The advantages of directly identifying continuous-time transfer function models in practical applications

References

• Ahmed, S., Huang, B., & Shah, S. (2006). Parameter and delay estimation of continuous-time models using a linear filter. Journal of Process Control, 16, 323–331.
   Web of Science ®Google Scholar
• Akaike, H. (1969). Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics, 21, 243–247.
   Web of Science ®Google Scholar
• Akaike, H. (1974). A new look at statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.
   Web of Science ®Google Scholar
• Aström, K.J. (1969). On the choice of sampling rates in parametric identification of time series. Information Science, 1, 273–278.
   Web of Science ®Google Scholar
• Aström, K. (1970). Introduction to stochastic control theory. New York: Academic Press.
   Google Scholar
• Aström, K., & Bernhardsson, B. (2003). Systems with Lebesgue sampling. In A. Rantzer & C.I. Byrnes (Eds.), Directions in mathematical systems theory and optimization (Vol. pp. 1–13). Berlin/Heidelberg: Springer-Verlag.
   Google Scholar
• Aström, K., Hagander, P., & Sternby, J. (1984). Zeros of sampled systems. Automatica, 20, 31–38.
   Web of Science ®Google Scholar
• Barel, M.V., & Bultheel, A. (1994). Discrete linearized least-squares rational approximation on the unit circle. Journal of Computational and Applied Mathematics, 50, 545–563.
   Web of Science ®Google Scholar
• Beven, K.J., Smith, P.J., & Wood, A. (2011). On the colour and spin of epistemic error (and what we might do about it). Hydrology and Earth System Science, 15, 3123–3133.
   Web of Science ®Google Scholar
• Box, G., & Jenkins, G. (1970). Time series analysis: Forecasting and control. San Francisco: Holden-Day.
   Google Scholar
• Bultheel, A., & Barel, V. (1995). Vector orthogonal polynomials and least squares approximation. SIAM Journal on Matrix Analysis and Applications, 16, 863–885.
   Web of Science ®Google Scholar
• Cellier, F. (1991). Continuous system modelling. New York: Springer-Verlag.
   Google Scholar
• Garnier, H. (2013). Continuous-time model identification of stiff systems from rapidly sampled time data ( Technical report). Centre de Recherche en Automatique de Nancy, University of Lorraine.
   Google Scholar
• Garnier, H., Gilson, M., Bastogne, T., & Mensler, M. (2008). CONTSID toolbox: A software support for continuous-time data-based modelling. In H. Garnier & L. Wang (Eds.), Identification of continuous-time models from sampled data (pp. 249–290). London: Springer.
   Google Scholar
• Garnier, H., Gilson, M., Young, P.C., & Huselstein, E. (2007). An optimal IV technique for identifying continuous-time transfer function model of multiple input systems. Control Engineering Practice, 46, 471–486.
   Web of Science ®Google Scholar
• Garnier, H., Mensler, M., & Richard, A. (2003). Continuous-time model identification from sampled data. Implementation issues and performance evaluation. International Journal of Control, 76, 1337–1357.
   Web of Science ®Google Scholar
• Garnier, H., & Wang, L. (Eds.). (2008). Identification of continuous-time models from sampled data. London: Springer-Verlag.
   Google Scholar
• Garnier, H., Wang, L., & Young, P.C. (2008). Direct identification of continuous-time models from sampled data: Issues, basic solutions and relevance. In H. Garnier & L. Wang (Eds.), Identification of continuous-time models from sampled data (pp. 1–30). London: Springer-Verlag.
   Google Scholar
• Gilson, M., & Garnier, H. (2003, August). Continuous-time model identification of systems operating in closed-loop. In P. Van den Hof, B. Wahlberg & S. Weiland (Eds.), 13th IFAC Symposium on System Identification (SYSID’2003) (pp. 425–430). Rotterdam, The Netherlands: IFAC.
   Google Scholar
• Gilson, M., Garnier, H., Young, P., & Van den Hof, P. (2008). Instrumental variable methods for closed-loop continuous-time model identification. In H. Garnier & L. Wang (Eds.), Identification of continuous-time models from sampled data (pp. 133–160). London: Springer.
   Google Scholar
• Gilson, M., Garnier, H., Young, P.C., & Van den Hof, P. (2011). Optimal instrumental variable method for closed-loop identification. IET Control Theory & Applications, 5, 1147–1154.
   Web of Science ®Google Scholar
• Gilson, M., Welsh, J., & Garnier, H. (2013, June). Frequency-domain instrumental variable based method for wide band system identification. In American Control Conference (ACC’2013). Washington, DC: IEEE.
   Google Scholar
• Goodwin, G., Yuz, J., & Garnier, H. (2005, June). Robustness issues in continuous-time system identification from sampled data. In 16th Triennial IFAC World Congress on Automatic Control. Prague, Czech Republic: IFAC.
   Google Scholar
• Jakeman, A., & Young, P.C. (1979). Refined instrumental variable methods of time-series analysis: Part II, Multivariable systems. International Journal of Control, 29, 621–644.
   Web of Science ®Google Scholar
• Johansson, R. (1994). Identification of continuous-time models. IEEE Transactions on Signal Processing, 42, 887–896.
   Web of Science ®Google Scholar
• Kristensen, N., Madsen, H., & Jorgensen, S. (2004). Parameter estimation in stochastic grey-box models. Automatica, 40, 225–237.
   Web of Science ®Google Scholar
• Larsson, E., Mossberg, M., & Söderström, T. (2006). An overview of important practical aspects of continuous-time ARMA system identification. Circuits Systems Signal Processing, 25, 17–46.
   Web of Science ®Google Scholar
• Laurain, V., Gilson, M., & Garnier, H. (2012). Refined instrumental variable methods for Hammerstein Box-Jenkins models. In L. Wang & H. Garnier (Eds.), System identification, environmental modelling, and control system design (pp. 27–48). London: Springer-Verlag.
   Google Scholar
• Laurain, V., Gilson, M., Toth, R., & Garnier, H. (2010). Refined instrumental variable methods for identification of LPV Box-Jenkins models. Automatica, 46, 959–967.
   Web of Science ®Google Scholar
• Laurain, V., Toth, R., Gilson, M., & Garnier, H. (2011). Direct identification of continuous-time linear parameter-varying input/output models. IET Control Theory & Applications, 5, 878–888.
   Web of Science ®Google Scholar
• Linden, J.G., Young, P.C., Larkowski, T., & Burnham, K.J. (2009). Identification of a catchment model via errors-in-variables approaches – a preliminary study. In Proceedings of 20th International Conference on Systems Engineering (pp. 312–320). Coventry, UK: Coventry University.
   Google Scholar
• Littlewood, I.G., Young, P.C., & Croke, B.F.W. (2010). Preliminary comparison of two methods for identifying rainfall–streamflow model parameters insensitive to data time-step: The Wye at Cefn Brwyn, Plynlimon, Wales. In C. Walsh (Ed.), Role of hydrology in managing consequences of a changing global environment (pp. 539–543). University of Newcastle, UK: British Hydrological Society.
   Google Scholar
• Ljung, L. (1999). System identification: Theory for the user (2nd ed.). Upper Saddle River: Prentice Hall.
   Google Scholar
• Ljung, L. (2003, September). Initialisation aspects for subspace and output-error identification methods. In European Control Conference (ECC’2003). Cambridge, UK: IFAC.
   Google Scholar
• Ljung, L. (2009, July). Experiments with identification of continuous-time models. In 15th IFAC Symposium on System Identification (SYSID’2009). Saint-Malo, France: IFAC.
   Google Scholar
• Ljung, L., & Singh, R. (2012, July). Version 8 of the system identification toolbox. In 16th IFAC Symposium on System Identification (SYSID’2012). Brussels, Belgium: IFAC.
   Google Scholar
• Mahata, K., & Garnier, H. (2006). Identification of continuous-time errors-in-variables models. Automatica, 42, 1477–1490.
   Web of Science ®Google Scholar
• Mahata, K., & Garnier, H. (2007). Identification of continuous-time Box-Jenkins models with arbitrary time-delay. In 46th Conference on Decision and Control (CDC’2007). New Orleans, LA: IEEE.
   Google Scholar
• Miller, D., Hulett, J., McLaughin, J., & de Callafon, R. (2013). Thermal dynamical identification of light-emitting diodes by step-based realization and convex optimization. IEEE Transactions on Components, Packaging, and Manufacturing Technology, 3, 997–1007.
   Web of Science ®Google Scholar
• Ni, B., Gilson, M., & Garnier, H. (2013). A refined instrumental variable method for Hammerstein-Wiener continuous-time model identification, in press. IET Control Theory & Applications.
   Web of Science ®Google Scholar
• Nielsen, J., Madsen, H., & Young, P.C. (2000). Parameter estimation in stochastic differential equations: An overview. Annual Reviews in Control, 24, 83–94.
   Google Scholar
• Pedregal, D.J., Taylor, C.J., & Young, P.C. (2005). The CAPTAIN handbook. Centre for Research on Environmental Systems and Statistics, Lancaster University.
   Google Scholar
• Pierce, D. (1972). Least squares estimation in dynamic disturbance time-series models. Biometrika, 5, 73–78.
   Google Scholar
• Pintelon, R., & Schoukens, J. (2001). System identification: A frequency domain approach. Piscataway, NJ: IEEE Press.
   Google Scholar
• Pintelon, R., Schoukens, J., & Guillaume, P. (2006). Continuous-time noise modelling from sampled data. IEEE Transactions on Instrumentation and Measurement, 55, 2253–2258.
   Web of Science ®Google Scholar
• Pintelon, R., Schoukens, J., & Rolain, Y. (2000). Box-Jenkins continuous-time modeling. Automatica, 36, 983–991.
   Web of Science ®Google Scholar
• Pintelon, R., Schoukens, J., & Rolain, Y. (2008). Frequency-domain approach to continuous-time system identification: Some practical aspects. In H. Garnier & L. Wang (Eds.), Identification of continuous-time models from sampled data (pp. 215–248). London: Springer.
   Google Scholar
• Price, L., Young, P.C., Berckmans, D., Janssens, K., & Taylor, J. (1999). Data-based mechanistic modelling and control of mass and energy transfer in agricultural buildings. Annual Reviews in Control, 23, 71–82.
   Google Scholar
• Rao, G., & Garnier, H. (2002). Numerical illustrations of the relevance of direct continuous-time model identification. In 15th Triennial IFAC World Congress on Automatic Control. Barcelona, Spain.
   Google Scholar
• Rao, G., & Garnier, H. (2004). Identification of continuous-time systems: Direct or indirect?. Systems Science, 30, 25–50.
   Google Scholar
• Rao, G., & Sinha, N. (1991). Continuous-time models and approaches. In N.K. Sinha & G.P. Rao (Eds.), Identification of continuous-time systems. Methodology and computer implementation (pp. 1–15). Dordrecht: Kluwer Academic Publishers.
   Google Scholar
• Rao, G., & Unbehauen, H. (2006). Identification of continuous-time systems. IEE Proceedings – Control Theory and Applications, 153, 185–220.
   Google Scholar
• Schorsch, J., Garnier, H., Gilson, M., & Young, P.C. (2013, in press). Instrumental variable methods for identifying partial differential equation models. International Journal of Control.
   Google Scholar
• Schoukens, J., Pintelon, R., & Van Hamme, H. (1994). Identification of linear dynamic systems using piecewise constant excitations: Use, misuse and alternatives. Automatica, 30, 1953–1169.
   Web of Science ®Google Scholar
• Shibata, R. (1985). Various model selection techniques in time series analysis. In E.J. Hannan, P.R. Krishnaiah, & M. Rao (Eds.), Handbook of statistics 5: Time series in the time domain (pp. 179–187).
   Google Scholar
• Söderström, T. (2007). Errors-in-variables methods in system identification. Automatica, 43, 939–958.
   Web of Science ®Google Scholar
• Söderström, T. (2012). How accurate can instrumental variable models become?. In L. Wang & H. Garnier (Eds.), System identification, environmental modelling, and control system design (pp. 3–26). London: Springer-Verlag.
   Google Scholar
• Söderström, T., & Stoica, P. (1983). Instrumental variable methods for system identification. New York: Springer Verlag.
   Google Scholar
• Solo, V. (1978). Time series recursions and stochastic approximation. Canberra: Australian National University.
   Google Scholar
• Thil, S., Garnier, H., & Gilson, M. (2008). Third-order cumulants based methods for continuous-time errors-in-variables model identification. Automatica, 44, 647–658.
   Web of Science ®Google Scholar
• Thil, S., Garnier, H., Gilson, M., & Mahata, K. (2007, December 12–14). Continuous-time model identification from noisy input/output measurements using fourth-order cumulants. In 46th Conference on Decision and Control (CDC’2007). New Orleans, LA, USA.
   Google Scholar
• Toth, R., Laurain, V., Gilson, M., & Garnier, H. (2012). Instrumental variable scheme for closed-loop LPV model identification. Automatica, 48, 2314–2320.
   Web of Science ®Google Scholar
• Unbehauen, H., & Rao, G. (1990). Continuous-time approaches to system identification – a survey. Automatica, 26, 23–35.
   Web of Science ®Google Scholar
• Unbehauen, H., & Rao, G. (1998). A review of identification in continuous-time systems. Annual Reviews in Control, 22, 145–171.
   Google Scholar
• Van den Hof, P., & Schrama, R. (1993). An indirect method for transfer function estimation from closed loop data. Automatica, 29, 1345 1523–1527.
   Web of Science ®Google Scholar
• Victor, S., Malti, R., Garnier, H., & Oustaloup, A. (2013). Parameter and differentiation order estimation in fractional models. Automatica, 49, 926–935.
   Web of Science ®Google Scholar
• Wahlberg, B. (1990). The effects of rapid sampling in system identification. Automatica, 26, 167–170.
   Web of Science ®Google Scholar
• Wang, J., Zheng, W., & Chen, T. (2009). Identification of linear dynamic systems operating in a networked environment. Automatica, 45, 2763–2772.
   Web of Science ®Google Scholar
• Young, P.C. (1966). Process parameter estimation and self adaptive control. In P.H. Hammond (Ed.), Theory of self adaptive control systems (pp. 118–140). New York: Plenum Press.
   Google Scholar
• Young, P.C. (1969). Applying parameter estimation to dynamic systems: Part II – Applications. Control Engineering, 16, 118–124.
   Web of Science ®Google Scholar
• Young, P.C. (1970). An instrumental variable method for real-time identification of a noisy process. Automatica, 6, 271–287.
   Web of Science ®Google Scholar
• Young, P.C. (1976). Some observations on instrumental variable methods of time-series analysis. International Journal of Control, 23, 593–612.
   Web of Science ®Google Scholar
• Young, P.C. (1981). Parameter estimation for continuous-time models – a survey. Automatica, 17, 23–39.
   Web of Science ®Google Scholar
• Young, P.C. (1984). Recursive estimation and time-series analysis. Berlin: Springer-Verlag.
   Google Scholar
• Young, P.C. (1989). Recursive estimation, forecasting and adaptive control. In C.T. Leondes (Ed.), Control and dynamic systems: Advances in theory and applications (pp. 119–166). San Diego, CA: Academic Press.
   Google Scholar
• Young, P.C. (1998a). Data-based mechanistic modeling of environmental, ecological, economic and engineering systems. Journal of Modelling & Software, 13, 105–122.
   Web of Science ®Google Scholar
• Young, P.C. (1998b). Data-based mechanistic modeling of engineering systems. Journal of Vibration and Control, 4, 5–28.
   Web of Science ®Google Scholar
• Young, P.C. (2008). The refined instrumental variable method: Unified estimation of discrete and continuous-time transfer function models. Journal Européen des Systèmes Automatisés, 42.
   Google Scholar
• Young, P.C. (2010). The estimation of continuous-time rainfall-flow models for flood risk management. In C. Walshe (Ed.), Role of hydrology in managing consequences of a changing global environment (pp. 303–310). University of Newcastle: British Hydrological Society.
   Google Scholar
• Young, P.C. (2011). Recursive estimation and time-series analysis: An introduction for the student and practitioner. Berlin: Springer-Verlag.
   Google Scholar
• Young, P.C., & Garnier, H. (2006). Identification and estimation of continuous-time data-based mechanistic (DBM) models for environmental systems. Environmental Modelling & Software, 21, 1055–1072.
   Web of Science ®Google Scholar
• Young, P.C., Garnier, H., & Gilson, M. (2008). Refined instrumental variable identification of continuous-time hybrid Box-Jenkins models. In H. Garnier & L. Wang (Eds.), Identification of continuous-time models from sampled data (pp. 91–132). London: Springer-Verlag.
   Google Scholar
• Young, P.C., Garnier, H., & Gilson, M. (2009, July). Simple refined IV methods of closed-loop system identification. In 15th IFAC Symposium on System Identification (SYSID’2009). Saint-Malo, France.
   Google Scholar
• Young, P.C., & Jakeman, A.J. (1979). Refined instrumental variable methods of time-series analysis: Parts I and II. International Journal of Control, 29, 1–30; 29, 621–644.
   Web of Science ®Google Scholar
• Young, P.C., & Jakeman, A. (1980). Refined instrumental variable methods of time-series analysis: Part III, Extensions. International Journal of Control, 31, 741–764.
   Web of Science ®Google Scholar