Kernel-Based Simultaneous Parameter-State Estimation for Continuous-Time Systems
Kernel-Based Simultaneous Parameter-State Estimation for Continuous-Time Systems
复制标题
连续时间系统基于内核的同步参数状态估计
DOI：
10.1109/tac.2019.2953146
复制
发表时间：
2020
影响因子：
6.8
通讯作者：
T. Parisini
中科院分区：
文献类型：
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作者：
Peng Li;F. Boem;Gilberto Pin;T. Parisini
研究了连续时间系统状态和参数的联合估计问题。利用适当设计的Volterra积分算子，该估计器不需要可测信号的时间导数的可用性，并且消除了对未知初始条件的依赖。因此，在无噪声的情况下，估计在任意短的时间内收敛到真实值。在存在有界测量和过程扰动的情况下，估计误差是有界的。讨论了数值实现方面的问题，并提供了大量的仿真结果，证明了该估计器的有效性。
In this article, the problem of jointly estimating the state and the parameters of continuous-time systems is addressed. Making use of suitably designed Volterra integral operators, the proposed estimator does not need the availability of time derivatives of the measurable signals, and the dependence on the unknown initial conditions is removed. As a result, the estimates converge to the true values in arbitrarily short time in a noise-free scenario. In the presence of bounded measurement and process disturbances, the estimation error is shown to be bounded. The numerical implementation aspects are dealt with, and extensive simulation results are provided showing the effectiveness of the estimator.