| It makes great development about the theory ,and application of Fuzzy Logic Control (FLC) in recent years. Because this approach can efficiently utilize system local information and expert operation experiences to get the general model by means of fuzzy reasoning, it resolves what's meaning constructing model and controlling for complex plants. But, there still exists two bottlenecks in design of FLC: One is the choice and optimization of fuzzy rules. Because the choice of rules depends on expert operation experiences, it is subjective. While the control system is complex or inputs are much more, the probable selective space quickly becomes large so that it will cause "rules burst". Even while the control system uses linear fuzzy rules with two inputs and one output, the design of fuzzy rules is short of graphic guidance. The other is the choice of the fuzzy set partition and membership function. After gaining the fuzzy rules, the performance of FLC depends on membership function of each fuzzy set partition. On account of optimization of many parameters, acquiring general optimization is very difficult.Based on above discuss facts, this dissertation makes a study on the design of optimization of FLC using a combination of fuzzy logic, rough sets, sets pair analysis, sliding mode and genetic algorithms. The main contents are as follows:1) Both rough and fuzzy sets theories can be used to deal with imprecise and incomplete information. Both of them are often used to observe, test and reason about data. This chapter applies degree of connection in sets pair analyzed to rough sets theory. The relationship between rough-degree of connection and cardinal of rough lower/upper approximation is demonstrated. The difficulties of drawing fuzzy control rules are analyzed in this paper. A new method for drawing and filtering fuzzy control rules by using rough sets and sets pair analyzed is proposed. The result shows this method is quite effective in fuzzy rules optimization for a large class of complex systems.2) For a typical fuzzy controller with two inputs and one output, a method of analyzing the rule-base with the theory of sliding mode is presented when it useslinear fuzzy control rules. Both graphic and numerical analysis illustrate or demonstrate that typical FLC possesses properties of its linear control and sliding mode control. These dual features can help us understand the robustness of FLC systems. Simulation results show the effectiveness of the proposed method.3) To combine fuzzy logic control with genetic algorithm, On the one hand, fuzzy control can express the systemic information with non-linear and fuzzy knowledge; on the other hand, genetic algorithms can improve the systemic ability of self-study. In this chapter, fuzzy genetic algorithm is used to optimize the fuzzy set partitions and the fuzzy logic control method is used to adjust the probabilities of crossover and mutation. It can be obtained to the design of optimal or sub-optimal fuzzy logic controller. For an industrial process described by a second-order model, a computer simulation is conducted. The result shows the proposed method is effective.Finally, on the basis of the summarization of the research results in this dissertation, the future developments about FLC are discussed.
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