Upon completing this chapter, the reader will be able to design a variety of adaptive fuzzy controllers for practical applications. The reader should consider this chapter fundamental to the study of fuzzy control systems as adaptation techniques such as the ones presented in this chapter have proven to be some of the most effective fuzzy control methods
11 Upon completing this chapter, the reader will be able to design a variety of adaptive fuzzy controllers for practical applications. The reader should consider this chapter fundamental to the study of fuzzy control systems as adaptation techniques such as the ones presented in this chapter have proven to be some of the most effective fuzzy control methods
4.2 Fuzzy Model Reference Learning Control(FMRLC) A"learning system"possesses the capability to improve its performance over time by interacting with its environment. A learning control system is designed so that its learning controller" has the ability to improve the performance of the closed-loop system by generating command inputs to the plant and utilizing feedback information from the plant 12
12 4.2 Fuzzy Model Reference Learning Control (FMRLC) A "learning system" possesses the capability to improve its performance over time by interacting with its environment. A learning control system is designed so that its "learning controller" has the ability to improve the performance of the closed-loop system by generating command inputs to the plant and utilizing feedback information from the plant
In this section we introduce the"fuzzy model reference learning controller"(FMRLC), which is a(direct) model reference adaptive controller. The term"learning"is used as opposed to adaptiveto distinguish it from the approach to the conventional model reference adaptive controller for linear systems with unknown plant parameters. In particular, the distinction is drawn since the fMrlc will tune and to some extent remember the values that it had tuned in the past, while the conventional approaches for linear systems simply continue to tune the controller parameters Hence, for some applications when a properly designed FMRLC returns to a familiar operating condition, it will already know how to control for that condition. Many past conventional adaptive control techniques for linear systems would have to retune each time a new operating condition is encountered. 13
13 In this section we introduce the "fuzzy model reference learning controller" (FMRLC), which is a (direct) model reference adaptive controller. The term "learning" is used as opposed to "adaptive" to distinguish it from the approach to the conventional model reference adaptive controller for linear systems with unknown plant parameters. In particular, the distinction is drawn since the FMRLC will tune and to some extent remember the values that it had tuned in the past, while the conventional approaches for linear systems simply continue to tune the controller parameters. Hence, for some applications when a properly designed FMRLC returns to a familiar operating condition, it will already know how to control for that condition. Many past conventional adaptive control techniques for linear systems would have to retune each time a new operating condition is encountered
Learning mechanism Reference k model Knowledge-base Knowledge-base PETA modifier Inference mechanism storage Fuzzy inverse model" yc(KT mite Fuzzy sets Rule- base Plant r(kr) g. Inference e mechanism Fuzzy controller FIGURE 4.3 Fuzzy model reference learning controller
14 FIGURE 4.3 Fuzzy model reference learning controller
he functional block diagram for the FMRLC is lant, the fuzzy controller to be tuned, tho- the time signals since it is easier to exp lain the03 o shown in Figure 4.3. It has four main parts plal reference model, and the learning mechanisi (an adaptation mechanism). We use discret operation of the FMRLC for discrete time systems. The FMRLC uses the learning mechanism to observe numerical data from a fuzzy control system i.e., r(kT and y(kT) where T is the sampling period). Using this numerical data, it characterizes the fuzzy control system's current performance and automatically synthesizes or adjusts the fuzzy controller so that some given performance objectives are met
15 The functional block diagram for the FMRLC is shown in Figure 4.3. It has four main parts: the plant, the fuzzy controller to be tuned, the reference model, and the learning mechanism (an adaptation mechanism). We use discrete time signals since it is easier to explain the operation of the FMRLC for discrete time systems. The FMRLC uses the learning mechanism to observe numerical data from a fuzzy control system (i.e., r(kT) and y(kT) where T is the sampling period). Using this numerical data, it characterizes the fuzzy control system's current performance and automatically synthesizes or adjusts the fuzzy controller so that some given performance objectives are met