logo

SCIENCE CHINA Information Sciences, Volume 63, Issue 1: 112201(2020) https://doi.org/10.1007/s11432-018-9845-6

Prediction-based event-triggered identification of quantized input FIR systemswith quantized output observations

More info
  • ReceivedOct 24, 2018
  • AcceptedMar 4, 2019
  • PublishedDec 16, 2019

Abstract

This paper addresses the identification of finite impulse response (FIR) systems withboth quantized and event-triggered observations.An event-triggered communication scheme for the binary-valued output quantization is introducedto save communication resources.Combining the empirical-measure-based identification techniqueand the weighted least-squares optimization,an algorithm is proposed to estimate the unknown parameterby full use of the received data and thenot-triggered condition.Under quantized inputs,it is shown that the estimate can strongly converge to the real valuesand the estimator is asymptotically efficient in terms of the Cramér-Rao lowerbound.Further, the limit of the average communication rate is derived and the tradeoff between this limitand the estimation performance is discussed.Moreover, the case of multi-threshold quantized observations is considered.Numerical examples are included to illustrate the obtained main results.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No. 61773054).


References

[1] Söderström T, Stoica P. System Identification. Upper Saddle River: Prentice Hall, 1989. Google Scholar

[2] Wang L Y, Zhao W X. System Identification: New Paradigms, Challenges, and Opportunities. Acta Automatica Sin, 2013, 39: 933-942 CrossRef Google Scholar

[3] Akyildiz I F, Su W, Sankarasubramaniam Y. Wireless sensor networks: a survey. Comput Networks, 2002, 38: 393-422 CrossRef Google Scholar

[4] Hespanha J P, Naghshtabrizi P, Xu Y. A Survey of Recent Results in Networked Control Systems. Proc IEEE, 2007, 95: 138-162 CrossRef Google Scholar

[5] Guo J, Mu B, Wang L Y. Decision-Based System Identification and Adaptive Resource Allocation. IEEE Trans Automat Contr, 2017, 62: 2166-2179 CrossRef Google Scholar

[6] Ma C Q, Li T, Zhang J F. Consensus control for leader-following multi-agent systems with measurement noises. J Syst Sci Complex, 2010, 23: 35-49 CrossRef Google Scholar

[7] Ma C Q, Zhang J F. On formability of linear continuous multi-agent systems. Journal of Systems Science and Complexity, 2012, 25(1): 13-29 doi: 10.1007/sl1424-012-0108-3. Google Scholar

[8] Aström K J, Bernhardsson B M. Comparison of Riemann and Lebesgue sampling for first order stochastic systems. In: Proceedings of the 41st IEEE conference on decision and control, Las Vegas, 2002. Google Scholar

[9] Wang A, Liao X, Dong T. Event-triggered gradient-based distributed optimisation for multi-agent systems with state consensus constraint. IET Control Theory Appl, 2018, 12: 1515-1519 CrossRef Google Scholar

[10] Shi D, Chen T, Shi L. On Set-Valued Kalman Filtering and Its Application to Event-Based State Estimation. IEEE Trans Automat Contr, 2015, 60: 1275-1290 CrossRef Google Scholar

[11] Hetel L, Fiter C, Omran H. Recent developments on the stability of systems with aperiodic sampling: An overview. Automatica, 2017, 76: 309-335 CrossRef Google Scholar

[12] Wang A, Liao X, Dong T. Event-driven optimal control for uncertain nonlinear systems with external disturbance via adaptive dynamic programming. Neurocomputing, 2018, 281: 188-195 CrossRef Google Scholar

[13] Wang A, Dong T, Liao X. Event-triggered synchronization strategy for complex dynamical networks with the Markovian switching topologies.. Neural Networks, 2016, 74: 52-57 CrossRef PubMed Google Scholar

[14] Yu Y G, Zeng Z W, Li Z K. Event-triggered encirclement control of multi-agent systems with bearing rigidity. Sci China Inf Sci, 2017, 60: 110203 CrossRef Google Scholar

[15] Zheng C, Li L, Wang L. How much information is needed in quantized nonlinear control?. Sci China Inf Sci, 2018, 61: 092205 CrossRef Google Scholar

[16] Wang L Y, Zhang J F, Yin G G. System identification using binary sensors. IEEE Trans Automat Contr, 2003, 48: 1892-1907 CrossRef Google Scholar

[17] Wang T, Tan J W, Zhao Y L. Asymptotically efficient non-truncated identification for FIR systems with binary-valued outputs. Sci China Inf Sci, 2018, 61: 129208 CrossRef Google Scholar

[18] Guo J, Wang L Y, Yin G. Asymptotically efficient identification of FIR systems with quantized observations and general quantized inputs. Automatica, 2015, 57: 113-122 CrossRef Google Scholar

[19] Zhao Y, Wang L Y, Yin G G. Identification of Wiener systems with binary-valued output observations. Automatica, 2007, 43: 1752-1765 CrossRef Google Scholar

[20] Guo J, Wang L Y, Yin G. Identification of Wiener systems with quantized inputs and binary-valued output observations. Automatica, 2017, 78: 280-286 CrossRef Google Scholar

[21] Casini M, Garulli A, Vicino A. Input design in worst-case system identification with quantized measurements. Automatica, 2012, 48: 2997-3007 CrossRef Google Scholar

[22] Zhao Y, Bi W, Wang T. Iterative parameter estimate with batched binary-valued observations. Sci China Inf Sci, 2016, 59: 052201 CrossRef Google Scholar

[23] Goudjil A, Pouliquen M, Pigeon E. Identification of systems using binary sensors via Support Vector Machines. In: Proceedings of IEEE 54th Annual Conference on Decision and Control, Osaka, 2015. Google Scholar

[24] Chow Y S, Teicher H. Probability Theory: Independence, Interchangeability, Martingales. 2rd ed. New York: Springer-Verlag, 1997. Google Scholar

[25] Wang L Y, Yin G G. Asymptotically efficient parameter estimation using quantized output observations. Automatica, 2007, 43: 1178-1191 CrossRef Google Scholar

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1