Prediction of states of discrete systems with unknown input of the model using compensation

The problem of state prediction for linear dynamic systems with discrete time is considered in the presence of unknown input and inaccurately specified parameters in the model. An algorithm with compensation for the constant component and estimation of the unknown variable input component by the least squares method is suggested. Results of statistical simulation are presented. The algorithm can be used for solving problems of processing information obtained as a result of observations over physical processes

Prediction of states of discrete systems with unknown input of the model using compensation

The problem of state prediction for linear dynamic systems with discrete time is considered in the presence of unknown input and inaccurately specified parameters in the model. An algorithm with compensation for the constant component and estimation of the unknown variable input component by the least squares method is suggested. Results of statistical simulation are presented. The algorithm can be used for solving problems of processing information obtained as a result of observations over physical processes

Robust filtering for discrete systems with unknown inputs and jump parameters

The paper deals with robust filtering algorithms for discrete systems with unknown inputs (disturbances) and Markovian jump parameter. The proposed filtering algorithm is based on the separation principle, minimization of a quadratic criterion and the use of Kalman filters with unknown input and smoothing procedures. Solving a non-stationary problem is represented solving a two-point boundary value problem in kind of difference matrix equations. In the stationary case problem is represented matrix algebraic equations. Robustness ensures the stability of the filter dynamics when errors occur in identifying the jump parameter. An example is provided to illustrate the proposed approach, which showed that the use of smoothing procedures for estimating an unknown input improves the accuracy of estimates

Delayed control for discrete systems with incomplete information about disturbances depending on Markov jump parameter

The problem delay-dependent control for discrete systems is considered. The control algorithm is synthesized under conditions of incomplete information about the disturbance model and is based on the optimization of the local criterion, using robust Kalman filtering for systems with unknown input and Markov jump parameter. An example is given to illustrate the proposed approach

Робастная экстраполяция для систем с неизвестным входом и интервальной неопределенностью в объекте и наблюдениях

The problem of robust extrapolation for discrete linear system with unknown input and uncertain interval parameters in system and model of observations is considered. The probabilistic approach is used, which is based on replacing uncertain parameters of interval type by independent random variables with uniform distribution in recursive Kalman schemes. The LSM algorithms and nonparametric smoothing procedures are applied for estimating unknown input. The proposed algorithms can be used in control systems with incomplete information. Simulation results are presented and discussed