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Adaptive Observers for Linear Stochastic Time-Variant Systems with Disturbances

Abstract : Motivated by fault detection and isolation problems, we present an approach to the design of state observers for linear time-variant stochastic systems with unknown parameters and disturbances. The novelties with respect to more conventional techniques are as follows: (a) the joint estimation of state, disturbances and parameters can be carried out; (b) it is a full-stochastic approach: the unknown parameters and disturbances are random quantities and prior information, in terms of means and covariances, can be easily taken into account; (c) the observer structure is not fixed a priori, rather derived from the optimal one by means of a sliding window approximation; (d) contrary to descriptor system techniques, which estimate the state starting from a restricted set of disturbance-free equations, our approach is focused on disturbance estimation, from which state estimates are derived straightforwardly.
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Submitted on : Thursday, January 17, 2013 - 4:04:01 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM

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Stefano Perabò, Qinghua Zhang. Adaptive Observers for Linear Stochastic Time-Variant Systems with Disturbances. International Journal of Adaptive Control and Signal Processing, Wiley, 2009, 23 (6), pp.547-566. ⟨10.1002/acs.1051⟩. ⟨hal-00777535⟩



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