基于BP神经网络PID的改进偏差耦合同步控制

Improved Deviation Coupling Control Based on BP Neuron Network PID

针对多电机同步控制系统控制精度不高的现状,对多电机同步控制系统进行研究。在对多电机同步控制算法进行分析的基础上,设计了具有自学习和自适应能力的BP神经网络PID控制器,以弥补传统PID控制器在控制过程中的不足。采用偏差耦合控制,在传统速度补偿器速度偏差的基础上乘以速度反馈耦合增益,再引入一个包含各台电机速度信息的指标增强各台电机之间的耦合性,并与BP神经网络PID控制器相结合。在Matlab/Simulink环境下,搭建了多电机同步控制系统仿真模型,仿真结果表明基于BP神经网络PID的改进偏差耦合同步控制系统同步控制精度高、收敛速度快、稳定性能好,能够很好的实现多电机的同步控制。

The multi-motor synchronous control system was analyzed for the status of control accuracy of multi-motor synchronous control system in the paper. Through a comprehensive analysis of multi-motor synchronous control system, BP neuron network PID controller with self-learning and self-adaptive was designed, which made up the deficiency of traditional PID controller. Using deviation coupling control strategy , traditional speed compensator＇s speed devotion was multiplied by the speed feedback gain and reintroducing an index with the imformation of each motor＇s speed to enhance couping of each motor and then combined with BP neuron network PID controller. With the use of Matlab/Simulink, the multi-motor synchronous con- trol system simulation model can be established, the simulation result shows that improved deviation eouping control based on BP neuron network PID controller system has high precious of synchronization, fast convergence and stable performance. The purpose that improved the control accuracy of multi-motor synchronous control system can be achieved.