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Study On Inverse Fuzzy Model Based Adaptive Inverse Control And Its Applications

Posted on:2013-07-07Degree:MasterType:Thesis
Country:ChinaCandidate:Y LiuFull Text:PDF
GTID:2248330392454656Subject:Control theory and control engineering
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Adaptive inverse control(AIC) method was named and proposed by professorWidrow in1986. The basic idea of AIC is to identify the inverse model of the controlledobject with adaptive filtering theory and take the inverse model as the controller in serieswith the plant. The output of the inverse model is used to drive the the controlled plant andthe dynamic characteristics is controlled. Thus, AIC doesn’t need to know the exactmathematical model of the controlled object and can design the control system with littleprior knowledge. The nonlinear adaptive filter is the basis of nonlinear adaptive inversecontrol and currently there doesn’t exist a clear mechanism to choose the approriatenonlinear adaptive filter. Fuzzy system has universal approximation and can takeadvantage of data and language information at the same time. It possesses thecharacteristics of self-learning, adaptive and data fusion, and provides an effectivemodeling way for stiff or complex systems which can’t be expressed by accuratemathematical model. So, fuzzy system is very suitable to be used as the nonlinear adaptivefilter of nonlinear AIC. The main subject of this paper is the AIC based on fuzzy model.The main study content states as follow:Firstly, an overview of the research significance and sources of the subject was given.The development process and the research status of AIC and fuzzy inverse model wasanalysed, in addition, the basic structure of the AIC was described in this part. This laysthe foundation for the later study contents of subsequent chapters. Lately, the basicknowledge of the fuzzy modeling theory was introduced, including the traditional typeⅠfuzzy modeling method and type-II fuzzy modeling method. What’s more, an systemicanalysis of input variables selection problem in fuzzy modeling was done.Secondly, the model reference adaptive inverse control based on T-S fuzzy modelwas proposed for the single input single output nonlinear system. By use of the diagonaldivision and recursive least squares method, the number of the fuzzy rules was subatractedand the learning process was simplified. Considering the existing real-time disturbance forthe controlled object, the disturbance canceller was introduced to the system and thedisturbances were suppressed effectively. The simulation results demonstrate the rapid convergence of the method and the strong noisy.Then, given that the typeⅠfuzzy system exists limitation in dealing with uncertaintyproblems, interval type-II T-S fuzzy system based adaptive inverse control method wasproposed in the paper. With the use of type-II fuzzy set, type-II fuzzy system can providemore adjustable parameters and design freedom and is much more powerful to handleuncertainties and nonlinearities directly. By comparing to the adaptive inverse controlbased on traditional type-I fuzzy system, the validity and superiority of the proposedmethod was verified.Finally, given that it is difficult to achieve the mechanism model of the pneumaticloading system, a kind of smart online modeling method based on fuzzy identificationtheory was adopted to model the pneumatic loading system. What’s more, the adaptiveinverse control method based on fuzzy model was applied to the pressure control of apneumatic loading system and has been compared with the traditional PI control scheme,the experimental results show that the fuzzy adaptive inverse control achieved goodcontrol effect.
Keywords/Search Tags:Adaptive inverse control, Inverse fuzzy model, Type-Ⅱ fuzzy system, LMSalgorithm, Disturbance canceller, Pneumatic loading system
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