中英文翻译--常规pid和模糊pid算法的分析比较

中英文翻译--常规pid和模糊pid算法的分析比较内容摘要：

eal process. The model usually does not correspond to real system which limits the success of optimum methods. The prime idea of fuzzy control was to apply it at the place where there is no deep knowledge of transfer function of controlled system and where this knowledge can be hardly identified. These are often the cases where the fuzzy control leads to better performance paring with classical approach. Also for this instance it seems to be advantageous to have physical connection between fuzzy controller and its classical counterpart because it can significantly simplify the adjustment of regulator for real process. The heuristic optimum of parameters settings is also suitable for fuzzy PI controller with unified universe where the parameters are the same as the ones of classical PI controller. The parallel bination of fuzzy PI and PD controllers can be used for heuristic optimum of parameters settings but it should be noted that because of the presence of double proportional part in this regulator the adjusted parameters will differ from the ones of classical PID controller. But important thing is that the adjustment of this parameters is still in the same physical meaning. Note that for all previously 信息与电子工程系 毕业设计（中英文资料） 7 mentioned controllers it is also possible to employ time transformation (sample time modification) without having to change the scope of universes. 2. FUZZY PI CONTROLLER DESIGN The control signal generated by fuzzy PI controller (according to [2]) is A realization of the fuzzy PI controller is shown in Fig. 3. Let us assume plant described by following transfer function to illustrate and to pare behavior of fuzzy PI controller with classical continuous PI controller: Using the tuning method from [6] we obtain parameters K=, TI = s. The responses of controlled system using this control algorithm are shown in Fig. 4. The disturbance acts on the input of the system. Fuzzy controller was realized according to Fig. 3 with rule base mapping according to Fig. 2. The membership functions were distributed as shown in . The similar settings of parameters K = , TI = 5 with respect to optimum for different methods of inference and defuzzification methods was used. The scale was settled to M = 10 and sample period was set to T = s. The time responses for different inference and defuzzification methods are shown in Fig. 5. The following settings were tested according to [3]. The inference method MinMax and ProdMax. Defuzzification was done using COG method with singletons or triangles as an output membership functions. The simulations were launched with either three of seven fuzzy sets in all the normalized universes. Also singletons were realized using normalized universe. The singletons were located in vertexes of original fuzzy sets (see Fig. 1). Comparing the results obtained using classical PI and fuzzy PI controllers following discussion can take place. The output of the system has very small overshoot when it is controlled with fuzzy 信息与电子工程系 毕业设计（中英文资料） 8 regulator. The disturbance rejection using fuzzy controller is parable with disturbance rejection of classical PI controller. In the reference [6] in Fig. 5 there was shown that the step disturbance in the input of the system with amplitude brings the system to the significant oscillations. The same settings as in this article were used to obtain time responses. As it can be seen in Fig. 6, where there are corresponding time respo。