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Abstract

In the paper an example of application of the Kalman filtering in the navigation process of automatically guided vehicles was presented. The basis for determining the position of automatically guided vehicles is odometry – the navigation calculation. This method of determining the position of a vehicle is affected by many errors. In order to eliminate these errors, in modern vehicles additional systems to increase accuracy in determining the position of a vehicle are used. In the latest navigation systems during route and position adjustments the probabilistic methods are used. The most frequently applied are Kalman filters.
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Abstract

A navigation complex of an unmanned flight vehicle of small class is considered. Increasing the accuracy of navigation definitions is done with the help of a nonlinear Kalman filter in the implementation of the algorithm on board an aircraft in the face of severe limitations on the performance of the special calculator. The accuracy of the assessment depends on the available reliable information on the model of the process under study, which has a high degree of uncertainty. To carry out high-precision correction of the navigation complex, an adaptive non-linear Kalman filter with parametric identification was developed. The model of errors of the inertial navigation system is considered in the navigation complex, which is used in the algorithmic support. The procedure for identifying the parameters of a non-linear model represented by the SDC method in a scalar form is used. The developed adaptive non-linear Kalman filter is compact and easy to implement on board an aircraft.
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Abstract

The paper deals with the application of the extended Kalman filters in the control structure of a two-mass drive system. In the first step only linear extended Kalman filter was used for the estimation of mechanical state variables of the drive including load torque value. The estimation algorithm showed good robustness to mechanical parameters variations. For the system with some parameters changing in the wide range, simultaneous estimation of the state variables and chosen system parameters is required. For this reason the non-linear extended Kalman filter, which estimates simultaneously state variables and mechanical parameters of the two-mass drive system, was developed. Parameters of covariance matrices of used Kalman filters were set using the genetic algorithm. Both proposed estimators were investigated in simulation and experimental tests, in the open-loop operation and in the state-feedback control system of the two-mass system.
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