Abstract
A novel iterative learning algorithm is proposed for the identification of linear time-varying (LTV) output-error (OE) systems that perform tasks repetitively over a finite-time interval. Conventional LTV system identification normally relies on recursion algorithms in time domain, which are unable to follow fast changing parameters because of an inevitable estimation lag. To overcome this problem, an extra iteration axis is introduced besides the time axis in the parameter estimation process, and identification algorithm performed in iteration domain is proposed. Firstly, a norm-optimal identification approach is presented to balance the tradeoff between convergence speed and noise robustness. Then a bias compensation algorithm is further proposed to improve the estimation accuracy. Finally, numerical examples are provided to validate the algorithm and confirm its effectiveness. The algorithm is effective to estimate both slow and abrupt parameter changes with high accuracy without estimation lags.
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Acknowledgments
This work was supported by the State Key Program of National Natural Science of China under Grant 51537002, and Chinese National Science Foundation under Grant 51405097.
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Song, F., Liu, Y., Yang, Z., Yang, X., He, P. (2017). Iterative Learning Identification with Bias Compensation for Stochastic Linear Time-Varying Systems. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_24
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DOI: https://doi.org/10.1007/978-981-10-6373-2_24
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