that the plant provides affect our ability to achieve high-performance control, and these are still present just as they were for conventional control (e.g, nonminimum phase or unstable behavior still presents challenges for fuzzy control How do we design a fuzzy controller? Fuzzy control system design essentially amounts to(1)choosing the fuzzy controller inputs and outputs, (2)choosing the preprocessing that is needed for the controller inputs and possibly postprocessing that is needed for the outputs, and (3) designing each of the four components of the fuzzy controller shown in Figure 2.1 As you will see in the next chapter, there are standard choices for the fuzzification and defuzzification interfaces Moreover, most often the designer settles on an inference mechanism and may use this for many different processes Hence, the main part of the fuzzy controller that we focus on for design to the rule-base What is the main task in fuzzy controller design process? The rule-base is constructed so that it represents a human expert"in-the-loop. Hence, the information that we load into the rules in the rule-base may come from an actual human expert who has spent a long time learning how best to control he process. In other situations there is no such human expert, and the control engineer will simply study the plant dynamics(perhaps using modeling and simulation) and write down a set of control rules that makes sense. As an example, in the cruise control problem discussed above it is clear that anyone who has experience driving a car can practice regulating the speed about a desired set-point and load this information into a rule-base. For instance one rule that a human driver may use is "If the speed is lower than the set-point, then press down further on the accelerator pedal. "A rule that would represent even more detailed information about how to regulate the speed would be If the speed is lower than the set-point AND the speed is approaching the set-point very fast, then release the accelerator pedal by a small amount This second rule characterizes our knowledge about how to make sure that we do not overshoot our desired goal (the set-point speed ) Generally speaking, if we load very detailed expertise into the rule-base, we enhance our chances of obtaining better performance What are the performance evaluation of fuzzy control? Each and every idea on performance evaluation for conventional controllers applies here as well. The basic reason for this is that a fuzzy controller is a nonlinear controller-so many conventional modeling, analysis (via mathematics simulation, or experimentation), and design ideas apply directly Since fuzzy control is a relatively new technology, it is often quite important to determine what value it has relative to conventional methods. Unfortunately, few have performed detailed comparative analyses between conventional and intelligent control that have taken into account a wide array of available conventional methods (linear, nonlinear adaptive, etc. ) fuzzy control methods(direct, adaptive, supervisory): theoretical, simulation, and experimental analyses computational issues; and so on What should we pay attention in fuzzy controller design? Moreover, most work in fuzzy control to date has focused only on its advantages and has not taken a critical look at what possible disadvantages there could be to using it(hence the reader should be cautioned about this when reading the literature). For example, the following questions are cause for concern when you employ a strategy of gathering heuristic control knowled Will the behaviors that are observed by a human expert and used to construct the fuzzy controller include all situations that can occur due to disturbances, noise, or plant parameter variations? Can the human expert realistically and reliably foresee problems that could arise from closed-loop system instabilities or limit cycles?that the plant provides affect our ability to achieve high-performance control, and these are still present just as they were for conventional control (e.g, nonminimum phase or unstable behavior still presents challenges for fuzzy control). How do we design a fuzzy controller? Fuzzy control system design essentially amounts to (1) choosing the fuzzy controller inputs and outputs, (2) choosing the preprocessing that is needed for the controller inputs and possibly postprocessing that is needed for the outputs, and (3) designing each of the four components of the fuzzy controller shown in Figure 2.1. As you will see in the next chapter, there are standard choices for the fuzzification and defuzzification interfaces. Moreover, most often the designer settles on an inference mechanism and may use this for many different processes. Hence, the main part of the fuzzy controller that we focus on for design to the rule-base. What is the main task in fuzzy controller design process? The rule-base is constructed so that it represents a human expert "in-the-loop." Hence, the information that we load into the rules in the rule-base may come from an actual human expert who has spent a long time learning how best to control the process. In other situations there is no such human expert, and the control engineer will simply study the plant dynamics (perhaps using modeling and simulation) and write down a set of control rules that makes sense. As an example, in the cruise control problem discussed above it is clear that anyone who has experience driving a car can practice regulating the speed about a desired set-point and load this information into a rule-base. For instance, one rule that a human driver may use is "If the speed is lower than the set-point, then press down further on the accelerator pedal." A rule that would represent even more detailed information about how to regulate the speed would be "If the speed is lower than the set-point AND the speed is approaching the set-point very fast, then release the accelerator pedal by a small amount." This second rule characterizes our knowledge about how to make sure that we do not overshoot our desired goal (the set-point speed). Generally speaking, if we load very detailed expertise into the rule-base, we enhance our chances of obtaining better performance. What are the performance evaluation of fuzzy control? Each and every idea on performance evaluation for conventional controllers applies here as well. The basic reason for this is that a fuzzy controller is a nonlinear controller—so many conventional modeling, analysis (via mathematics, simulation, or experimentation), and design ideas apply directly. Since fuzzy control is a relatively new technology, it is often quite important to determine what value it has relative to conventional methods. Unfortunately, few have performed detailed comparative analyses between conventional and intelligent control that have taken into account a wide array of available conventional methods (linear, nonlinear, adaptive, etc.); fuzzy control methods (direct, adaptive, supervisory); theoretical, simulation, and experimental analyses; computational issues; and so on. What should we pay attention in fuzzy controller design? Moreover, most work in fuzzy control to date has focused only on its advantages and has not taken a critical look at what possible disadvantages there could be to using it (hence the reader should be cautioned about this when reading the literature). For example, the following questions are cause for concern when you employ a strategy of gathering heuristic control knowledge: • Will the behaviors that are observed by a human expert and used to construct the fuzzy controller include all situations that can occur due to disturbances, noise, or plant parameter variations? • Can the human expert realistically and reliably foresee problems that could arise from closed-loop system instabilities or limit cycles?