【正文】 t is the instantaneous control modifier u which is the variation for the converter’s voltage. The instantaneous modifier can be used to control the converter’s output frequency. In the figure, Ke, Kec and ku denote the quantitative factor for error e, error variance ratio de/dt and control modifier u respectively. Thus a twoinputoneoutput fuzzy controller is designed for the frequency control system. B. Design of the Fuzzy Controller Design of fuzzy control rules is the core of design of the fuzzy controller. Fuzzy control rules are equivalent to the correcting device or pensator in the traditional control system. The corresponding language variables for the speed error e, the error rate de/dt and the output control variable u are E, EC and U, respectively. They are divided into five states and every state corresponds to a fuzzy subset, as shown in the following table. TABLEⅠ . Table for System Control Rules The language variables used to describe the state of E, EC and U are: negative big (NB), negative small (NS), zero (ZE), positive small (PS) and positive big (PB). As the values in domain Nj=[njnj] are symmetrical, we can transform the error of e in domain [25,25], the error of de/dt in domain [18,18] and the error of u in domain [35,35] into [5,5] according to the conversion formula given by: K=2n/（ ba） (1) in which a is the lowest value 5 and b is the highest value 5. Thus :Ku=7, Ke=, Kec=5/18 and 1/K is the quantitative factor. We can get the general fuzzy implication relation matrix R for the basic fuzzy controller in the frequency converter system ,based on the “union” operation of the 25 (i =1, 2, …, 25) fuzzy relation, as shown in (2). R=R1∪ R 2∪ ...∪ R25= 251i??iR (2) “Defuzzication”is used to convert the fuzzy set into single values. One of the most monly used methods is the average value method in the maximum membership methods, as shown in (3) in which j=1, 2,…. n, and n points of the memberships reach maximum value. 1njjmomuu n??? (3) Output of the Controlled Variables The defuzzed rules are adopted by Siemens PLC series to realize fuzzy control function. The system acquires input data through an analog input module, then mands the executive unit through an analog output module. The system will get the value of the error e from the given speed and the actual speed sampled by encoder,then multiply e and de/dt by would be counted by fuzzy control quantity U which is fixed by E and EC, multiplied by the proportional factor Ku. Then the motor speed can be controlled by the convertor. The following chart (Figure 3) is the flow chart for fuzzy control algorithm. Flow Chart for Fuzzy Control Algorithm The fuzzycontrol lookup table is the most important part in program design. It can be realized as the following chart, : ladder diagram (LAD) for fuzzycontrol lookup table In , CMP is the pare instruction. When the input relay changes from 0 to 1, the data of the first operand (VW102) will be pared with the preset data stored in seven consecutive digital registers starting from the second operand (VW110). If the data in the first operand is the same as one in the second operand, then the auxiliary relay in the third operand will be set to “ ON ”。 otherwise, it will be “ OFF ”. The 25 control results of fuzzy rules are stored sequentially in VW60VW84. The data in VW102 and VW103 will be pared with the elements in input domain (VW110VW116) respectively. The result will be exported to VW100 according to the state lookup results of and . As the capacity of the fuzzycontrol lookup table is limited, the designed program has a quick response and good control performance. IV. COMPUTER SIMULATION A． The Connection of FIS and SIMULINK Based on Fuzzy Inference System (FIS), the fuzzy control system is simulated through the SIMULINK module in the MATLAB software. The “Fuzzy Logic Controller” in the “ Fuzzy Logic Toolbox” must be used in the structure of the simulation model for fuzzy control system[3] Fuzzy Logic Controller Module The FIS structured text in the FIS editor is sent to the MATLAB work space, then a file is obtained by entering of “ readfis” in the main window. The structure list of the file is given in the following figure. Structure Text of FIS Displayed in MATLAB Workspace After sent to the MATLAB workspace, the FIS structure text can be embedded to the “Fuzzy Logic Controller” module. Figure 6 shows the FIS is embedded successfully. Dialog Box Implying the Success Embedment of FIS A. SIMULINK Simulation of the System After the embedding of FIS into the “Fuzzy Logic Controller” [6], we can call other modules in SIMULINK for the system simulation. The simulation model is shown in Figure 7. Simulation Model in SIMULINK Comparison of PID Motor Speed Control and Fuzzy Control V. CONCLUSIONS From the simulation results, the speed regulation performance of the fuzzy controller is superior to that of the traditional PID speed regulator. It can effectively overe the effects of timevarying parameters and nonlinearity of the system on the speedregulationperformance. The fuzzycontroller has a good rapidity, stability and stronger robustness. In recent years, the hoist speed control system bining the frequency converter and PLC is widely applied in mining industry. To conduct fuzzy control based on PLC, the speed of motor can be regulated according to external factors. In this way, the features of PLC such as reliability, flexibility, patibility can be maintained, and also the robustness and automation of the control system are boosted. The proposed speed control system for mine hoist has a broad prospective in application. REFERENCES [1] Liao Changchu. PLC pro