In Electronic Infomation Category: D | on April 26,2011

Based on the fuzzy controller based on the design and **TS4990IQT datasheet** and implementation of a dual-fuzzy controller, based on the actual size of the system output error signal, respectively, using two fuzzy controllers to control, to improve system speed and **TS4990IQT price** and eliminate errors.

** 1 Double Fuzzy Controller **

** Single fuzzy controller is mainly used for rapid response and TS4990IQT suppliers and the elimination of large errors in a single fuzzy controller, scaling factor Ke its error increases, which is equivalent to reducing the basic domain of the error, increases on Control of error variables. At the same time, the error change rate factor Kec increased to reduce the excess. Scaling factor to control the amount of Ku reduction, to reduce the system oscillations. **

** Principle of dual fuzzy controller shown in Figure 1, assuming the variable eo to the large and small errors in the critical value (set according to the actual people), when the system error is larger, with a single fuzzy controller, a control, to achieve rapid response, the purpose of eliminating errors; when the system error is small, with a single fuzzy controller 2 to control, thereby improving the fuzzy controller for the system error is small, the control effect, thus helping to achieve better control effect. **

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** The simulation, a given input signal unit step signal. Control object is a typical time-varying objects, mathematical model is expressed as: where T1, T2 is the time constant, respectively, 100 s and 72 s, for the system lag time 10 s, K is proportional coefficient, a value of 2. The system is a large delay system, nonlinear characteristics, typical of industrial control object! **

** Dual fuzzy controller design, the input signal quantization error e of 8 grades, {NB, NM, NS, NO, PO, PS, PM, PB}, error change rate and the output variable u ec quantified 7 grades, {NB, NM, NS, ZO, PS, PM, PB}, rate of change of error e and error ec, the output variable u of the domain is [-6,6]. Rate of change of error e and error ec, the output variable membership functions u trapezoidal membership functions chosen as shown in Figure 2; **

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** Experience and knowledge of experts in the summary, based on the fuzzy control rule table are shown in Table 1. Control rules determine how much the accuracy of the control system, the number of control rules with the input and output variables are the number of linguistic values ??of each variable number of other factors. The system designed a total of 56 rules shown in Table 1: **

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** Reasoning used is the maximum and minimum inference method. The final reasoning result fuzzy set is expressed in the form of the system output valve correction. The valve can not be adjusted so that way, so the need of the Fuzzy precision, the design used in the center of gravity method for defuzzification. **

** 2 simulation process and results **

** Simulation using MATLAB-SIMULINK, the establishment of the system simulation of the two fuzzy controller structure shown in Figure 3. Simulation of structure in the design of the two subsystems as shown in Figure 3, the two subsystems are basically the same structure, just select a different specific parameters. **

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** Observed experimental results using SCOPE, records, comparative test results, which Figure 4 shows the results of conventional PID control output, Figure 5 shows the output of fuzzy controller, fuzzy controller in Figure 6 for the dual-output. **

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** Control chart from the results of view, the system response time were compared: the shortest response time, dual-fuzzy control, fuzzy control Second, the longest conventional PID control response; conventional PID control is about 600 s to reach steady-state time, fuzzy control needs 400 s to reach steady state, but less than 300 s double-fuzzy controller to reach steady state. **

** Obvious conventional PID control overshoot, fuzzy control and fuzzy control there is no double overshoot. Fuzzy control method and the double difference between the fuzzy controller, fuzzy control 2% to 5% of the steady-state error, while the dual fuzzy controller in the steady state to eliminate the steady state error! **

** 3 Summary **

** This design implements a dual-fuzzy controller, fuzzy controller using two complete simulation of the system. Simulation results show that two-short rise time of the fuzzy controller, response speed, steady and high precision. From the simulation results, and the conventional PID control and fuzzy control than ordinary dual fuzzy controller effectively reduces the steady-state error, response time, overshoot, settling time and other properties are better than traditional PID control and fuzzy control. Dual fuzzy controller can improve the control precision and stability, can be applied in practical engineering, and will bring great value! **

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