The Study And Application Of GPC Based On The BP Neural Network

Along with advancement of industrial control demand and 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 systems that are not easily to establish the accurate mathematics model. The algorithm is successful applied in petroleum, chemical industry, metallurgy and mechanism, so it has a good prospect in application.In this paper, it makes a discussion on the basic structure and theory of generalized predictive control, makes a detailed analysis including its predictive model, its methods of feedback adjustment and rolling optimization. Simulation results show the effectiveness of the generalized predictive control algorithm. On the basis of pointing out the problem of the present difficulty and actuality, we propose the idea that GPC combined by neural networks. Actually it solves the control law by BP neural networks for it can approach the function very well. This new algorithm simplifies the generalized predictive control and can be used to control the fast system. At last we choose the single inverted pendulum as the research object and apply the new algorithm to the control of single inverted pendulum. The results show that the performance of the algorithm is satisfied.