Predictive Control of Multivariable Nonlinear System Based on Multilayer Local Recurrent Neural Networks
基于多层局部回归神经网络的多变量非线性系统预测控制

Taking the stirred tank reactor for example, the predictive control of MIMO nonlinear system based on \{multilayer\} local recurrent neural networks is presented. Aiming at the difficulties in modeling the complex MIMO nonlinear system, the multilayer local recurrent neural network is used to build the predictive model of the process off line. In feedback correction, considering the requirements of the accuracy and practicability, error compensation and model correction are adopted to correct the predictive model online for the predictive control. We draw the conclusion that negative exponential weighting of future tracking errors can improve the control performance of the control systems. The results of simulation show the effectiveness of the control algorithm.