se in controller development, especially for many conventional control design procedures that require restrictive assumptions for the plant(e.g, linearity) Why do we select fuzzy controllers in many real-world systems? It is for this reason that in practice conventional controllers are often developed via simple models of the plant behavior that satisfy the necessary assumptions, and via the ad hoc(special) tuning of relatively simple linear or nonlinear controllers. Regardless, it is well understood(although sometimes forgotten) that heuristics enter the conventional control design process as long as you are concerned with the actual implementation of the control system. It must be acknowledged, moreover, that conventional control engineering approaches that use appropriate heuristics to tune the design have been relatively successful How much of the success can be attributed to the use of the mathematical model and conventional control design approach, and how much should be attributed to the clever heuristic tuning that the control engineer uses upon implementation? And if we exploit the use of heuristic information throughout the entire design process, can we obtain higher performance control systems? Fuzzy control provides a formal methodology for representing, manipulating, and implementing a human s heuristic owledge about how to control a system. In this section we seek to provide a philosophy of how to approach the design of fuzzy controllers. The fuzzy controller block diagram is given in Figure 3. 1, where we show a fuzzy controller embedded in a closed-loop control system. The plant outputs are denoted by y(), its inputs are denoted by u(t), and the reference input to the fuzzy controller is denoted by r(1) Fuzzy controller 8 Reference input.# Inference Cutput r(t mechani sm subProcess Rule-base Figure 2. 1 Fuzzy controller architecture Basically, you should view the fuzzy controller as an artificial decision maker that operates in a closed-loop system in real time. It gathers plant output data y (), compares it to the reference input r(1), and then decides what the plant input u(t) should be to ensure that the performance objectives, will be met What is a fuzzy controller composed of The fuzzy controller has four main components: (1)The"rule-base"holds the knowledge, in the form of a set of rules, of how best to control the system. (2) The inference mechanism evaluates which control rules are relevant at the current time and then decides what the input to the plant should be. (3)The fuzzificationuse in controller development, especially for many conventional control design procedures that require restrictive assumptions for the plant (e.g., linearity). Why do we select fuzzy controllers in many real-world systems?It is for this reason that in practice conventional controllers are often developed via simple models of the plant behavior that satisfy the necessary assumptions, and via the ad hoc(special) tuning of relatively simple linear or nonlinear controllers. Regardless, it is well understood (although sometimes forgotten) that heuristics enter the conventional control design process as long as you are concerned with the actual implementation of the control system. It must be acknowledged, moreover, that conventional control engineering approaches that use appropriate heuristics to tune the design have been relatively successful. How much of the success can be attributed to the use of the mathematical model and conventional control design approach, and how much should be attributed to the clever heuristic tuning that the control engineer uses upon implementation? And if we exploit the use of heuristic information throughout the entire design process, can we obtain higher performance control systems? Fuzzy control provides a formal methodology for representing, manipulating, and implementing a human's heuristic knowledge about how to control a system. In this section we seek to provide a philosophy of how to approach the design of fuzzy controllers. The fuzzy controller block diagram is given in Figure 3.1, where we show a fuzzy controller embedded in a closed-loop control system. The plant outputs are denoted by y(t), its inputs are denoted by u (t), and the reference input to the fuzzy controller is denoted by r (t). Figure 2.1 Fuzzy controller architecture. Basically, you should view the fuzzy controller as an artificial decision maker that operates in a closed-loop system in real time. It gathers plant output data y(t), compares it to the reference input r (t), and then decides what the plant input u(t) should be to ensure that the performance objectives, will be met. What is a fuzzy controller composed of ? The fuzzy controller has four main components: (1) The "rule-base" holds the knowledge, in the form of a set of rules, of how best to control the system. (2) The inference mechanism evaluates which control rules are relevant at the current time and then decides what the input to the plant should be. (3) The fuzzification