渠道运行神经网络控制

针对渠道运行的不确定性和非线性，在渠道自动控制系统的观测器回路上并联一个BP神经网络，通过实测水位的学习，修正控制系统数学模型的不准确性；在控制器增益回路上并联一个BP神经网络，补偿控制增益的不精确。与传统的线性二次调节(LQR)最优控制相比，渠道运行控制过渡过程更为平稳，达到稳定的时间缩短，闸门运行的振动和超调大为改善。

Neural control of channel operation

Aiming at the uncertainty and the nonlinear during the channel operation, a BP neural network of parallel connection on the state observer loop of channel automatic control system, through learning from the water level of actual measurement, corrects mathematics model for channel operation control system ,and a BP neural network of parallel connection on controller gain loop compensates the control gain, with this kind of method to solve the nonlinear and uncertainty problems in the channel operation control system. Comparing with linear quadratic regulation(LQR) control, the transition process is more smooth, and the time to reach steady is shortened, the vibration of gate operation is mitigated, and the excessive regulation is reduced considerably.