| Generalized Predictive Control (GPC), a new type of computer control method birthing in 1980's, is one of the most representative algorithms. It is researched actively and has received increasing attention in the field of control and industry. This paper studies the fundamental principles of predictive control and the new development of GPC. The calculation of control law in generalized predictive control needs to solve Diophantine equations and inverse matrix. Because of the mass calculation the applications in high rapid and strong timely system are limited. According to these problems and on the basis of the massive domestics and foreign literatures, several fast generalized predictive control algorithms are proposed and some simulation studies are carried out for the typical industrial models. We study the algorithm of fast generalized predictive control. The main research results have been shown below:Firstly, combining with neural network and fuzzy control, we summarize all kinds of fast algorithms of GPC on the basis of the basic algorithm, and we also introduce GPC's characteristic and how to choose parameter.Secondly, for the mass calculation of generalized predictive control algorithm, an improved generalized predictive control algorithm is proposed. It introduces constraint matrix into basic generalized predictive control algorithm which change the matrix inversion part into scalar form in order to get the solution, thus reduce the mass computation greatly. We introduce the Elman neural network into this algorithm as well as in order to use this algorithm for the nonlinear system.Finally, Based on strong nonlinear and multi-variable coupling features of chaos system, we use recursive least squares with forgotten factor to identify parameters and get chaos system model. Adding the soft coefficient to input signal, we can reduce calculation, achieve faster system response and get GPC algorithm used in chaos system with better application results. |
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