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Multi-state dependent parameter model identification and estimation for nonlinear dynamic systems

Multi-state dependent parameter model identification and estimation for nonlinear dynamic systems

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An important generalisation of the state dependent parameter approach to the modelling of nonlinear dynamic systems to include multi-state dependent parameter (MSDP) nonlinearities is described. The recursive estimation of the MSDP model parameters in a multivariable state space occurs along a multipath trajectory, employing the Kalman filter and fixed interval smoothing algorithms. The novelty of the method lies in redefining the concepts of sequence (predecessor, successor), allowing for its use in a multi-state dependent context, so producing efficient parameterisation for a fairly wide class of nonlinear, stochastic dynamic systems. The format of the estimated model allows its direct use in control system design.

Inspec keywords: Kalman filters; state-space methods; recursive estimation; nonlinear dynamical systems

Other keywords: multistate dependent parameter model identification; recursive estimation; multistate dependent parameter nonlinearities; Kalman filter; fixed interval smoothing; multipath trajectory; multistate dependent parameter estimation nonlinear dynamic systems; multivariable state space

Subjects: Simulation, modelling and identification; Signal processing theory; Other topics in statistics; Control system analysis and synthesis methods; Nonlinear control systems

References

    1. 1)
    2. 2)
      • M.B. Priestley . (1988) Nonlinear and nonstationary time series analysis.
    3. 3)
    4. 4)
      • S. Chen , S.A. Billings . Representations of non-linear systems: the NARMAX model. Int. J. Control , 3 , 1013 - 1032
    5. 5)
    6. 6)
      • T.J. Hastie , R.J. Tibshirani . (1993) Generalized additive models.
    7. 7)
      • P.C. Young . Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. Environ. Model. Softw. , 105 - 122
    8. 8)
      • P.C. Young . Comment on “A quasi-ARMAX approach to modelling nonlinear systems”' by J. Hu et al.. Int. J. Control , 18 , 1767 - 1771
    9. 9)
      • P.C. Young , D.J. Pedregal , W. Tych . Dynamic harmonic regression. Forecast. , 369 - 394
    10. 10)
      • N.V. Truong , L. Wang , P.K.C. Wong . Modelling and short-term forecasting of daily peak power demand in Victoria using two-dimensional wavelet based SDP models. Int. J. Electr. Power Energy Syst. , 9 , 511 - 518
    11. 11)
    12. 12)
      • R.E. Kalman . A new approach to linear filtering and prediction problems. Trans. ASME, D , 35 - 45
    13. 13)
      • A.E. Bryson , Y.C. Ho . (1969) Applied optimal control.
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