Home About us Contact
|
Home About us Contact | ||
|
|
||
Linear Stochastic Systems (linear + stochastic_system)
Selected AbstractsOn-line almost-sure parameter estimation for partially observed discrete-time linear systems with known noise characteristicsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 6 2002Robert J. Elliott Abstract In this paper we discuss parameter estimators for fully and partially observed discrete-time linear stochastic systems (in state-space form) with known noise characteristics. We propose finite-dimensional parameter estimators that are based on estimates of summed functions of the state, rather than of the states themselves. We limit our investigation to estimation of the state transition matrix and the observation matrix. We establish almost-sure convergence results for our proposed parameter estimators using standard martingale convergence results, the Kronecker lemma and an ordinary differential equation approach. We also provide simulation studies which illustrate the performance of these estimators. Copyright © 2002 John Wiley & Sons, Ltd. [source] Integral sliding mode design for robust filtering and control of linear stochastic time-delay systemsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 9 2005Michael Basin Abstract This paper presents an integral sliding mode technique robustifying the optimal controller for linear stochastic systems with input and observation delays, which is based on integral sliding mode compen-sation of disturbances. The general principles of the integral sliding mode compensator design are modified to yield the basic control algorithm oriented to time-delay systems, which is then applied to robustify the optimal controller. As a result, two integral sliding mode control compensators are designed to suppress disturbances in state and observation equations, respectively, from the initial time moment. Moreover, it is shown that if certain matching conditions hold, the designed compensator in the state equation can simultaneously suppress observation disturbances, as well as the designed compensator in the observation equation can simultaneously suppress state disturbances. The obtained robust control algorithm is verified by simulations in the illustrative example, where the compensator in the observation equation provides simultaneous suppression of state and observation disturbances. Copyright © 2005 John Wiley & Sons, Ltd. [source] Optimal state filtering and parameter identification for linear systemsOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 2 2008Michael Basin Abstract This paper presents the optimal filtering and parameter identification problem for linear stochastic systems with unknown multiplicative and additive parameters over linear observations, where unknown parameters are considered Wiener processes. The original problem is reduced to the filtering problem for an extended state vector that incorporates parameters as additional states. The obtained optimal filter for the extended state vector also serves as the optimal identifier for the unknown parameters. Performance of the designed optimal state filter and parameter identifier is verified for both stable and unstable linear uncertain systems. Copyright © 2007 John Wiley & Sons, Ltd. [source] Stochastic Model Reduction by Maximizing IndependenceASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 3-4 2005Hui Zhang By analysing information descriptions in state space models of linear stochastic systems, this paper proposes two model reduction methods via principles of maximizing independence and conditional independence among the reduced state vector, respectively. These methods are based on state aggregation. The independence and conditional independence are measured by the Kullback-Leibler information distance. It is demonstrated that the maximum conditional independence method is not only applicable to stable systems, but also applicable to unstable systems. Simulation results illustrate the efficiency of the present methods. [source] | ||||||||||