Design of Speed-regulation System of PMLSM Based on BP Neural Network PID Control
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摘要: 针对永磁直线同步电动机提升系统的非线性、时变性、易受扰动等特性，在所建立的永磁直线同步电动机d-q轴动态数学模型的基础上，设计了一种改进型BP神经网络PID控制的PMLSM调速系统。该系统将BP神经网络算法中固定的学习速率改为自适应可调，同时添加动量项以减小学习过程中的振荡趋势，极大地改善了算法的收敛速度，避免了网络落入局部最小值的结果。仿真结果表明，使用改进的BP神经网络PID控制器可使PMLSM调速系统的调节时间和超调量大幅减小，响应速度加快，使提升系统具备较好的动态性能和较强的鲁棒性。
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关键词:
- 永磁直线同步电动机 /
- 动态数学模型 /
- BP神经网络PID控制 /
- 调速系统 /
- 速度控制器
Abstract: For characteristics of nonlinearity, time-varying volatility and nonstationarity to disturbance of hoisting system of PMLSM, an improved PMLSM speed-regulation system was designed on basis of established PMLSM d-q axis dynamic mathematical model and BP neural network PID control. The system modifies the fixed learning rate in BP neural network to a self-adaptive one, and adds momentum to reduce oscillation tendency in learning process, so it greatly improves convergence speed and avoid that network falls into a local minimum. The simulation result showed that using the improved BP neural network PID controller can make PMLSM speed-regulation system reduce adjusting time and overshoots greatly and accelerat response speed, and make the hoist system have better dynamic performance and robustness. -
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