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MATLAB-assisted fuzzy control system design and simulation to optimize the application of research


) = 0.65 * k2 * u (2); sys (3) = k3 +2.6; else / * other cases * / sys (1) = 0.9 * k1 * u (1); sys ( 2) = 0.8 * k2 * u (2); sys (3) = k3; endOtIlerwisesys = []; end to save the Sregulafion. m files. Then drag the library from the SIMULINK S. function module, double-click pop-up dialog box shown in Figure 6, double-click the middle of the module, in the s. FunctionDame fill entry has 78 * Zhongyuan Institute of Technology Paul 20O4 Vol 15 save over the file name Sregulation. Package was shown in Figure 7 has two input terminals and three output terminals of the new module. Figure 7 Figure 6S function module package of new module parameters self-tuning fuzzy control regulation is not only a short time, the system response speed, but also in the overshoot and interference are better than ordinary fuzzy controller. Than ordinary fuzzy controller has better dynamic performance and steady precision. Add these new modules, that is to be self-adjusting fuzzy controller parameters of the simulation module Figure 8. Part of a second-order system without delay into the 4 {I-line simulation results obtained response curve shown in Figure 9. Simulation curve shows that the use of Figure 8 parameters self-adjusting fuzzy controller simulation model of the fuzzy inference output surface Figure 9 this by writing S function,herve leger toronto, the conventional fuzzy control algorithm for optimization, while under the SIMULINK environment to build complex non-linear system, optimized fuzzy controller effectiveness simulation. Basically, do not write another control algorithm and simulation program. Than c / c + + program simulation simpler and more complex to avoid in the past a lot of work, and strong visual resistance. And then design a reasonable structure, better performance of the fuzzy controller. References: [1] Zhang Guoliang, Zeng Jing. Application of Fuzzy Control and MATLAB [M]. Xi'an: Xi'an Jiaotong University Press, 2OO2.11. [2] Wu Xiaoli, Jer-Huei Lin. MATLAB auxiliary fuzzy system design [M]. Xi'an: Xidian University Press, 2OO2.8. [3] Xue Dingyu. Control System Computer Aided Design - MArl1AB language and application [M]. Beijing: Tsinghua University Press,Beats By Dre Headphones, 1996. [4] Rowan wide,herve leger sale, Han Junfeng, Li Lan red. Based on MATLAB (SIMUHNK) language and efficient simulation of fuzzy control system [J]. Computer Simulation, 2001,18 (3) :14-16. TheStudyofApplicationofMATLAB'SFuzzyLogicToolboxon SimulationandOptimumDesignofFuzzyControlZHANGYi, WANGDing-yuan, DENGLin (1.ShenhuoCorporation,vibram five fingers outlet, Yongcheng476600; 2.ZhongyuanInstituteofTechnology, Zhengzhou450007; 3.YongneiCorporation, Yongcheng476600, China) Abstract: Thispaperintroducesameansofsim ~ ationandoptimumdesignofcommonfuzzylogiccontrolsyst emwithMATLAB'stoolbox. ThroughtheorganiccombinationofMALABandsimulation, werealizedthedesignandsimulationofpa-rametersselfregulationoffuzzycontrolsystembycompil ingS-function. Itprovidesanefficientsimulationmethodforthedeformi ntelligentalgorithmoffuzzyContro1. Keywords: MATLAB; S-function; parametersselfregulation; simulation
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