Ｆｕｚｚｙ ＰＩＤ－Ｌｉｋｅ Ｃｏｎｔｒｏｌｌｅｒ Ｄｅｓｉｇｎ

• Main Authors: ＷｅｉＳｈｅｎｇ Ｃｈｉ, 紀渭勝
• Other Authors: ＲｅｙＣｈｕｎ Ｈｗａｎｇ
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/02420783587506223380

碩士 === 義守大學 === 電機工程學系 === 88 === PID controller is the most popular control tool in many industrial applications since its simple architecture and conceivable physical intuition of its parameters. Traditionally, the parameters of a conventional PID controller, i.e., , , and , are usually fixed during operation. Consequently, such a controller is inefficient for control of a system while the system is disturbed by unknown factors, or the surrounding environment of the system is changed. Several methods including optimization, genetic algorithm, neural network and so on for PID gains tuning have been proposed. However, the common problem of these methods is that the mechanism takes too much time for computing. It is a liability in real-time control and hardware implementation. Fuzzy theory has been applied to many real engineering areas. Unlike traditional control techniques, precise information of the system is not necessary before a suitable fuzzy controller is designed. Therefore, a wide variety of fuzzy controllers have also been investigated and developed. However, it is not easy to design a good fuzzy controller for very complex nonlinear systems, if a highly accurate performance of the control is desired. As we know, there are many parameters are involved and needed considering when a fuzzy system is developed. Until now, no theoretical technique for the best parameters' decision is reported, trial-and-error is the only well-known method. In most of fuzzy controller designing process, the error (e) and error change rate (Δe) of the system’s output are two mainly input variables used to the fuzzy system. Its control rule is basically composed of by many IF-THEN statements that are mainly inferred from the ideas of PD controller. The sum of error ( ) is generally not included in the inference engine of fuzzy controller designing like PID controller done. In fact, the rule base is very hardly constructed if needs to be considered. That is the reason why that e andΔe are variables adopted in most of fuzzy controllers. Therefore, the performance of a simple fuzzy controller acts almost like a PD controller. The characteristic of I controller will then be displayed after the parameters of fuzzy system are accurately tuned by the operator with a full knowledge of fuzzy controller design. Such a tuning process is usually minute and complicated. In this study, a fuzzy PID-like controller is developed. This controller is basically composed of by two independent fuzzy controllers. Since the first fuzzy controller is expected to act as a PD controller, we do not pay much attention to the parameters tuning in this controller designing process. Second controller is designed by using the TSK fuzzy model. is the only variable that needs to be considered in this controller. Then, these two controllers are combined to be a fuzzy PID-like controller we presented. Since the number of fuzzy parameters needed tuning is greatly reduced, the designing procedure of a good fuzzy controller becomes easier. Fuzzy PID-like controller has not only the characteristics of fuzzy controller, but also the advantages of PID controller. Such a designing technique makes this controller can perform well for very complex, nonlinear systems, especially for the control environment is limited under constraint condition. Several nonlinear systems are implemented by the controller we proposed to demonstrate its practicability. From the simulation results, such a controller we designed shows a great promising and has the potential in practical using for control applications.