The Research For The Modeling And Compensation Of The Time Delay And Dropout In NCS Based On The Fuzzy T-S Model

Networked Control System (NCS) is a kind of distributed feedback control system with a closed control loops through real time network. In NCS, controller, sensors and actuator exchange information through a shared network. The network time delay and dropout are the two most important parameters in Networked Control Systems (NCS), which maybe degrade the performance of NCS, reduce the scope of the stability, furthermore, make the system unstable. In order to overcome the bad effects caused by them, the scheme of compensate the delay and dropout is proposed in this paper.Firstly, the concept of NCS is presented in this paper, and the development and advantages of it are all introduced. After that, the problems of delays and dropouts are presented, as well as some problems related to them. The methods dealing with delays and dropouts which are used by predecessors are also reviewed.Secondly, the method of the experimental measurement for the time delays and dropouts is introduced. All the data collected here will be used to build the model later.Thirdly, two methods for compensating the delay and dropout are introduced here. One is based on Markov chain, and the other one is based on prediction. In order to validate the two methods, experimental simulations are made using the real data of delays and dropouts.Fourthly, a novel approach to compensate delays and dropouts using the T-S fuzzy method is proposed in this paper. The basic idea of this method is to assume that the system dynamics can be described by a set of rules rather than a single one, and the final output is given by a combination of the estimates according to all these fuzzy rules. GK fuzzy clustering will play a key role in the process of building the T-S fuzzy model and generating the predicted values.By numerical simulations, it is proved that through the T-S fuzzy method, the time delay and dropout can be predicted accurately, and compensated effectively and directly. The problem how to choose parameters of the T-S fuzzy model is also discussed in detail, and some improvements are put forward according to the discussion. By experimental simulations, the method using the fuzzy T-S model is also compared with the method using the BP neural network.At last, the conclusions and the prospects of the paper are proposed.