Research On Application Of Neural Network In Nonlinear Predictive Control

Based on the study status of model predictive control and the problem on application neural network in nonlinear predictive control, some problems are studied and discussed in the article, also the corresponding results are given. The main contents are as follows:1. Based on the basic structure and theory of dynamic matrix predictive control, the predictive model, the method of revising feedback, receding horizon optimization and the influence on performance of controlling of the predictive horizon P, control horizon M and others parameters is analyzed in detail.2. To improve the rate of convergence of the BP neural networks for nonlinear system identification in nonlinear predictive control, a novel parallel quasi-Newton optimization Technique is proposed and as multi-step predictive model of nonlinear industrialized process. The simulation results confirm the algorithm is able to increase the precision of nonlinear predictive model greatly and improve the rate of rate of convergence of neural network. Moreover, a novel recurrent neural network - Elman be given for dynamic nonlinear system predictive model.3. To improve the revising feedback performances, a fuzzy self-compensating correction method for nonlinear multi-step prediction is presented. The simulation results showing the effectiveness fend robustness of the algorithm in nonlinear predictive control and dynamic of the controlled system.4. Based on the analysis present receding horizon optimization methods for neural network predictive model, a neural network adaptive PI controller is proposed. The parameters of the PI controller are tuned by the error of predictive output and the real system output. The simulation results prove the method is effective in adaptation and tracking of the different process operating.