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This paper deals with control of first and second moment of the states of linear stochastic discrete-time systems. In this paper, the expectation and covariance of the states are combined together to construct a new higher order system. Our approach tends to use the covariance and the expectation of the states as feedback signals to obtain the appropriate control signal which leads the states of the...
This paper presents a Kalman filter-based adaptive disturbance-accommodating stochastic control scheme for linear uncertain systems to minimize the adverse effects of both model uncertainties and external disturbances. A rigorous stochastic stability analysis reveals a lower bound requirement on system process noise covariance to ensure the stability of the controlled system when the nominal control...
In this paper we study the error covariance matrix of the recursive Kalman filter when the parameters of the filter are driven by a Markov chain taking values in a countably infinite set. In this context, the error covariance matrix of the filter depends on the Markov state realizations, and in this sense forms a stochastic process. We show in a rather direct and comprehensive manner that a standard...
This paper proposes an estimation technique in terms of the recursive least-squares (RLS) Wiener filter by operating the wavelet transform to the state vector generating a signal in linear discrete-time stochastic systems. The RLS Wiener filter uses the factorized covariance information of the signal and the variance of observation noise. Here, it is assumed that the observation vector consists of...
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