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Abstract:
We begin with an analysis of nonlinear observers in terms of passivity.
By suitably defining input and output of the estimation error dynamics,
designing observer injection gain can be viewed as making the error
dynamics passive. This finding in turn leads to another interpretation
of reduced-order observer since the error dynamics of reduced-order
observere is nothing but the zero-dynamics of full-order error dynamics.
Then, we note that passivity property naturally leads to some
robustness, which is, in the case of observer, against measurement
disturbance. We will talk about recent results on nonlinear observers
robust to measurement disturbance in the ISS (input-to-state stability)
sense.
Abstract:
In this study, a robust Pareto suboptimal strategy for
uncertain Markov jump linear stochastic systems (UMJLSSs) with
multiple decision makers is investigated. A guaranteed cost-control
principle is employed to obtain the conditions given using
a stochastic algebraic Riccati inequality (SARI), such that the
closed-loop stochastic system is exponentially mean square stable (EMSS),
having a cost bound. The minimization problem of the cost bound is
formulated, and the necessary conditions, which are obtained via the
set of cross-coupled stochastic Riccati equations (CCSAREs), are derived
with the help of the Karush-Kuhn-Tucker (KKT) conditions.
Finally, a numerical example is solved to demonstrate the effectiveness
and usefulness of the proposed strategy.