Fuzzy Predictive Control Theory And Design Methods

Model-based predictive (MPC) control is one kind of advanced control technique, which was developed from the industrial process in the 1970s. It adopts the predictive model based on experiment and is one new computer optimization control algorithm that requires low in the model precision, has good integration control quality and is convenient for the online calculation. Since the emergence of MPC, it gets the comprehensive attention by the control kingdom all the time and has been applied in various complicated industrial practice successfully. MPC has shown powerful life. However now MPC is facing new challenge: the industrial kingdom puts forward that further resolve some problems such as uncertain description, multi-object optimization decision-making in uncertain environment, real-time control and fast computation. The development of MPC has been appeared some shortage: the predictive model is still single model; the optimization in MPC to special applied situation and self-adapting instance is complex for computation; revise method is limited. So MPC must draw value ideas from other theories and methods. Intelligent control provides one good reference to MPC. With the combine between the gained productions of MPC and Fuzzy Control, Neural Network and Gene Algorithm, MPC is developing along the intelligent model predictive control (IMPC), which strengthens the disposal abilities facing complex environment, object and task. So the fields of theory study and application are open wide.Fuzzy identification and control theory have great potential in solving control problems of complex system that is one novel intelligent control methodology. As the widely used T-S fuzzy model, it is essentially one nonlinear model and is easy to describe complicated dynamic system. It has been proved in theory that T-S model can approach arbitrary nonlinear systems according to arbitrary precision. Using the T-S fuzzy model as predictive model can fitly remedy the shortage of single predictive model. This paper integrates the T-S fuzzy model and its identification methods into the generalized predictive control, and then presents one new method of fuzzy generalized predictive control. Further research on the fuzzy predictive control will have great signification for theory and practical applications.The content summary of this dissertation:1. Review the basic principles and characters of fuzzy control and predictive control, analyze the possibility of integration between them, induce and summarize the research actuality of MPC.2. Analyze the T-S model and its identification, point out that this model is oneuniversal approximator and present the corresponding proves. Combined with the analysis the worked examples were given which shows the superiority of the fuzzy identification methods and this T-S model.3. Present two means of fuzzy generalized predictive control based on T-S fuzzy model and generalized predictive control (GPC); present and analyze two kinds ofidentification methods of the fuzzy predictive model. The simulation to nonlinear system shows that this T-S model has better tracking effect. The simulation, analysis and compare between the two fuzzy GPC provide the gist to the practical design of fuzzy predictive control.4. Give the qualitative theoretical analysis to the choice of the GPC parameters, stability and robustness; discuss the methods on the design and choice for the main parameters of GPC systems; by simulation validate the correctness of the principle for the parameters choice, further strengthen the study of MPC and perfect this fuzzy predictive control.