自动染料配浆控制算法研究

Research on Control Algorithm of Automatic Dye Blending

在配浆控制过程中,是一个多干扰时变的系统,且有很强的非线性,存在一定的滞后,常规的自动控制算法不能满足快速,精确的调整目标。根据调浆的过程,提出了一套基于对参考轨迹寻优的ESO-GPC算法,通过扩张状态观测器(ESO)对非线性系统的动态补偿线性化,使广义预测控制的计算量大大降低,对干扰提前进行补偿,提高了系统的响应速度和抗干扰能力。基于神经网络对GPC(广义预测控制)的参考轨迹在线寻优调整,进一步提高系统的鲁棒性,优化控制轨迹。

In the process of slurry mixing control,it is a multi-interference and time-varying system with strong nonlinearity and a certain lag.Conventional automatic control algorithms cannot meet the fast and accurate adjustment goals.According to the process of tuning,a set of ESO-GPC algorithm based on optimizing the reference trajectory is proposed.The dynamic compensation linearization of the nonlinear system through the extended state observer(ESO)greatly can reduce the calculation amount of generalized predictive control.The interference is compensated in advance,which improves the response speed and anti-interference ability of the system.Based on the reference trajectory of BP neural network to Generalized Predictive Control(GPC),the online optimization and adjustment are carried out,which further improves the robustness of the system and optimize the control trajectory.