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Cutting issues of quantitative dynamic weighing solutions

Segment linearization approximation of the nonlinear time-varying system as a linear system of many small period of time, so in equation (11) k can not take infinite, but when k takes a large integer values (11) similar to set up, we can see from (12) on the intestine (z) the formula is correct. Thus,[link widoczny dla zalogowanych], the calculation (f.) Problem is transformed into a model parameter identification problem. Model shown in Figure 3. Process we can see from the above formula, using this dynamic weighing method has the following advantages: (1) does not consider the impact of materials falling, the system tracking error, blank material quality. (2) measurements independent of the initial state of the system. Therefore,[link widoczny dla zalogowanych], without regard to tare the scale body. (3) as shown in Figure 3, the process parameter model using the least square method, the interference filter so much noise. Identification of model parameters prince Yu 3 3 Because of the time-varying nonlinear equations using piecewise linear approach,[link widoczny dla zalogowanych], but the actual model will change over time, so the least squares algorithm with limited memory, so that over time, continue to add new information, discarding the old information. Due to real-time data processing, real-time calculation of the current cutting speed, but also to accelerate the calculation speed and reduce the computation, so the use of recursive algorithm for online parameter identification. Use hyperbolic Householder transformation least squares restricted memory, which has the following advantages: (1) hyperbolic Householder transform numerical stability of the sensitivity of the data error is smaller. (2) calculation of clear, concise, easy data updates. (3) hyperbolic Householder transformation can be achieved in parallel computing. Measurement of July 1998 (4) the use of hyperbolic Householder transform the camera did not start the process of small squares. 4 Results According to the measurement program, conducted a prototype design and experimental. Feeding amount of quantification were 5kg,[link widoczny dla zalogowanych], 10kg, 20kg, cutting speed is about lkg / s. After several experiments,[link widoczny dla zalogowanych], the absolute error is less than quantitative cutting 25g. Experimental data in Table 1. The results show that. In this paper, the dynamic weighing method is feasible. Improve the quantitative accuracy and speed of weighing. Table 1 Quantitative experimental error of cutting dead weight table (error unit: g) Note: The experimental plan difference = actual weight value of a setting l material value. Actual weight value is weighed after each visit to the system by static weighing scale calibration value obtained. Weighing the active particles embedded object: Reference l Junqin. Random signal processing. Beijing: Beijing Aeronautics and Astronautics Press. 19902 Shu Wei group. Chih-based re-estimation method for dynamic measurement of, 1993; 14 (2): 1493 Yao-dry. System identification Beijing: Beijing Aeronautics and Astronautics Press. 1990DynamicWeighingSolutionforPredeterminedFillingMateriaZhangHaiqing, (DeptofAutomaticControl, BingUnivLiBaoan.LuoXianheotAeronauticssadAstronautics.Beijing100083) Abstract - ThispaperbringsOutthesolutiontothefixquantitydumpingproblemThepmb-lemisanalysizedindetails. Itsmathematicmodelandparametermodelareestab [ishedThenthemethodmeasuringthedumpingmaterialweightbydumpingrateispresentedTheformulaofdumpingrateisinfetedandthedumpingrateiscalculatedbyhyperbolicHou ~ holderRIS. Thesstm 【piemachineisdesignedandtested. Theresultsshowthatthesolutioniscorrectand12apleKeywords: Dynamicweighing; Signalprocessing; Systemindentify

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