interface simply modifies the inputs so that they can be interpreted and compared to the rules in the rule-base. (4)the defuzzification interface converts the conclusions reached by the inference mechanism into the inputs to the plant How do we design a fuzzy controller? To design the fuzzy controller, the control engineer must gather information on how the artificial decision maker should act in the closed-loop system. Sometimes this information can come from a human decision maker who performs the control task, while at other times the control engineer can come to understand the plant dynamics and write down a set of rules about how to control the system without outside help. These"rules"basically say, "If the plant output and reference input are behaving in a certain manner, then the plant input should be some value. "A whole set of such"If-Then"rules is loaded into the rule-base, and an inference strategy is chosen, then the system is ready to be tested to see if the closed-loop specifications are met. This brief description provides a very high level overview of how to design a fuzzy control system. Below we will expand on these basic ideas and provide more details on this procedure and its relationship to the conventional control design procedure whether do we need a model in fuzzy control? People working in fuzzy control often say that"a model is not needed to develop a fuzzy controller, and this is the main advantage of the approach. However, will a proper understanding of the plant dynamics be obtained without trying to use first principles of physics to develop a mathematical model? And will a proper understanding of how to control the plant be obtained without simulation-based evaluations that also need a model? We always know roughly what process we are controlling(e.g, we know whether it is a vehicle or a nuclear reactor), and it is often possible to produce at least an approximate model, so why not do this? For a safety-critical application, if you do not use a formal model, then it is not possible to perform mathematical analysis or simulation-based evaluations Is it wise to ignore these analytical approaches for such applications? Clearly, there will be some applications where you can simply"hack"together a controller(fuzzy or conventional)and go directly to implementation In such a situation there is no need for a formal model of the process; however, is this type of control problem really so challenging that fuzzy control is even needed? Could a conventional approach(such as PID control)or a"table look-up"scheme work just as well or better, especially considering implementation complexity? Overall, when you carefully consider the possibility of ignoring the information that is frequently available in a mathematical model, it is clear that it will often be unwise to do so. Basically, then, the role of modeling in fuzzy control design is quite similar to its role in conventional control system design. In fuzzy control there is a more significant emphasis on the use of heuristics, but in many control approaches(e. g, PID control for process control)there is a similar Basically, in fuzzy control there is a focus on the use of rules to represent how to control the plant rather than ordinary differential equations(ODE). This approach can offer some advantages in that the representation of knowledge in rules seems more lucid and natural to some people. For others, though, the use of differential equations is more clear and natural. Basically, there is simply a"language difference"between fuzzy and conventional control: ODEs are the language of conventional control, and rules are the language of fuzzy control The performance objectives and design constraints are the same as the ones for conventional control that we ummarized above, since we still want to meet the same types of closed-loop specifications. The fundamental limitaticinterface simply modifies the inputs so that they can be interpreted and compared to the rules in the rule-base. (4) the defuzzification interface converts the conclusions reached by the inference mechanism into the inputs to the plant. How do we design a fuzzy controller? To design the fuzzy controller, the control engineer must gather information on how the artificial decision maker should act in the closed-loop system. Sometimes this information can come from a human decision maker who performs the control task, while at other times the control engineer can come to understand the plant dynamics and write down a set of rules about how to control the system without outside help. These "rules" basically say, "If the plant output and reference input are behaving in a certain manner, then the plant input should be some value." A whole set of such "If-Then" rules is loaded into the rule-base, and an inference strategy is chosen, then the system is ready to be tested to see if the closed-loop specifications are met. This brief description provides a very high level overview of how to design a fuzzy control system. Below we will expand on these basic ideas and provide more details on this procedure and its relationship to the conventional control design procedure. Whether do we need a model in fuzzy control? People working in fuzzy control often say that "a model is not needed to develop a fuzzy controller, and this is the main advantage of the approach." However, will a proper understanding of the plant dynamics be obtained without trying to use first principles of physics to develop a mathematical model? And will a proper understanding of how to control the plant be obtained without simulation-based evaluations that also need a model? We always know roughly what process we are controlling (e.g, we know whether it is a vehicle or a nuclear reactor), and it is often possible to produce at least an approximate model, so why not do this? For a safety-critical application, if you do not use a formal model, then it is not possible to perform mathematical analysis or simulation-based evaluations. Is it wise to ignore these analytical approaches for such applications? Clearly, there will be some applications where you can simply "hack" together a controller (fuzzy or conventional) and go directly to implementation. In such a situation there is no need for a formal model of the process; however, is this type of control problem really so challenging that fuzzy control is even needed? Could a conventional approach (such as PID control) or a "table look-up" scheme work just as well or better, especially considering implementation complexity? Overall, when you carefully consider the possibility of ignoring the information that is frequently available in a mathematical model, it is clear that it will often be unwise to do so. Basically, then, the role of modeling in fuzzy control design is quite similar to its role in conventional control system design. In fuzzy control there is a more significant emphasis on the use of heuristics, but in many control approaches (e.g, PID control for process control) there is a similar emphasis. Basically, in fuzzy control there is a focus on the use of rules to represent how to control the plant rather than ordinary differential equations (ODE). This approach can offer some advantages in that the representation of knowledge in rules seems more lucid and natural to some people. For others, though, the use of differential equations is more clear and natural. Basically, there is simply a "language difference" between fuzzy and conventional control: ODEs are the language of conventional control, and rules are the language of fuzzy control. The performance objectives and design constraints are the same as the ones for conventional control that we summarized above, since we still want to meet the same types of closed-loop specifications. The fundamental limitations