References
- Aziz, M. H. R. A., Mohd-Mokhtar, R., & Wang, L. P. (2013). Identification of step response estimates utilizing continuous time subspace approach. Journal of Process Control, 23(3), 254–270. https://doi.org/https://doi.org/10.1016/j.jprocont.2012.12.002
- Bastogne, T., Garnier, H., & Sibille, P. (2001). A PMF-based subspace method for continuous time model identification. Application to a multivariable winding process. International Journal of Control, 74(2), 118–132. https://doi.org/https://doi.org/10.1080/00207170150203471
- Bergamasco, M., & Lovera, M. (2011). Continuous-time predictor-based subspace identification using Laguerre filters. IET Control Theory and Applications, 5(7), 856–867. https://doi.org/https://doi.org/10.1049/iet-cta.2010.0228
- Cheng, P. Y., Chen, J., Ljung, L., & Verhaegen, M. (2017). Subspace identification of continuous-time models using generalized orthonormal bases. IFAC System Identification, 5280–5285. https://doi.org/http://doi.org/10.1109/CDC.2017.8264440
- Chiuso, A. (2010). On the asymptotic properties of closed-Loop CCA-type subspace algorithms: equivalence results and role of the future horizon. IEEE Transactions on Automatic Control, 55(3), 634–649. https://doi.org/https://doi.org/10.1109/TAC.2009.2039239
- Garnier, H., Gilson, M., & Zheng, W. X. (2000). A bias-eliminated least-squares method for continuous-time model identification of closed-loop systems. International Journal of Control, 73(1), 38–48. https://doi.org/https://doi.org/10.1080/002071700219920
- Garnier, H., Soderstrom, T., & Yuz, J. I. (2011). Editorial special issue on continuous-time model identification. IET Control Theory & Applications, 5(7), 839–841. https://doi.org/https://doi.org/10.1049/iet-cta.2011.9043
- Garnier, H., & Wang, L. (2008). Identification of continuous-time models from sampled data. Springer.
- Gunes, B., Wingerden, J. W. V., & Verhaegen, M. (2018). Tensor nuclear norm LPV subspace identification. IEEE Transactions on Automatic Control, 63(11), 3897–3903. https://doi.org/https://doi.org/10.1109/TAC.2018.2800772
- Gunes, B., Wingerden, J. W. V., & Verhaegen, M. (2020). Tensor networks for MIMO LPV system identification. International Journal of Control, 93(4), 797–811. https://doi.org/https://doi.org/10.1080/00207179.2018.1501515
- Haverkamp, B. R. J., Verhaegen, M., Chou, C. T., & Johansson, R. (1997). Continuous-time subspace model identification method using laguerre filtering. IFAC System Identification, 1093–1098. https://doi.org/https://doi.org/10.1016/S1474-6670(17)42986-7
- Hou, J., Liu, T., & Wang, Q. G. (2019). Subspace identification of Hammerstein-type nonlinear systems subject to unknown periodic disturbance. International Journal of Control, 94(4), 849–859. https://doi.org/https://doi.org/10.1080/00207179.2019.1621385
- Houtzager, I., Wingerden, J. W. V., & Verhaegen, M. (2012). Recursive predictor-based subspace identification with application to the real-time closed-loop tracking of flutter. IEEE Transactions on Automatic Control, 20(4), 934–949. https://doi.org/https://doi.org/10.1109/TCST.2011.2157694
- Hu, Y. S., Fan, Y., Wei, Y. H., & Wang, Y. (2015). Subspace-based continuous-time identification of fractional order systems from non-uniformly sampled data. International Journal of Systems Science, 47(1), 122–134. https://doi.org/https://doi.org/10.1080/00207721.2015.1029568
- Hu, Y. S., Jiang, Y. F., & R. A. D. Callafon (2020). Variance reduction in covariance based realization algorithm with application to closed-loop data. Automatica, 113(28), 108683. https://doi.org/https://doi.org/10.1016/j.automatica.2019.108683
- Huang, B., Ding, S. X., & Qin, S. J. (2005). Closed-loop subspace identification: An orthogonal projection approach. Journal of Process Control, 15(1), 53–66. https://doi.org/https://doi.org/10.1016/j.jprocont.2004.04.007
- Katayama, T., & Tanaka, H. (2007). An approach to closed-loop subspace identification by orthogonal decomposition. Automatica, 43(9), 1623–1630. https://doi.org/https://doi.org/10.1016/j.automatica.2007.02.011
- Kheradmandi, M., & Mhaskar, P. (2018). Model predictive control with closed-loop re-identification. Computers and Chemical Engineering, 109(7), 249–260. https://doi.org/https://doi.org/10.1016/j.compchemeng.2017.11.016
- Klein, V., & Morelli, E. A. (2006). Aircraft and rotorcraft system identification: Engineering methods with flight-test examples. AIAA.
- Li, K., Luo, H., Yang, C. M., & Yin, S. (2020). Subspace-aided closed-loop system identification with application to DC motor system. IEEE Transactions on Industrial Electronics, 67(3), 2304–2313. https://doi.org/https://doi.org/10.1109/TIE.41
- Li, W. H., Raghavan, H., & Shah, S. (2003). Subspace identification of continuous time models for process fault detection and isolation. Journal of Process Control, 13(5), 407–421. https://doi.org/https://doi.org/10.1016/S0959-1524(02)00066-5
- Mensler, M. H. R. A., Joe, R., & Kawabe, L. P. (2006). Identification of a toroidal continuously variable transmission using continuous-time system identification methods. Control Engineering Practice, 14(1), 45–58. https://doi.org/https://doi.org/10.1016/j.conengprac.2005.01.001
- Middleton, R. H., & Goodwin, G. C. (1990). Digital control and estimation-a unified approach. Prentice-Hall.
- Noël, J. P., Marchesiello, S., & Kerschen, G. (2014). Subspace-based identification of a nonlinear spacecraft in the time and frequency domains. Mechanical Systems and Signal Processing, 43(1–2), 217–236. https://doi.org/https://doi.org/10.1016/j.ymssp.2013.10.016
- Ohsumi, A., Kameyama, K., & Yamaguchi, K. I. (2002). Subspace identification for continuous-time stochastic systems via distribution-based approach. Automatica, 38(1), 63–79. https://doi.org/https://doi.org/10.1016/S0005-1098(01)00190-X
- Overschee, P. V., & Moor, B. D. (1996). Subspace identification for linear systems: Theory, implementation, applications [Ph.D. thesis]. Katholieke Universiteit Leuven.
- Shang, L. L., Liu, J. C., & Zhang, Y. W. (2016). Recursive fault detection and identification for time-varying processes. Industrial & Engineering Research, 55(46), 12149–12160. https://doi.org/https://doi.org/10.1021/acs.iecr.6b02653
- Sinha, N. K., & Rao, G. P. (1991). Identification of continuous-time systems. Kluwer Academic Publishers.
- Thornhill, N. F., Patwardhan, S. C., & Shah, S. L. (2008). A continuous stirred tank heater simulation model with applications. Journal of Process Control , 18(3–4), 347–360. https://doi.org/https://doi.org/10.1016/j.jprocont.2007.07.006
- Tischler, M., & Remple, R. (2006). Aircraft and rotorcraft system identification: Engineering methods with flight-test examples. AIAA.
- Varanasi, S. K., & Jampana, P. (2020). Nuclear norm subspace identification of continuous time state-space models with missing outputs. Control Engineering Practice, 95(7), 104239. https://doi.org/https://doi.org/10.1016/j.conengprac.2019.104239
- Veen, G. V. D., Wingerden, J. W. V., Bergamasco, M., Lovera, M., & Verhaegen, M. (2013). Closed-loop subspace identification methods: An overview. IET Control Theory & Applications, 7(10), 1339–1358. https://doi.org/https://doi.org/10.1049/cth2.v7.10
- Wang, J., & Qin, S. J. (2006). Closed-loop subspace identification using the parity space. Automatica, 42(2), 315–320. https://doi.org/https://doi.org/10.1016/j.automatica.2005.09.012
- Wang, Y. Q., Zhang, L., & Zhao, Y. L. (2018). Improved closed-loop subspace identification with prior information. International Journal of Systems Science, 49(9), 1821–1835. https://doi.org/https://doi.org/10.1080/00207721.2018.1460409
- Wingerden, J. W. V., & Verhaegen, M. (2009). Subspace identification of Bilinear and LPV systems for open- and closed-loop data. Automatica, 45(2), 372–381. https://doi.org/https://doi.org/10.1016/j.automatica.2008.08.015
- Yu, M., & Liu, J. C. (2018). Nuclear norm-based recursive subspace prediction of time-varying continuous-time stochastic systems via distribution theory. Journal of the Franklin Institute, 355(17), 8830–8856. https://doi.org/https://doi.org/10.1016/j.jfranklin.2018.09.020
- Yu, M., Liu, J. C., & Wang, H. H. (2018). Nuclear norm subspace identification for continuous-time stochastic systems based on distribution theory method. ISA Transactions, 83(1), 165–175. https://doi.org/https://doi.org/10.1016/j.isatra.2018.08.014
- Yu, M., Liu, J. C., & Zhao, L. C. (2020). Nuclear norm subspace system identification and its application on a stochastic model of plague. Journal of Systems Science and Complexity, 33(1), 43–60. https://doi.org/https://doi.org/10.1007/s11424-019-8003-9
- Zhang, G. W., Tang, B. P., & Tang, G. W. (2012). An improved stochastic subspace identification for operational modal analysis. Measurement, 45(5), 1246–1256. https://doi.org/https://doi.org/10.1016/j.measurement.2012.01.012
- Zhang, L., Zhou, D. H., Zhong, M. Y., & Wang, Y. Q. (2019). Improved closed-loop subspace identification based on principal component analysis and prior information. Journal of Process Control, 80(4), 235–246. https://doi.org/https://doi.org/10.1016/j.jprocont.2019.06.001