Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Sensing Parameter Selection Strategy for Ultra-low-power Micro-servosystem Identification

초저전력 마이크로 서보시스템의 모델식별을 위한 계측 파라미터 선정 기법

  • Hahn, Bongsu (Department of Applied Robotics, Kyungil University)
  • 한봉수 (경일대학교 로봇응용학과)
  • Received : 2014.05.08
  • Accepted : 2014.05.23
  • Published : 2014.08.01
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Abstract

In micro-scale electromechanical systems, the power to perform accurate position sensing often greatly exceeds the power needed to generate motion. This paper explores the implications of sampling rate and amplifier noise density selection on the performance of a system identification algorithm using a capacitive sensing circuit. Specific performance objectives are to minimize or limit convergence rate and power consumption to identify the dynamics of a rotary micro-stage. A rearrangement of the conventional recursive least-squares identification algorithm is performed to make operating cost an explicit function of sensor design parameters. It is observed that there is a strong dependence of convergence rate and error on the sampling rate, while energy dependence is driven by error that may be tolerated in the final identified parameters.

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References

  1. B. Hahn and K. Oldham, "On-off adaptive controller for low-power micro-robotic step regulation," Asian J. Control, PP, Oct. 2010.
  2. N. Kasagi, Y. Suzuki, and K. Fukagaata, "Microelectromechanical systems-based feedback control of turbulence for skin friction reduction, Annual Rev. Fluid Mech., vol. 41, pp. 231-251, Jan. 2009. https://doi.org/10.1146/annurev.fluid.010908.165221
  3. G. Fantauzzi, "The relation between the sampling time and stochastic error for the impulsive response of linear time-independent systems," IEEE Trans. Automatic Control, vol. 13-4, pp. 426-428, Aug. 1968. https://doi.org/10.1109/TAC.1968.1098945
  4. A. I. Liff and J. K. Wolf, "On the optimum sampling rate for discrete-time modeling of continuous-time systems," IEEE Trans. Automatic Control, vol. 11-2, pp. 288-298, Apr. 1966. https://doi.org/10.1109/TAC.1966.1098317
  5. K. J. Astrom, "On the choice of sampling rates in parametric identification of time series," Inforamtion Sciences, vol. 1-3, pp. 273-278. Jul. 1969. https://doi.org/10.1016/S0020-0255(69)80013-7
  6. N. K. Sinha and S. Puthenpura, " Choice of the sampling interval for the identification of continuous-time systems from samples of input/output data," IEE Proceedings D, vol. 132-6, pp. 263-267, Nov. 1985. https://doi.org/10.1049/ip-d.1985.0046
  7. S. Hillenbrand and M. Pandit, "A discrete-time iterative learning control law with exponential rate of convergence," Proc. 38th Conf. on Decision & Control, Phoenix, Az, pp. 1575-1580, Dec. 1999.
  8. A.-C. Lee and J.-H Chen, "Optimal sampling in the identification of a randomly excited structural system," Mechanical Systems and Signal Processing, vol. 6, no. 4, pp. 329-344, 1992. https://doi.org/10.1016/0888-3270(92)90035-H
  9. National Semiconductior, "Select by operating voltage and supply current," Available: www.national.com/en/amplifiers.