| Summary: | 碩士 === 國立臺灣大學 === 電機工程學研究所 === 100 === Web 2.0 internet platform is an open space for sharing. Nowadays, sharing knowledge and ideas via the internet platform is extremely popular. For example, collaborative software programming and music composition through the internet platform are common. Inspired by this sharing possibility, this thesis proposes the concept of collaborative fuzzy controller design via the internet platform and studies related problem. Since the control problems uploaded to the proposed platform are more likely difficult ones, and fuzzy controllers are particularly suitable for nonlinear, time-varying and uncertain systems, collaborative design of fuzzy controllers has its niche.
The internet platform for collaboratively designing fuzzy controllers to be discussed in this thesis has similar components and structure to the online encyclopedia “Wikipedia”. The construction of such a platform is considered from three different perspectives: system structure, design principle, and web page. To understand the feasibility of implementing such a platform, similarities to and differences from the “Wikipedia” are investigated first. It is assumed that in response to the control problems uploaded, fuzzy control rules are sought for a given fuzzy controller framework, and the platform needs to accomplish the following tasks.
i. To judge if the performance is better or worse when an existing rule is substituted by a new control rule. Fuzzy controllers are frequently adopted for plants without explicit mathematical model, so it is challenging to judge if control rules would enhance the performance before new control rules are replaced into the original rule base.
ii. To select control rules to compose a new fuzzy rule base with better performance than that of the original fuzzy controller.
To do the above to four selected plants, this thesis proposes six criteria for control problem providers to discriminate and utilize the control advices received on the platform. All the six criteria are developed by testing four plants through the following five steps: (i) observing the change of performance index value with respect to the membership function changes; (ii) developing the solution policy based on results from step (i); (iii) searching other tools to assist judging the performance of control rules; (iv) selecting plants, control specifications, fuzzy controllers, and control rule advices to create a set of judging criteria; and (v) testing different plants and specifications to adjust the criteria by the trial and error method. The performance indices of the above five steps are maximum overshoot and rising time. According to criterion 1, one can classify all the control rule advices into several groups. Users who need to engage in control design can understand what control advices are available on the internet platform. Criterion 2 suggests which control rule advice with the same antecedent should be substituted into original rule base to run simulation first. This thesis classifies the change of performance index value with respect to the membership function changes into three types. Criterion 3 is developed for invariant type. According to criterion 4 and criterion 5, one can judge if the performance will become better or not by observing the linguistic gradient changes of original fuzzy controller and fuzzy controller whose rules are replaced with control rule advices on the platform. Criterion 6 provides a feasible combination mechanism of control rule for designing a fuzzy controller whose performance is better than the original one.
Finally, an inverted pendulum is adopted as the plant to verify the feasibility of the proposed criteria. Assume that there are 647 control rule advices proposed on the platform, and 196 control rules need to be substituted into the original rule base to run simulations. According to the criteria proposed in this thesis, performances of the remaining 451 control rules are judged without replacing the rules into original rule base. The accuracy of judging result is about 86.3%.
Keywords:sinternet platform, collaborative design, fuzzy controller, fuzzy control rule.
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