Potential of randomization in kalman-type prediction algorithms at arbitrary external noise in observations

Amelin, Konstantin; Granichin, Oleg

doi:10.1134/s2075108711040031

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…These results were extended to the case of time-varying parameters in [9], [10]. The information about the maximum possible amplitude of the noise has only been used in the formulas for estimating the rate of convergence, i. e., this knowledge is not required for operability of an identification algorithm.…”

Section: Introductionmentioning

confidence: 97%

Combined procedure with randomized controls for the parameters' confidence region of linear plant under external arbitrary noise

Amelin

Amelina

Granichin

et al. 2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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The new algorithm is proposed for the estimating of linear plant's unknown parameters in the case of observations with arbitrary external noises. It is based on adding of randomized inputs (test perturbations) through the feedback channel. The assumptions about the noise are reduced to a minimum: it can virtually be arbitrary but independently of it the user must be able to add test perturbations. We combine the previous result about asymptotic properties of randomized control strategy with the new one which is followed by a nonasymptotic approach of LSCR (Leave-out Sign-dominant Correlation Regions) method. The new algorithm gives confidence regions for series of finite sets of observations. These regions shrink to the true values of an unknown parameters when number of observations tents to infinity while the algorithm complexity does not increases.

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Section: Introductionmentioning

confidence: 97%

Combined procedure with randomized controls for the parameters' confidence region of linear plant under external arbitrary noise

Amelin

Amelina

Granichin

et al. 2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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show abstract

“…The paper proposes to extend the results obtained in [Amelin and Granichin, 2011;Amelin, 2012;Amelin and Granichin, 2012;Amelin and Granichin, 2016] for the application of the SPSA method to counteract random changes in wind strength and direction, and to develop a wind estimation module based on this method for testing on a real UAV.…”

Section: Introductionmentioning

confidence: 99%

Using of the SPSA method to improve the accuracy of the UAV following a given route under the action of wind loads

Amelin¹,

Maltsev²

2021

CAP

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In the modern world, UAVs (unmanned aerial vehicles) are increasingly used in everyday life in solving civilian tasks. One of the main applications of UAVs is data collection with their reference to a given coordinate system. For example, for the task of aerial photography, it is necessary to accurately link each image to the global coordinate system. In addition to the exact location of coordinates, it is worth the exact movement of a given route, to collect data of exactly those places that are needed. Thus, it is very important to ensure the minimum deviation of the UAV from the given route under the conditions of external disturbances (wind disturbances) acting on it. The article describes a procedure for assessing wind speed and direction for a UAV control system using the SPSA method. The simulation results of the algorithm operation, confirmed during flight tests on an ultralight UAV with an ardupilot autopilot, are presented.

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Randomized controls for linear plants and confidence regions for parameters under external arbitrary noise

Amelin

Granichin

2012

2012 American Control Conference (ACC)

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Potential of randomization in kalman-type prediction algorithms at arbitrary external noise in observations

Cited by 4 publications

References 7 publications

Combined procedure with randomized controls for the parameters' confidence region of linear plant under external arbitrary noise

Combined procedure with randomized controls for the parameters' confidence region of linear plant under external arbitrary noise

Using of the SPSA method to improve the accuracy of the UAV following a given route under the action of wind loads

Randomized controls for linear plants and confidence regions for parameters under external arbitrary noise

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