalized ex-tended state observer(GESO)is constructed to estimate the unmeasurable state in the pantograph-cate-nary system and compensate the model uncertainty.Finally,the simulation verifies that the designed controller can effectively reduce the fluctuation of the contact force under the condition of considering the model uncertainty.
pantograph active controlmodel predictive controlstate estimationfeedforward compensationparticle swarm optimization
万方数据
维普
