A combined observer and filter based approach for the determination of unknown parameters

We consider the simultaneous reconstruction of the state vector and unknown parameters for a special class of non-linear parameter-dependent systems. The approach suggested here consists of two stages. First, the state is reconstructed by a reduced order unknown input observer. Second, this observer is augmented by a filter to obtain the desired parameter. The design procedure is straightforward and may be used for an online estimation of the unknown parameter. In contrast to previous work on adaptive systems, we do not require a persistence of excitation. The approach is illustrated on two non-linear cell models.

[1]  Denis Dochain,et al.  State and parameter estimation in chemical and biochemical processes: a tutorial , 2003 .

[2]  Philip Denbigh,et al.  System analysis and signal processing: with emphasis on the use of MATLAB , 1998 .

[3]  Bruno A. Olshausen,et al.  Book Review , 2003, Journal of Cognitive Neuroscience.

[4]  G. Ermentrout,et al.  Analysis of neural excitability and oscillations , 1989 .

[5]  Jaime A. Moreno,et al.  PASSIVITY AND UNKNOWN INPUT OBSERVERS FOR NONLINEAR SYSTEMS , 2002 .

[6]  Alberto Isidori,et al.  Nonlinear control systems: an introduction (2nd ed.) , 1989 .

[7]  R. Marino Adaptive observers for single output nonlinear systems , 1990 .

[8]  Robert Haber Nonlinear System Identification : Input-output Modeling Approach , 1999 .

[9]  C. Morris,et al.  Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.

[10]  Bruce Smaill,et al.  Hodgkin-Huxley type ion channel characterization: an improved method of voltage clamp experiment parameter estimation. , 2006, Journal of theoretical biology.

[11]  P. Gage,et al.  Conventional Voltage Clamping With Two Intracellular Microelectrodes , 1985 .

[12]  Miroslav Krstic,et al.  Nonlinear and adaptive control de-sign , 1995 .

[13]  Andreas Kugi,et al.  Symbolic Computation For The Analysis AndSynthesis Of Nonlinear Control Systems , 1999 .

[14]  Arthur J. Krener,et al.  Linearization by output injection and nonlinear observers , 1983 .

[15]  H. Nijmeijer,et al.  New directions in nonlinear observer design , 1999 .

[16]  A. Isidori Nonlinear Control Systems: An Introduction , 1986 .

[17]  Riccardo Marino,et al.  Nonlinear control design: geometric, adaptive and robust , 1995 .

[18]  Shahab Sheikholeslam Observer-Based Parameter Identifiers for Nonlinear Systems with Parameter Dependencies , 1993, 1993 American Control Conference.

[19]  Stanley H. Johnson,et al.  Use of Hammerstein Models in Identification of Nonlinear Systems , 1991 .

[20]  R. Marino,et al.  Adaptive observers with arbitrary exponential rate of convergence for nonlinear systems , 1995, IEEE Trans. Autom. Control..

[21]  S. Van Huffel,et al.  SLICOT system identification software and applications , 2002, Proceedings. IEEE International Symposium on Computer Aided Control System Design.

[22]  Ute Feldmann,et al.  Communication by chaotic signals: the inverse system approach , 1995, Proceedings of ISCAS'95 - International Symposium on Circuits and Systems.

[23]  H. Sebastian Seung,et al.  The Autapse: A Simple Illustration of Short-Term Analog Memory Storage by Tuned Synaptic Feedback , 2004, Journal of Computational Neuroscience.

[24]  S. Sastry,et al.  Adaptive Control: Stability, Convergence and Robustness , 1989 .

[25]  Bram de Jager Symbolic calculation of zero dynamics for nonlinear control systems , 1991, ISSAC '91.

[26]  G. Besançon Remarks on nonlinear adaptive observer design , 2000 .

[27]  H. G. Kwatny,et al.  Nonlinear Control and Analytical Mechanics: A Computational Approach , 2001 .

[28]  Ute Feldmann,et al.  On the design of a synchronizing inverse of a chaotic system , 1995 .

[29]  John Guckenheimer,et al.  An Improved Parameter Estimation Method for Hodgkin-Huxley Models , 1999, Journal of Computational Neuroscience.

[30]  M. Gevers,et al.  Stable adaptive observers for nonlinear time-varying systems , 1987 .

[31]  G. L. Amicucci,et al.  On nonlinear detectability , 1998 .

[32]  Jaime A. Moreno,et al.  Unknown input observers for SISO nonlinear systems , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[33]  J. Gauthier,et al.  A simple observer for nonlinear systems applications to bioreactors , 1992 .

[34]  D. Johnston,et al.  Foundations of Cellular Neurophysiology , 1994 .

[35]  Albert J. Rosa,et al.  The Analysis and Design of Linear Circuits , 1993 .

[36]  Harry G. Kwatny,et al.  Nonlinear Control and Analytical Mechanics: A Computational Approach , 2000 .

[37]  P. S. Sastry,et al.  Memory neuron networks for identification and control of dynamical systems , 1994, IEEE Trans. Neural Networks.

[38]  Paris A. Mastorocostas,et al.  A recurrent fuzzy-neural model for dynamic system identification , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[39]  G. Kreisselmeier Adaptive observers with exponential rate of convergence , 1977 .

[40]  Richard Bertram,et al.  A calcium-based phantom bursting model for pancreatic islets , 2004, Bulletin of mathematical biology.

[41]  Andreas Griewank,et al.  Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition , 2000, Frontiers in applied mathematics.

[42]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[43]  H. Lecar,et al.  Voltage and Patch Clamping with Microelectrodes , 1985, Springer New York.

[45]  Rolf Isermann,et al.  Identifikation dynamischer Systeme , 1988 .

[46]  H. Fortell,et al.  Calculation of zero dynamics using the Ritt algorithm , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[47]  B. Sakmann,et al.  Single-channel currents recorded from membrane of denervated frog muscle fibres , 1976, Nature.

[48]  Philip Denbigh,et al.  System analysis and signal processing , 1998 .

[49]  C. Koch,et al.  Methods in Neuronal Modeling: From Ions to Networks , 1998 .

[50]  M. J. Korenberg,et al.  The identification of nonlinear biological systems: Wiener and Hammerstein cascade models , 1986, Biological Cybernetics.