Simultaneous and sequential state and parameter estimation using receding-horizon nonlinear Kalman filter

Rangegowda, Pavanraj H.; Valluru, Jayaram; Patwardhan, Sachin C.; Mukhopadhyay, Siddhartha

doi:10.1016/j.jprocont.2021.11.003

Cited by 7 publications

(8 citation statements)

References 32 publications

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“…Recently, Rangegowda et al have developed two approaches for simultaneous state and parameter estimation based on RNK. These approaches pose the simultaneous state and parameter estimation problem either as minimization of the complete likelihood function or a likelihood function constructed using the innovation sequence (prediction error) generated by RNK.…”

Section: Robust State and Parameter Estimationmentioning

confidence: 99%

“…In this subsection, the complete likelihood based state and parameter scheme recently proposed by Rangegowda et al is modified by including M-estimators. The following additional assumption is considered in the formulation of the complete likelihood robust RNK based simultaneous state and parameter estimation scheme. Assumption 6: For a perfectly known parameter vector θ , the prediction error sequence, , generated over the window k ] by RNK is a stochastic process having a time-varying Gaussian distribution with zero mean and covariance ; that is, …”

Section: Robust State and Parameter Estimationmentioning

confidence: 99%

“…represents the update step in the proposed Explicit (i.e., OPT or REC)/Implicit M-RNK (i.e., OPT or REC) algorithm described in the previous section. The stacked measurement error is normalized using inside the CL based state and parameter estimator scheme since the original problem is defined for X k , which is replaced by the smoothed estimates . This optimization problem yields and X̂ k | k , which is further used to compute the corresponding updated covariance matrix.…”

Section: Robust State and Parameter Estimationmentioning

confidence: 99%

“…In this subsection, the prediction error based state and parameter scheme recently proposed by Rangegowda et al . is modified by using Implicit M-RNK.…”

Section: Robust State and Parameter Estimationmentioning

confidence: 99%

“…This approach facilitates the use of the update step in conventional RNK without requiring explicit use of the M-estimator. Further, robust versions of the recently proposed RNK based simultaneous state and parameter estimation schemes, , are developed by combining them with the proposed M-RNK schemes. The proposed robust estimation schemes are generic in nature and can employ any M-estimator available in the literature .…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Development of a Robust Receding-Horizon Nonlinear Kalman Filter Using M-Estimators

Rangegowda

Valluru

Patwardhan

et al. 2022

Ind. Eng. Chem. Res.

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The majority of Bayesian methods for the state estimation are based on the assumption that the measurements are corrupted only with random errors. In practice, however, the measurements are often corrupted with gross errors or biases, which leads to biased state estimates when the conventional Bayesian estimators are used. This, in turn, deteriorates the performance of model based process monitoring or control schemes that rely on the state estimator. In this work, to minimize the effects of gross errors on state estimates, two robust versions of the receding-horizon nonlinear Kalman filter (RNK) are developed by integrating M-estimators with the conventional RNK. In the first approach, referred to as Explicit M-RNK, the update step is recast as an optimization problem and further modified by explicitly including an M-estimator. Using the Taylor series approximation, a recursive update step is derived analytically and further used to arrive at a recursive rule for the associated covariance update. The second approach, referred to as the Implicit M-RNK, uses the gradient of the influence function of the chosen M-estimator for adaptive modification of the measurement model used in the update step. This approach facilities the use of the update step in conventional RNK without requiring explicit use of the M-estimator. The proposed robust RNK state estimation formulation is further used to develop a robust state and parameter estimation scheme. The efficacies of the proposed estimation schemes have been demonstrated by conducting simulation studies on some benchmark systems and experimental data sets. The simulation studies reveal that the proposed robust RNK estimators can estimate states and drifting parameter(s) accurately even with the gross errors in the measurements.

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Section: Robust State and Parameter Estimationmentioning

confidence: 99%

Section: Robust State and Parameter Estimationmentioning

confidence: 99%

Section: Robust State and Parameter Estimationmentioning

confidence: 99%

“…In this subsection, the prediction error based state and parameter scheme recently proposed by Rangegowda et al . is modified by using Implicit M-RNK.…”

Section: Robust State and Parameter Estimationmentioning

confidence: 99%