Delay System Control And Nonlinear System Identification And Simulation

First of this thesis is a brief introduction about the development and situation of the considered problem, the control of the big time-delay system and nonlinear system identification. And points out that the research is not only theoretically interesting, but also very important in practical applications.Delay component exist universally in the process of industrial production. Big time-delay has a severe impact on the system stability. Even worse, it may cause the instability of closed loop system or incapability of implementing effective control. In chapter 2, we analyse the the weakness of traditional PID controller and the over reliance of Smith predictive control on the model. And put forward that a proper filter can be placed in front of the controller in order to improve the performance and robustness of the controller. The simulation experiments show that the control performance is very good.Nonlinear phenomena are general problems in every field of engineering technology, science research, natural world and human society activities. In chapter 3 and 4, we take the complex nonlinear systems as object and mainly studies their identification methods. And introduces the main theories of neural network and fuzzy logic system, including the technology and approach for nonlinear system identification and their advantages and shortcomings. A kind of identification methods based on algorithms available now is presented. It mixes the merits between Fuzzy Control and Neural Network. To adjust the parameters is completed under the online condition according to the dynamic changes of process parameters, and to make the nonlinear system identifieation efficient is solved. The simulation shows the superiority of the identification scheme.In the last chapter, we summarize the contents of the above two chapters and propose some problems and directions that are worthwhile to further research.