Optimization and Experimental Study of an Intelligent Bamboo-Splitting Machine Charging Manipulator

Liu, Tianhu; Wen, Yonglu; Li, Guiqi; Nie, Xiang-Ning

doi:10.1155/2020/4675301

Cited by 2 publications

(2 citation statements)

References 21 publications

(29 reference statements)

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“…When operating real systems-control, communications, navigation, etc.-the parameters of both the system itself and its state vector meters, as a rule, change randomly over time. This is especially evident in imperfect dynamical systems, the interest in the study of which has only been increasing lately [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Kalman Filter Adaptation to Disturbances of the Observer’s Parameters

et al. 2021

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Currently, one of the most effective algorithms for state estimation of stochastic systems is a Kalman filter. This filter provides an optimal root-mean-square error in state vector estimation only when the parameters of the dynamic system and its observer are precisely known. In real conditions, the observer’s parameters are often inaccurately known; moreover, they change randomly over time. This in turn leads to the divergence of the Kalman estimation process. The problem is currently being solved in a variety of ways. They include the use of interval observers, the use of an extended Kalman filter, the introduction of an additional evaluating observer by nonlinear programming methods, robust scaling of the observer’s transmission coefficient, etc. At the same time, it should be borne in mind that, firstly, all of the above ways are focused on application in specific technical systems and complexes, and secondly, they fundamentally do not allow estimating errors in determining the parameters of the observer themselves in order to compensate them for further improving the accuracy and stability of the filtration process of the state vector. To solve this problem, this paper proposes the use of accurate observations that are irregularly received in a complex measuring system (for example, navigation) for adaptive evaluation of the observer’s true parameters of the stochastic system state vector. The development of the proposed algorithm is based on the analytical dependence of the Kalman estimate variation on the observer’s parameters disturbances obtained using the mathematical apparatus for the study of perturbed multidimensional dynamical systems. The developed algorithm for observer’s parameters adaptive estimation makes it possible to significantly increase the accuracy and stability of the stochastic estimation process as a whole in the time intervals between accurate observations, which is illustrated by the corresponding numerical example.

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Section: Introductionmentioning

confidence: 99%

“…Development of so-called "interval observers" for systems with nonstationary fluctuating parameters whose variations are within the specified intervals [15,16]. 6.…”

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confidence: 99%