The fuzzy controller has four main components:(1) Therule-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 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
11 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 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 12
12 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 Modeling Issues and Performance Obiectives Fuzzy Controller Desian Performance Evaluation Application Areas 13
13 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. ◼ Modeling Issues and Performance Objectives ◼ Fuzzy Controller Design ◼ Performance Evaluation ◼ Application Areas
2. 1.1 Modeling Issues and Performance Objectives 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? 14
14 2.1.1 Modeling Issues and Performance Objectives 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(fuzry or conventional and go directly to implementation. 15
15 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