Research Of Predictive Control For Thermal Process

The paper is committed to the research of nonlinear predictive control for thermal process . The main contributions are as follows:1.In view of modeling problems of nonlinear and dynamic system ,a 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 convergency rate , accuracy and good stability. It can be conveniently applied to engineering practice.2. The characteristics of main stream temperature and reheated temperature plants are introduced. Then self-organizing fuzzy identification algorithm via the fuzzy T-S model is applied to modeling of main stream temperature and reheated temperature plants. 3.In this paper, a possibility of using Linear MBPC to control nonlinear systems is investigated. Takagi-Sugeno fuzzy models are chosen as the model structure. Local linear models can be derived from the linear rule consequents in a straight-forward way . Each sample time a local linear model is calculated and used to calculate the next incremental control action using linear Generalized Predictive Control(GPC). With these algorithms above, a simulation test has been taken to the main steam temperature 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 the nonlinear main stream temperature can be controlled successfully using predictive algorithm.