In Electronic Infomation Category: A | on April 15,2011

Abstract: In order to improve the control performance, a combination of fuzzy control and **N80C186XL16 datasheet** and PID control of the merits of proposed dual-mode based on fuzzy-PI controller design. A typical two-dimensional fuzzy controller part by the lack of integration, it is difficult to eliminate steady state error, control, precision often can not meet the system requirements; the PI controller has good steady-state error to eliminate the role, so combine it with the fuzzy controller form composite controller. By Matlab / Simulink simulation results show that the classical PID control compared to the control in the fast, steady-state aspects of sexual and **N80C186XL16 price** and accuracy greatly improved.

PI control PID control as a typical representative of its simple algorithm, robustness and **N80C186XL16 suppliers** and high reliability, are widely used in industrial process control and motion control. However, the traditional PI control applied to establish accurate mathematical model of deterministic control systems, and most industrial processes to varying degrees, nonlinearity, large delay, parameter variability and model uncertainty, it is difficult to obtain an ordinary PI controller satisfactory control results. Fuzzy control does not require a precise model of the controlled object and adaptable, able to overcome the shortcomings of the traditional PI controller, fuzzy controller can be combined with the PI controller constitutes a complex controller, fuzzy-PI dual-mode control with PI control while steady-state performance and dynamic performance of fuzzy control, play a good control effect.

** 1 dual-mode fuzzy-PI control structure **

** Fuzzy-PI control system consists of dual-mode fuzzy controller (FC) and the PI controller is paralleled by the control switch for mode selection, the structure shown in Figure 1. **

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** Figure 1, dual-mode fuzzy-PI control structure **

** Its working principle is larger when the system error, falls outside a threshold A, it uses fuzzy control to achieve good dynamic performance; when the system error small, falls within the threshold time to use PI steady-state control to achieve better performance. **

** Control switch control rules can be described as: **

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** 2 dual-mode fuzzy-PI control system design **

** 2.1 Selection of the controlled object **

** In control engineering practice, a typical second order system is very common, even for many high-end systems, under certain conditions can be approximated as a second-order system to study. The transfer function of the generalized object systems can be approximately read as: **

** **

** Where K1, K2 is based on changes in control object can take different values ??to simulate the nonlinear characteristics of the system. **

** 2.2 PI controller design **

** Steady state to obtain better control effect, commonly used PI control, which is in the system by adding a proportional amplifier and an integrator. Obtained by parameter tuning PI controller parameters Kp = 0.5, Ki = 8, unit step response curve shown in Figure 2. **

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** unit step response curve of Figure 2 **

** 2.3 Fuzzy Controller Design **

** 2.3.1 to determine the input and output function of several Li **

** Fuzzy controller uses two-dimensional structure to bias and bias rate of change of e ec as the input signal of fuzzy controller, fuzzy controller, fuzzy, fuzzy logic reasoning, ambiguity and a series of operations, the resulting fuzzy control the amount of controller output signal u. Fuzzy inference language input variables E and EC, fuzzy on the domain [-6,6], output fuzzy variables of the domain U, fuzzy on the domain [0,10]. Changes in the actual error e is [-0.5,0.5], the actual deviation of the change rate of change of ec is [-1,1], the actual control input u, output range is [0,10]. So you can determine the deviation e of the quantization factor Ke = 12, error rate of change of the quantitative factors ec Kec = 6, control the volume of the quantitative factor u Ku = 1. The language of the value of the variable E is set to 6, that is {negative big (NB), negative in the (NM), negative small (NS), positive small (PS), middle (PM), CP (PB)}; the variable EC language value is set to 5, that is {negative big (NB), negative small (NS), zero (Z), is small (PS), CP (PB)}; output value of the variable U is set to the language 5, that is {negative big (NB), negative small (NS), zero (Z), is small (PS), CP (PB)}, and set the membership function, as shown in Figure 3, Figure 4 and Figure 5 instructions. **

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** Figure 3 E membership function graph **

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** Figure 4 EC membership function graph **

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** Figure 5 U membership function graph **

** 2.3.2 Fuzzy Rules Design **

** Fuzzy-PI control in dual-mode fuzzy controller is working in the transition process, hoping to speed up the fuzzy control system response speed, the rate of change according to the deviation and the deviation of the different states of engineering personnel and technical knowledge and practical experience, fuzzy rules to establish the appropriate form, get fuzzy control rules shown in Table 1. **

** Table 1 Fuzzy-PI dual-mode control fuzzy control rules **

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** 3 dual-mode fuzzy-PI control system simulation **

** 3.1 Structure of the fuzzy inference system **

** In MATLAB command window, type the command into the fuzzy logic fuzzy research toolbox, in FISEditor window Edit menu to determine the input and output variables of each domain of the scope and shape of membership functions of linguistic variables and other parameters, double-click each icon to edit documents by the fuzzy controller. **

** 3.2 the fuzzy control rules **

** Rules with the Edit menu, open the editor of fuzzy rules to determine "IF ... THEN" form of fuzzy control rules. u There are 30 control rules, each rule weighted value defaults to 1, max-min inference algorithm synthesis method, the solution method by taking the median fuzzy method. The designed fuzzy controller is stored in a user-defined file suffix fis. Create a simulation block diagram **

** 3.3 **

** In the Simulink environment, the fuzzy-PI controller, dual-mode simulation system structure shown in Figure 6, the simulation results shown in Figure 7. **

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** Figure 6 dual-mode controller in the Simulink environment model **

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** Figure 7 Fuzzy-PI dual-mode controller simulation results **

** dual-mode system is stable and the difference lies in the elimination Kp, Ki choice of two parameters, A primary role is to improve the form of front-end simulation curve, which adjust the overshoot and rise time. It can be seen from the simulation results, dual-mode fuzzy-PI control system rise time and maximum overshoot has been reduced in better system performance. **

** 4 Conclusion **

** The proposed dual-mode fuzzy-PI controller is larger when the system error, the fuzzy control to achieve good dynamic performance; When the system is relatively small deviations, the use of PI control to obtain a better steady state performance. By Matlab / Simulink simulation environment, can be seen from the simulation results, compared with a typical PI controller, fuzzy-PI double chess controller can solve the former long rise time, overshoot and large defects. Fuzzy-Pl in the fast mode control system, stability and accuracy are greatly improved terms. **

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