expert system as a controller (i.e, "expert control "); then we highlight ideas on how to use planning systems for control 4.5.1 Expert Control For the sake of our discussion, we will simply view the expert system that is used here as a controller for a dynamic system, as is shown in Figure 4.7. Here, we have an expert system serving as feedback controller with reference input r and feedback variable y. It uses the information in its knowledge-base and its inference mechanism to decide what command input u to generate for the plant. Conceptually, we see that the expert controller is closely related to the fuzzy controller Reference input Inference rdo mechanism Outputs Proces knowledge-base igure 4.7 expert control systems There are however. several differences 1. The knowledge-base in the expert controller could be a rule-base but is not necessarily so. It could be developed using other knowledge-representation structures, such as frames, semantic nets, causal diagrams, and so on 2. The inference mechanism in the expert controller is more general than that of the fuzzy controller. It can use more sophisticated matching strategies to determine which rules should be allowed to fire. It can use more elaborate inference strategies. For instance, some expert systems use(a)"refraction, "where if a rule has fired recently it may not be allowed back into the"conflict set"(i.e, the set of rules that are allowed to fire), (b)"recency, where rules that were fired most recently are given priority in being fired again, and(c) various other priority schemes It is in fact the case that an expert system is in a sense more general than a fuzzy system since it can be shown that a single rule in an expert controller can be used to represent an entire fuzzy controller 163 From another perspective, we can"fuzzify" the expert controller components and make it a more general fuzzy system. Regardless, it is largely a waste of time to concern ourselves with which is more general. What is of concern is whether the traditional ideas from expert systems offer anything on how to design fuzzy systems. The answer is certainly affirmative. Clearly, certain theory and applications may dictate the need for different knowledge-representation schemes and inference strategies Next, we should note that Figure 4.7 shows a direct expert controller. It is also possible to use an expert system in adaptive or supervisory control systems. Expert systems can be used in a supervisory role for conventional controllers or for the supervision of fuzzy controllers(e.g, for supervision of the learning mechanism and reference model in an adaptive fuzzy controller). Expert systems themselves can also be used as the basis for general learning controllers 4.5.2 Planning Systems for Control Artificially intelligent planning systems(computer programs that emulate the way experts plan) have been used in path planning and high-level decisions about control tasks for robots. a generic planning system can be configured in the architecture of a standard control system, as shown in Figure 4.8. Here, the"problem domain"(the plant) is the environment that the planner operates in. There are measured outputs yk at step k(variables of the problem domain that can be sensed in real time), control actions M(the ways in which we can affect the problem domain), disturbances dk PDF文件使用" pdffactory Pro"试用版本创建ww. fineprint,com,cnexpert system as a controller (i.e., "expert control"); then we highlight ideas on how to use planning systems for control. 4.5.1 Expert Control For the sake of our discussion, we will simply view the expert system that is used here as a controller for a dynamic system, as is shown in Figure 4.7. Here, we have an expert system serving as feedback controller with reference input r and feedback variable y. It uses the information in its knowledge-base and its inference mechanism to decide what command input u to generate for the plant. Conceptually, we see that the expert controller is closely related to the fuzzy controller. Figure 4.7 expert control systems. There are, however, several differences: 1. The knowledge-base in the expert controller could be a rule-base but is not necessarily so. It could be developed using other knowledge-representation structures, such as frames, semantic nets, causal diagrams, and so on. 2. The inference mechanism in the expert controller is more general than that of the fuzzy controller. It can use more sophisticated matching strategies to determine which rules should be allowed to fire. It can use more elaborate inference strategies. For instance, some expert systems use (a) "refraction," where if a rule has fired recently it may not be allowed back into the "conflict set" (i.e., the set of rules that are allowed to fire), (b) "recency," where rules that were fired most recently are given priority in being fired again, and (c) various other priority schemes. It is in fact the case that an expert system is in a sense more general than a fuzzy system since it can be shown that a single rule in an expert controller can be used to represent an entire fuzzy controller [163]. From another perspective, we can "fuzzify" the expert controller components and make it a more general fuzzy system. Regardless, it is largely a waste of time to concern ourselves with which is more general. What is of concern is whether the traditional ideas from expert systems offer anything on how to design fuzzy systems. The answer is certainly affirmative. Clearly, certain theory and applications may dictate the need for different knowledge-representation schemes and inference strategies. Next, we should note that Figure 4.7 shows a direct expert controller.It is also possible to use an expert system in adaptive or supervisory control systems. Expert systems can be used in a supervisory role for conventional controllers or for the supervision of fuzzy controllers (e.g., for supervision of the learning mechanism and reference model in an adaptive fuzzy controller). Expert systems themselves can also be used as the basis for general learning controllers. 4.5.2 Planning Systems for Control Artificially intelligent planning systems (computer programs that emulate the way experts plan) have been used in path planning and high-level decisions about control tasks for robots. A generic planning system can be configured in the architecture of a standard control system, as shown in Figure 4.8. Here, the "problem domain" (the plant) is the environment that the planner operates in. There are measured outputs yk at step k (variables of the problem domain that can be sensed in real time), control actions Mk (the ways in which we can affect the problem domain), disturbances dk PDF 文件使用 "pdfFactory Pro" 试用版本创建 www.fineprint.com.cn