Fuzzy Immune PI Control Of Networked Control System Based On Prediction Compensation

Networked control system (NCS) is used more and more widely based on its advantages of high expansibility, flexible structure, low cost, expediently installed and maintained. But at the same time, the network introduced to the control loop will induce some problems that can not be avoided, of which, the most important is the time-delay and packet lost. The existence of time-delay will deteriorate the performance of the control system, such as increasing the overshot and rising time, and so on. Worse more, sometimes, it will make the stability domain small or even not stable. Therefore, the time-delay is one of the issues concerned most in networked control system.In this paper, based on the character of the delay, a control strategy composed of Smith prediction compensator and fuzzy immune PI controller is designed to compensate the time-delay.Firstly, in this paper, the plant and network are looked as one time-varying system. Because the time-delay is time-varying, the parameters of the system are time-varying. Least Squares (LS) is used to estimate the parameters on line. And based on this, Smith compensator is used to compensate the system.Considered the existing of prediction error caused by LS, Smith compensator can't compensate the delay completely. Therefore, at the same time, immune PI controller is applied to the system. This controller regulates the coefficients of PI controller according to the changes of control input. If parameters and nonlinear function are chose properly, it can greatly improve the control performance of the system. To show the validity of this approach, DC motor is taken as plant to do simulation. The result show that this method is easy, and flexible, can improve the dynamic and static performance, and make system more robust in disturb and uncertain environment.An experiment platform based on campus network is designed, and its software structure is introduced. Finally, validity of the control strategy proposed in this paper is verified on this platform.