Adaptive Kalman Filtering: A Simulation Result

Sąsiadek, Jurek Z.; Wojcik, P.

doi:10.1115/1.3152639

Cited by 2 publications

(4 citation statements)

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“…As stated earlier, the present contribution explores whether the adaptive estimation approach can improve the performance of SVSF-OBL in situations where the plant is nonlinear and its model is inaccurate. Although adaptive state estimators for linear plants have been known for quite some time, 9,[19][20][21] adaptive state estimators for nonlinear systems have recently drawn the attention of researchers. 2,6,8 For both linear and nonlinear adaptive state estimators, the broad classifications constitute process noise adaptive (Q-adaptive), measurement noise adaptive (R-adaptive), 7,20,22 and a combination of Q and R adaptive (QR-adaptive) estimators.…”

Section: Previous Work On Nonlinear Adaptive State Estimatorsmentioning

confidence: 99%

“…The ASVSF gain can be written as 9 followsAccording to the assumptions explained earlier, italicH is essentially a square matrix of full rank. The situation where the system has fewer measurements than the number of states can also be addressed as per.…”

Section: The Proposed Adaptive Smooth Variable Structure Filtermentioning

confidence: 99%

“…Approximation in the process model is often represented by process noise covariance. Several nonlinear robust state estimators, [1][2][3][4][5] as well as adaptive state estimators, 2,[6][7][8][9] have been reported in the literature. Recently, a robust variable structure filter called Smooth Variable Structure Filter (SVSF) 4,[10][11][12][13] has been proposed and its application to air traffic control problems has been proposed in references.…”

Section: Introductionmentioning

confidence: 99%

“…Previously proposed versions of SVSF, 4,15 which do not require the process noise covariance are considered robust with respect to the extent of modelling approximations. These robust versions of SVSF filters may, however, suffer from chattering 9,16,17 due to unknown process noise and other disturbances. The bounds of the amplitude of chattering in certain cases may be predicted and the same is said to be contained within an “existence sub-space.” 3 Smooth Variable Structure Filter with boundary layers 3,4,10,16,18 has been proposed to reduce such chattering.…”

Section: Introductionmentioning

confidence: 99%

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Performance evaluation of a novel adaptive variable structure state estimator

Patra

Sadhu

Ghoshal

2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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An improved nonlinear adaptive state estimator called Adaptive Smooth Variable Structure Filter ASVSF has been proposed and its algorithm described. ASVSF extends the functionality and performance of a previously reported robust smooth variable structure filter (SVSF) with optimal boundary layer (SVSF-OBL). Improvement in performance includes the provision for accommodating unknown and time varying process noise covariance, which generally characterizes modelling uncertainty. The novelty of this proposed ASVSF estimator, which inherits the features of the SVSF, is that it adaptively provides an estimate of the unknown time varying process noise covariance (and hence called adaptive SVSF or ASVSF) which is required for determining the optimal boundary layer width of SVSF-OBL thus obviating the need of the prior knowledge of the process noise covariance. This makes the proposed estimator performance to be insensitive to (and therefore robust with respect to) unknown time varying process noise covariance while retaining the optimality of SVSF-OBL. The performance of the proposed ASVSF estimator is evaluated using Monte Carlo simulation and is compared with previously reported state estimators using a case of maneuvering civilian aircraft where a simplified and grossly approximate process model is used in the estimator/filter for tracking and thereby generating a time varying and unknown process noise covariance situation. Three different measures of Root Mean Square (RMS) error over the trajectory have been used for comparison which demonstrates the strengths of the proposed ASVSF estimator.

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