A direct adaptive predictive control based on T-S fuzzy model was proposed. The structure parameters of T-S fuzzy model were identified online using the weighted recursive least squares method. The identified parameters of fuzzy model were used to directly receive the model predicted output with direct iterative for the T-S model. The algorithm is a good way to resolve the two major problems, modeling and optimization, and provide a guarantee for high-precision control of nonlinear systems. Simulation result shows that the proposed algorithm is an effective control strategy with excellent tracing ability.

A predictive control method based on differential evolution was put forward in connection with changing parameters nonlinear model. By using neural network and accord as input and output data of the system, a predictive model was established. And then differential evolution algorithm was used to do rolling optimization and improve system performance accordingly. At last, GA and DE were used respectively to do predictive simulation to realize the rolling optimization and compare the two results. The two simulation results show that the rolling optimization of predictive control based on DE is better than that based on GA on the whole.