Online Optimization Algorithm Of Fuzzy Predictive Control Based On T-S Model

In this dissertation, for complex nonlinear system difficult modeling and the large online optimization calculation of constrained predictive control, the identification algorithm of nonlinear system based on T-S fuzzy model is researched. On the basis of these, the fuzzy predictive control based on T-S model is researched. Several algorithms of the online optimization of constrained predictive control are studied, the simulation results show that the new method are of effectiveness. The main contents are concluded as followings:(1) In view of modeling problems of nonlinear and dynamic system, a self-learning fuzzy identification algorithm is presented based on T-S model in this paper. The premise parameter is identified by fuzzy clustering and the consequent parameter is identified by self-learning. The simulation result shows that the algorithm has high accuracy and can be used in online modeling.(2) The online optimization of constrained model predictive control could be converted into quadratic programming. Projected least-squares algorithm, active set method and Dantzig-Wolfe algorithm are contrastively researched in the quadratic programming of model predictive control. The simulation of Shell tower predictive control shows the projected least-squares algorithm is effective to solve the online optimization problem.(3) Two nonlinear fuzzy predictive control methods, namely fuzzy DMC and GPC based on T-S fuzzy model, are contrastively researched in the paper. And comparing the performance of fuzzy predictive control with the common PID control, the simulation result on pH neutralization demonstrates the performance of fuzzy predictive control performance is superior to conventional PID controller. Objective function is converted into linear programming and thus the huge computational burden is avoided. The projected least squares algorithm is also used in the online optimization of fuzzy predictive control.