Time-varying Static Systems Identification

  • Karel J. Keesman
Part of the Advanced Textbooks in Control and Signal Processing book series (C&SP)


In Chap. 7 recursive estimation is introduced and applied to static, linear or nonlinear, systems with possibly time-varying parameters. The idea is as follows. On the basis of common prior knowledge, the model parameters in the linear regression models are considered as constant. Subsequently, the experimental data, using recursive estimation techniques, will tell how the estimates of the parameters vary with time. This idea can be easily extended to the case with a dynamic parameter model in the form of a linear dynamic state equation, which clearly illustrates the system theoretic concept of a model parameter as a (unobserved) state. Hence, the resemblance of the recursive least-squares parameter estimator to the well-known Kalman filter is emphasized. For the nonlinear case, the concept of extended Kalman filtering is introduced.


Kalman Filter Extend Kalman Filter Unscented Kalman Filter Error Covariance Matrix Recursive Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Systems and Control GroupWageningen UniversityWageningenNetherlands

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