Figure 12Modeling error when the optional parameters have different values (using the variable step-size recursive least square estimation algorithm).Just as expected, Figure 12 displays that when the quantity of discretization lines L is elevated or even the step-size l is lowered, the modeling error is reduced. considering When L = 20 and l = one, the utmost modeling error is often diminished to 0.001mm.The experiment outcomes within this part can confirm the validity of your proposed hysteresis model within this paper, and in the comparison with the simulation success in the two identification algorithms, it may possibly be viewed that the variable step-size recursive least square estimation algorithm includes a greater functionality from the identification, plus the modeling accuracy is often adjusted by adjusting the optional parameters within the proposedselleck model.
5. ConclusionA mathematic modeling approach primarily based on KP model is proposed to describe the hysteresis of the MSMA actuator within this paper. To be able to determine the weighting parameters from the KP model, an enhanced gradient correction algorithm plus a variable step-size recursive least square estimation algorithm are adopted, and the simulation final results have proven the validity of your proposed hysteresis model. By adjusting the optional parameters of this hysteresis model, the modeling accuracy might be even more improved, and while in the simulation experiment, the modeling error might be diminished to 0.001mm together with the variable step-size recursive least square estimation algorithm. A satisfactory hysteresis model might be gotten if your optional Albendazole Oxideparameters are adjusted according as the necessities of modeling accuracy and computational complexity.
AcknowledgmentsThis do the job is partially supported by the Nationwide Natural Science Basis of China, Grant no. 51105170 and Program of Science and Engineering Development Prepare of Jilin province of China, Grant no. 20115015.
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