Research & Application Of Fuzzy Generalized Predictive Control Based On T-S Model

With the development of technology and productivity, the systems of the industrial process control become more complex due to the lack of precise, formal knowledge about system, strongly nonlinear behavior, the high degree of uncertainty, time varying characteristics, close coupled and high dimensional system, etc. Owing to the lack of precise mathematical model, it is difficult, even impossible, to control such complex systems. From the point view of modeling and control, we discuss the application of Fuzzy Predictive control based on T-S model in Sugar Boiler System in this paper. The main contents are concluded as followings:1. In view of modeling problems of nonlinear and dynamic system, self-organizing fuzzy identification algorithm (SOFIA) is presented based on T-S model in this paper. The procedure for finding the optimal identification is simplified, and both the premise and consequent parameters are identified simultaneously by using the SOFIA. Because of reduction of computational requirement for identifying a Takagi-Sugeno (T-S) fuzzy model by efficient parameter and structure identification, this algorithm can be used in on-line modeling. A lot of simulation results show that the SOFIA has the high convergence rate, accuracy and good stability. It can be conveniently applied to engineering practice.2. Based on the introduction of the principles of Takagi-Sugeno (T-S) fuzzy model and generalized predictive control (GPC) algorithm, the fuzzy predictive control method combining GPC and T-S model is classified as three kinds of algorithms. The design method of these algorithms is presented in detail. A comparison of these FGPC strategies in control performance and complexity of computation is livening by simulation. 3. In this paper, Takagi-Sugeno fuzzy models are chosen as the model structure. With these algorithms above, a simulation test has been taken to the water height control of boiler by means of Generalized Predictive Control using Takagi-Sugeno fuzzy models. The test shows that T-S model of the nonlinear system can be successfully identified on line and can be controlled successfully using predictive algorithm.