Download Advanced Control of Piezoelectric Micro-/Nano-Positioning by Qingsong Xu, Kok Kiong Tan PDF

By Qingsong Xu, Kok Kiong Tan

This publication explores rising equipment and algorithms that let distinct keep an eye on of micro-/nano-positioning platforms. The textual content describes 3 keep an eye on concepts: hysteresis-model-based feedforward keep an eye on and hysteresis-model-free suggestions keep an eye on in line with and loose from country commentary. each one paradigm gets committed realization inside a selected a part of the text.

Readers are proven how one can layout, validate and practice various new keep watch over techniques in micromanipulation: hysteresis modelling, discrete-time sliding-mode keep an eye on and model-reference adaptive regulate. Experimental effects are supplied all through and building up to an in depth remedy of sensible purposes within the fourth a part of the ebook. The functions concentrate on regulate of piezoelectric grippers.

Advanced keep an eye on of Piezoelectric Micro-/Nano-Positioning Systems will support educational researchers and practicing regulate and mechatronics engineers drawn to suppressing assets of nonlinearity reminiscent of hysteresis and flow whilst combining place and strength keep watch over of precision platforms with piezoelectric actuation.

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3 Hysteresis Modeling The purpose of hysteresis modeling is to capture the hysteresis behavior of the system. First, the hysteresis is modeled using the popular Bouc–Wen, MPI, and LSSVM models. In addition, the corresponding inverse hysteresis models are also derived for the purpose of hysteresis compensation. 1 Hysteresis Modeling with the Bouc–Wen Model Thanks to a fewer number of parameters, Bouc–Wen model has been widely employed in piezoelectric hysteresis modeling. 2) where t is the time variable; parameters m, b, k, and y represent the mass, damping coefficient, stiffness, and displacement response of the piezostage, respectively; d is the piezoelectric coefficient; u denotes the input voltage; and h indicates the hysteretic loop in terms of displacement whose magnitude and shape are determined by parameters α, β, γ , and the order n, with n governing the smoothness of the transition from elastic to plastic response.

In addition, Fig. 12b exhibits that the LSSVM model errors are more uniformly distributed in comparison with Bouc–Wen and MPI model results as shown in Figs. 10b, respectively. Relatively, the LSSVM model errors are not dependent on neither the amplitude nor the frequency of the input signals. Therefore, the trained LSSVM model captures the amplitudeand rate-dependent hysteresis accurately. 5 −2 0 2 4 Time (s) Fig. 12 Results of the trained LSSVM model. a Experimental result and LSSVM model output.

Struct. 19(7), 845–857 (2008) 48. : Stable control of an electro-hydraulic actuator during contact tasks: Theory and experiments. In: Proceeding of the American Control Conference, pp. 4114– 4118 (2000) 49. : Adaptive sliding-mode neuro-fuzzy control of the two-mass induction motor drive without mechanical sensors. IEEE Trans. Ind. Electron. 57(2), 553–564 (2010) References 19 50. : Miniature compliant grippers with vision-based force sensing. IEEE Trans. Robot. 26(5), 867–877 (2010) 51. : Hybrid microassembly combining robotics and water droplet self-alignment.

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