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State Estimation for Systems With Packet Dropping and State Equality Constraints

机译:具有丢包和状态等式约束的系统的状态估计

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We consider the linear estimation problem for systems with packet dropping and equality constraints in this brief, where two kinds of constrained estimators will be designed. First, the constrained state estimator is derived based on the projection method and the unconstrained linear minimum mean square error estimator, and the estimator gains can be yielded in terms of the solutions to a Ricatti equation and a Lyapunov equation. However, when referring to the infinite horizon state estimation, there is a need to consider the convergence of the Lyapunov equation. To meet the requirement of the convergence analysis, the condition that the system matrix is stable should be imposed. Then, in order to remove the restrict condition that the system matrix is stable, another constrained state estimator is designed based on the projection method and time-stamp technique, where the estimator gains can be yielded only in terms of the solutions to a Riccati equation. Finally, we give two numerical examples to show that the second constraint estimator is more effective compared with the first constraint estimator.
机译:在本简介中,我们考虑具有丢包和相等约束的系统的线性估计问题,其中将设计两种约束估计器。首先,基于投影方法和无约束线性最小均方误差估计器,导出了约束状态估计器,并且可以根据Ricatti方程和Lyapunov方程的解获得估计器增益。但是,在提到无限水平状态估计时,需要考虑Lyapunov方程的收敛性。为了满足收敛性分析的要求,应施加系统矩阵稳定的条件。然后,为了消除系统矩阵稳定的限制条件,基于投影方法和时间戳技术设计了另一个约束状态估计器,其中估计器的增益只能根据Riccati方程的解得出。 。最后,我们给出两个数值示例,表明第二个约束估计器比第一个约束估计器更有效。

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