The Neural Network Predictive Control

Along with advancement of industrial control demand, development of control theory and computer technology, a predictive control algorithm is produced with effective control and strong robustness, It is applicable to complex industrial processes and the control system that is not easily to establish the accurate mathematics model, and is successful applied in petroleum, chemical industry, metallurgy and mechanism, and have a good prospect in application.In this paper, it makes a discussion on the basic structure and theory of dynamic matrix predictive control, makes a detailed analysis including its predictive model, its methods of revising feedback and receding horizon optimization, its structure of inside model control (IMC) and its stability & rebustness. Simulation results confirm advanced of the dynamic matrix control algorithm. On the basis of pointing out the problem of the present difficulty and actuality, we propose the idea that DMC combined by neural networks. Actually it uses the neural networks as the identified model of control plant to produce predictive signal, the control law is solved by optimized algorithm. Accordingly we realize the predictive control of the nonlinear and time-variety system. We choose BP and RBF neural networks as identified model for they can approach the function very well. First we identify the controlled plant offline, when the precision reaches a certain extent, we will achieve recursive predictive model by on-line identification. Finally we acquire the optimized control law by minimizing the function of performance guideline. This algorithm not only solves the problem that nonlinear and time-variety plant is difficulty to build model, but also decreases the controller calculative burden. It benefits its application to real-time system. Therefore, its application scope of predictive control is further broadened. In the end, on the basis of introducing technical flow in hydrocracking units, two predictive models of jet fuel endpoint in hydrocracking units are built based on BPNN and RBFN, a new DMC project using the neural networks asthe identified model is proposed, and it provides good conditions foronline quality control of jet fuel endpoint.