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Generalized observer design of index one for descriptor systems with unknown inputs

Kumar Abhinav, Gupta Mahendra Kumar

Archives of Control Sciences

2023

Vol. 33, No. 1

83--99

Generalized observers are proposed to relax the existing conditions required to design Luenberger observers for rectangular linear descriptor systems with unknown inputs. The current work is focused on designing index one generalized observers, which can be naturally extended to higher indexes. Sufficient conditions in terms of system operators for the existence of generalized observers are given and proved. Orthogonal transformations are used to derive the results. A physical model is presented to show the usefulness of the proposed theory.

State filtering for networked control systems subject to switching disturbances

Chabir K., Rhouma T., Keller J. Y., Sauter D.

International Journal of Applied Mathematics and Computer Science

2018

Vol. 28, no. 3

473--482

State estimation of stochastic discrete-time linear systems subject to unknown inputs has been widely studied, but few works take into account disturbances switching between unknown inputs and constant biases. We show that such disturbances affect a networked control system subject to deception attacks on the control signals transmitted by the controller to the plant via unreliable networks. This paper proposes to estimate the switching disturbance from an augmented state version of the intermittent unknown input Kalman filter. The sufficient stochastic stability conditions of the obtained filter are established when the arrival binary sequence of data losses follows a Bernoulli random process.

Detection and Regulation Problems for Discrete-Time Distributed Systems With Delays

Namir A., Lahmidi F., Alif O.

International Journal of Applied Mathematics and Computer Science

1999

Vol. 9, no 2

347-369

A distributed discrete-time hereditary system is considered. An unknown input is supposed to be a perturbation. First, we investigate the possibility of reconstructing this input using the information provided by an output equation. Then we treat the problem of keeping the observation as close as possible to some desired values (with the system still perturbed by the unknown input). To illustrate the results, some examples are presented.