Filtering based parameter estimation for observer canonical state space systems with colored noise

Wang, Xuehai; Ding, Feng; Alsaedi, Ahmed; Hayat, Tasawar

doi:10.1016/j.jfranklin.2016.10.032

Cited by 9 publications

(6 citation statements)

References 46 publications

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“…Simulation results illustrate that the investigated method is effective and has advantages of simplicity and efficiency. The proposed IV-OMP optimization method can be extended to the colored noise systems, the networked dynamic systems [42][43][44][45][46], and so on.…”

Section: Discussionmentioning

confidence: 99%

Instrumental Variable‐Based OMP Identification Algorithm for Hammerstein Systems

2018

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Hammerstein systems are formed by a static nonlinear block followed by a dynamic linear block. To solve the parameterizing difficulty caused by parameter coupling between the nonlinear part and the linear part in a Hammerstein system, an instrumental variable method is studied to parameterize the Hammerstein system. To achieve in simultaneously identifying parameters and orders of the Hammerstein system and to promote the computational efficiency of the identification algorithm, a sparsity-seeking orthogonal matching pursuit (OMP) optimization method of compressive sensing is extended to identify parameters and orders of the Hammerstein system. The idea is, by the filtering technique and the instrumental variable method, to transform the Hammerstein system into a simple form with a separated nonlinear expression and to parameterize the system into an autoregressive model, then to perform an instrumental variable-based orthogonal matching pursuit (IV-OMP) identification method for the Hammerstein system. Simulation results illustrate that the investigated method is effective and has advantages of simplicity and efficiency.

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Section: Discussionmentioning

confidence: 99%

Instrumental Variable‐Based OMP Identification Algorithm for Hammerstein Systems

2018

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“…Equations (19)- (26) and 51- (56) form the MEKF-RLS algorithm for estimating the parameters of the Wiener system in (1). The basic idea in this article can be combined the mathematical tools 41,42 such as the data filtering technique, 43,44 the particle filter, 45,46 and the iterative methods [47][48][49] to study the identification of other linear stochastic systems, 50 bilinear stochastic systems, 51,52 and nonlinear stochastic systems [53][54][55][56][57] and can be applied to other engineering areas. [58][59][60][61] Theorem 3.…”

Section: The Mekf Based Rls Algorithmmentioning

confidence: 99%

The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

Wang

Zhu

Ding

2020

Adaptive Control & Signal

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This article develops the modified extended Kalman filter based recursive estimation algorithms for Wiener nonlinear systems with process noise and measurement noise. The prior estimate of the linear block output is computed based on the auxiliary model, and the posterior estimate is updated by designing a modified extended Kalman filter. A multi-innovation gradient algorithm and a recursive least squares algorithm are derived to estimate the parameters of the linear subsystem, respectively. The simulation examples are provided to demonstrate the effectiveness of the proposed algorithms.

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“…discussed parameter identification methods for linear state‐space models subjected to coloured noises. They took advantage of the data filtering technique to eliminate the interference of the coloured noises and utilised the least squares principle to obtain parameter estimates [19]. In consideration of the identification issues of bilinear state‐space systems, Zhang et al .…”

Section: Introductionmentioning

confidence: 99%

Recursive parameter and state estimation methods for observability canonical state‐space models exploiting the hierarchical identification principle

Cui

Ding

Alsaedi³

et al. 2019

IET Control Theory & Applications

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Filtering based parameter estimation for observer canonical state space systems with colored noise

Cited by 9 publications

References 46 publications

Instrumental Variable‐Based OMP Identification Algorithm for Hammerstein Systems

Instrumental Variable‐Based OMP Identification Algorithm for Hammerstein Systems

The modified extended Kalman filter based recursive estimation for Wiener nonlinear systems with process noise and measurement noise

Recursive parameter and state estimation methods for observability canonical state‐space models exploiting the hierarchical identification principle

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