电弧炉电极系统双模控制策略研究及应用

针对交流电弧炉电极控制系统具有非线性时变、多变量、强耦合及存在随机干扰的特点，采用基于最近邻聚类方法的径向基函数(RBF)神经网络快速学习算法，通过实时在线辨识，建立电弧炉电极系统的精确逆模型并用于控制，实现了将具有强耦合特性的多变量输入/输出(MIMO)系统解耦成单个独立的伪线性对象，并提出一种基于RBF神经网络逆控制与比例微分(P/D)控制相结合的双模控制策略。应用结果证实了该控制策略具有快速适应对象和过程变化的能力及较强的鲁棒性。
关 键 词 电弧炉; 解耦; 双模控制; 电极系统; 逆控制; 神经网络

Abstract An exact inverse model controller is constructed, which in accordance with the characteristics of the electrode control system in arc furnace. The arc furnace model can be identified on-line. The nonlinear multiple-input-multiple-output (MIMO) plant is converted into isolated dynamic decoupling pseudo linear system based on radial basis function (RBF) neural network that applies nearest neighbor clustering algorithm. A dual-mode control strategy is presented, which is based on the proposed RBF neural network inverse controller and a proportion differential (PD) controller. Result shows that the controller has rapid compliant ability of process changing and good robust performance in practical applications.
Key words arc furnace; decoupling; dual-mode control; electrode system; inverse control; neural network