两电机变频系统神经网络广义逆内模控制

Internal Model Control for Two-Motor Variable Frequency System Based on Neural Network Generalized Inverse

为了提高非线性强耦合的两电机变频调速系统的解耦控制性能和鲁棒性能,提出了基于神经网络广义逆系统的二自由度内模控制方法。先对原系统数学模型进行广义逆存在性分析,进而推导出原系统的广义逆数学模型,再用动态神经网络逼近广义逆模型,从而串接在原系统之前组成广义伪线性复合系统,实现系统的解耦线性化与开环稳定,有利于系统的综合。然后对广义伪线性系统引入二自由度内模控制,保证系统的鲁棒稳定性。最后基于S7-300的平台,做了相关的试验研究。结果表明,该方法不但能够很好地实现系统的解耦,而且当系统存在建模误差和负载扰动的情况时,仍能使系统保持高性能的控制。

To improve the decoupling control ability and robustness of nonlinear high coupling two-motor variable frequency speed-regu- lating systems （TVFSS） , the two-degree-of-freedom internal model control （2-DOF IMC ）is proposed based on neural network generalized inverse （NNGI）. On the basis of reversibility analysis of the original system, the generalized inverse model approximated by the dynamical neural network is cascaded with the original system. Based on the idea of NNGI, the decoupling linearization and open-loop stability of system are reached. The robust stability is improved by introducing 2-DOF IMC to generalized pseudo-linear system. The results of experimental researches based on ST-300 PLC show that the decoupling control of the system can be realized successfully and the high control performance can be ensured when the system has inverse modeling errors and changeable load.