Constraints Based On Ts Fuzzy Model Predictive Control

Research on linear predictive control has become mature and linear MPC has gained wide applications in industrial processes. However, mostprocesses in industry are nonlinear, time-variant and bear uncertainty,for a highly nonlinear system, it may not give rise to satisfactory dynamic performance. The T-S fuzzy model can approaching any nonlinear systems, and its structure is simple. So the controlled plant can be expressed as a linear model, then the GPC controller can designed for it. In this dissertation, the development about predictive control and the T-S fuzzy system's strongpoints and deficiencies are introduced firstly. The basic identification steps of Takagi-Sugeno fuzzy model are presented in detail. And then some new algorithms of constrained fuzzy generalized predictive control are presented for nonlinear systems. The simulation results show their superior performance for nonlinear systems. In conclusion the main contents are as follows:1) one kind of quickly constrained generalized predictive control algorithm is presented based on the T-S fuzzy model which is used to approach the SISO nonlinear systems, its computer load is not too large;2) one kind of quickly constrained generalized predictive control algorithm is presented based on the T-S fuzzy model which is used to approach the MIMO nonlinear systems,it avoids the nonlinear search and need not to solve Diophantine functions by using a soft gene of input,so its computer load is not too large;In the last section of this dissertation, a conclusion is presented, and some jobs needed to be done in the future are drawn.