基于BP神经网络的矿井提升机自校正容错PID控制

Self-tuning fault-tolerant PID control for mine hoist based on BP neural network

针对矿井提升机系统故障时动态性能难以用传统的解析方法获得的问题,提出了一种基于BP神经网络的矿井提升机自校正容错PID控制方法。该方法通过BP神经网络在线学习跟踪提升机系统的动态特性来预测系统输出值,并应用自适应控制中的自校正PID构建容错控制器,实现提升机系统故障下的稳定容错控制。仿真结果表明,该方法在提升机系统故障情况下能迅速跟踪系统故障状态,在线调整PID参数,快速恢复系统性能。

In view of problem that traditional analysis method is difficult to obtain dynamic feature when mine hoist system failures, the paper proposed a self-tuning fault-tolerant PID control method for mine hoist based on BP neural network. The method employs BP neural network to predict system output value by learning and tracking dynamic features of the hoist system online, uses self-tuning neural network PID of self-adapt control to build fault-tolerant controller, so as to realize stable fault-tolerant control of the hoist system when fault happens. The simulation result shows that control method can track system fault state rapidly when a sudden fault happens, and adjust PID parameters online and restore system features in short time.