基于事件的未知输入和状态联合估计
Event-Based Joint Estimation for Unknown Inputs and States
摘要: 本文研究了一类基于事件触发机制的具有随机非线性的离散时变系统的未知输入和系统状态的联合估计问题。为了节约网络资源，采用自适应事件触发机制来控制传感器与估计器之间的信号传输，即只有在满足一定的触发条件时，传感器才将数据释放到滤波器端。首先，引入了一个约束条件来解耦状态和未知输入。然后，利用数学归纳法，通过求解耦合的类黎卡蒂差分方程，分别得到状态和未知输入的滤波误差协方差的上界。随后，通过最小化滤波误差协方差的上界，获得了所需的估计器增益。最后，通过数值仿真验证了所提出的联合估计器设计方案的有效性。
Abstract: This paper addresses the joint estimation problem for both unknown inputs and system states for a class of discrete time-varying systems with stochastic nonlinearities under the dynamic event- triggering mechanism. For the purpose of energy-saving, the event-triggering mechanism is adopt-ed to govern the signal transmission between the sensor and the estimator, under which the data are released to the filter only when certain triggering condition is satisfied. First, some constraint conditions are introduced to decouple the state and the unknown input. Then, by means of mathe-matical induction, an upper bound of the filtering error covariance is individually obtained for the state and the unknown input by solving coupled Riccati-like difference equations. Subsequently, the required estimator gains are acquired by minimizing the obtained upper bounds of filtering error covariances. Finally, a numerical simulation is given to illustrate the effectiveness of the proposed joint estimator design scheme.
文章引用：扶苗苗, 邓军勇. 基于事件的未知输入和状态联合估计[J]. 应用数学进展, 2022, 11(9): 6787-6798. https://doi.org/10.12677/AAM.2022.119719

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