Abstract
In this chapter, the issues in real-time identification of continuous systems are considered. Starting with some aspects of plant model forms, measurement systems and preprocessing schemes, continuous model estimation algorithms are discussed. Further postprocessing techniques for the parameter estimates and residuals which may be necessary to satisfy the requirements of specific applications such as adaptive control, fault detection, condition monitoring etc. are indicated. Salient features of hardware and software important for practical implementations are also discussed briefly. A practical example of real-time parameter estimation is presented. The chapter concludes with a summarizing view and a look into the future in the light of emerging technology.
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References
Agarwal, M. and C.A. Canudas (1987), “On-line estimation of time-dalay and continuous-time process parameters”, Int. J. Control, Vol.46, No.l, pp. 295–311.
Äström, K.J. and P. Eykhoff (1971), “System identification — A survey”, Automatica, Vol.7, pp. 123–162.
Äström, K.J. and B. Wittenmark (1989), “Adaptive Control”, Addison Wesley.
Betta, A. and D.A. Linkens (1990), “Intelligent knowledge-based system for dynamic system identification”, Proc. IEE Pt. D, Vol. 137, No. 1, pp.1–12.
Burr Brown Corporation (1987), “The Handbook of Personal Computer Instrumentation”.
Chakravarty, B.B., N. Mandayam, S. Mukhopadhyay, A. Patra and G.P. Rao (1989), “Real-time parameter estimation via block pulse functions”, Proc. SICE-89, Matsuyama, Japan, pp. 1095–1098.
Chisci, L. (1988), “High speed RLS parameter estimation by systolic like arrays”, in Advanced Computing Concepts and Techniques in Control Engineering (Ed. M.J. Denham and A.J. Laub), Springer.
Dai, H. and N.K. Sinha (1990), “Robust coefficient estimation of Walsh Functions”, Proc. IEE, Pt. D, Vol.137, No.6, pp. 357–363.
Durgaprasad, G., G.P. Rao, A. Patra and S. Mukhopadhyay (1991), “Indirect methods of parameter estimation of discrete time models”, Proc. 9th IFAC/ IFORS Symp. on Identification and System Parameter Estimation, 8-12 July, 1991, Budapest, Hungary.
Gawthrop, P.J. (1987), “Continuous-time Self Tuning Control, Vol. 1 — Design”, Research Studies Press, Lechworth, England.
Goodwin, G.C. (1988), “Some observations on robust stochastic estimation and control”, Preprints 8th IFAC/ IFORS Symp. on Identification and System Parameter Estimation, Beijing, China.
Goodwin, G.C. and D.Q. Mayne (1987), “A parameter estimation perspective of continuous-time adaptive control”, Automatica, Vol.23, No.1, pp 57–70.
Goodwin, G.C. and K.S. Sin (1984), “Adaptive Filtering Prediction and Control”, Prentice Hall.
Hägglund, T. (1984), “Adaptive Control of systems subject to large parameter changes”, Proc. 9th IFAC World Congress, Budapest, pp. 993–998.
Hanselmann, H. (1987), “Implementation of digital controllers — A survey”, Automatica, Vol.23, No.l, pp. 7–32.
Isermann, R. (1984), “Process fault detection based on modelling and estimation methods — A survey”, Automatica, Vol.20, No.4, pp. 387–404.
Isermann, R. and K.-H. Lachmann (1987), “Adaptive controllers — Supervision level”, in Systems and Control Encyclopedia, M.G. Singh (Ed.), Pergamon Press.
Kailath, T. (1980), “Linear Systems”, Prentice Hall.
Ljung, L (1987), “System Identification — Theory for the User”, Prentice Hall.
Ljung, L and S. Gunnarsson (1990), “Adaptation and tracking in system identification — A survey”, Automatica, Vol.26, No.1, pp. 7–21.
Ljung, L and T. Söderström (1983), “Theory and Practice of Recursive Identification”, MIT Press.
McMillan, G.K. (1990), “Tuning and Control Loop Performance”, Instrument Society of America.
Middleton, R.H. and G.C. Goodwin (1990), “Digital Control and Estimation — A Unified Approach”, Prentice Hall.
Mukhopadhyay, S. (1990), “Continuous-time Models and Approches for Estimation and Control of Linear systems”, Ph.D. Thesis, Department of Electrical Engineering, IIT Kharagpur, India.
Mukhopadhyay, S. and G.P. Rao (1991), “Integral equation approach to joint state and parameters estimation for MIMO systems”, Proc. IEE Pt. D, (To appear).
Narendra, K.S. and P.G. Gallman (1966), “An iterative method for identification of nonlinear systems using Hammerstein models”, IEEE Trans. Automatic Control, Vol.AC-11, p. 546.
Narendra, K.S. and K. Parthasarathy (1990), “Identification and control of dynamical systems using neural networks”, IEEE Trans. on Neural Networks, Vol.1, No.l, pp. 4–27.
Patra, A. (1989), “General Hybrid Orthogonal Functions and Some Applications in Systems and Control”, Ph.D. Thesis, Department of Electrical Engineering, IIT Kharagpur, India.
Patra, A., P.V. Bhaskar and G.P. Rao (1988), “A package for simulation and parameter estimation of continuous-time dynamical systems”, Proc. 8th IFAC/ IFORS Symp. on Identification and System Parameter Estimation, Beijing, pp. 1959–1963.
Patra, A. and G.P. Rao (1989), “Continuous-time approach to self-tuning control — Algorithms, implementation and assessment”, Proc. IEE Pt. D, Vol.136, No.6, pp. 333–340.
Puthenpura, S. and N.K. Sinha (1986), “Robust bootstrap method for joint estimation of states and parameters of a linear system”, J. Dynamic Systems Measurement and Control, Vol.108, pp. 255–263.
Rao, G.P. (1983), “Piecewise Constant Orthogonal Functions and Their Application to Systems and Control”, Springer Verlag, Berlin Heidelberg New York Tokyo.
Rao, G.P. (1985), “Decomposition, decentralization and coordination of identification algorithms for large-scale systems”, Proc. 7th IF AC Symp. on Identification and System Parameter Estimation, York, U.K., pp. 297–301.
Rao, G.P., H. Unbehauen, S. Mukhopadhyay and A. Patra (1991), “From calculus to algebra in models of continuous-time systems”, Proc. 9th IFAC/IFORS Symp. on Identification and System Parameter Estimation, 8-12 July 1991, Budapest, Hungary.
Saha, D.C and G.P. Rao (1983), “Identification of Continuous Dynamical Systems — the Poisson Moment Functional (PMF) Approach”, Springer Verlag, Berlin Heidelberg New York Tokyo.
Söderström, T. and P. Stoica (1989), “System Identification”, Prentice Hall.
Sripada, N.R. and D.G. Fisher (1987), “Improved least squares identification”, Int. J. Control, Vol.46, No.6, pp. 1889–1913.
Unbehauen, H. and G.P. Rao (1987), “Identification of Continuous Systems”, North Holland, Amsterdam.
Unbehauen, H. and G.P. Rao (1990), “Continuous-time approaches to system identification — A survey”, Automatica, Vol.26, No.1, pp. 23–35.
Wahlberg, B. (1988), “On continuous-time system identification”, Preprints 8th IFAC/IFORS Symp. on Identification and System Parameter Estimation, Beijing.
Wellstead, P.E. and P. Zanker (1982), “Techniques of self-tuning”, OCAM, Special Issue on Selftuning Control, Vol.3, No.4, pp. 305–322.
Young, P.C. (1981), “Parameter estimation for continuous-time models — A survey”, Automatica, Vol.7, No.l, pp. 23–29.
Young, P.C. (1984), “Recursive Estimation and Time Series Analysis”, Springer Verlag, Berlin.
Zhao, Z.-Y. (1990), “Linear Integral Filter Approach to Identification of Continuoustime Systems”, Ph.D. Thesis, Department of Electrical Engineering, Kyushu University, Fukuoka, Japan.
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Rao, G.P., Patra, A., Mukhopadhyay, S. (1991). Real-time issues in continuous system identification. In: Sinha, N.K., Rao, G.P. (eds) Identification of Continuous-Time Systems. International Series on Microprocessor-Based Systems Engineering, vol 7. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-3558-0_20
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DOI: https://doi.org/10.1007/978-94-011-3558-0_20
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