基于事件的未知输入和状态融合估计算法
Event-Based Unknown Input and State Fusion Estimation Algorithm
摘要: 针对具有未知输入的离散时变不确定系统，提出了一种基于动态事件触发的多传感器融合估计算法。为了降低数据传输过程中的能源消耗，采用一种动态事件触发协议来决定当前测量值是否传输到滤波器。在此基础上，给定相应约束条件以解耦未知输入，进而构造一个递归滤波器来同时估计局部状态和未知输入，然后在每个采样时刻获得局部状态和未知输入的误差协方差的上界。利用完全平方法和拉格朗日乘子法，获得能够最小化所得上界的滤波器增益。对于局部状态估计，利用协方差交叉(CI)融合估计方法得到新的状态融合估计，并给出基于CI的融合估计算法的一致性。最后，通过一个数值仿真，验证了所提融合估计算法的有效性。
Abstract: A multi-sensor fusion estimation algorithm based on dynamic event triggering is proposed for discrete-time time-varying uncertain systems with unknown inputs. In order to reduce the energy consumption during data transmission, a dynamic event-triggered protocol is used to determine whether the current measured value is transmitted to the filter. On this basis, given the corresponding constraints to decouple the unknown input, a recursive filter is constructed to estimate the local state and the unknown input at the same time, and then the upper bound of the error covariance of the local state and the unknown input at each sampling time is obtained. By using the complete square method and the Lagrangian multiplier method, the filter gains that can minimize the upper bound are obtained. For local state estimation, the covariance intersection (CI) fusion estimation method is used to obtain new state fusion estimation and the consistency of CI-based fusion estimation algorithm is given. Finally, a numerical simulation is carried out to verify the effectiveness of the proposed fusion estimation algorithm.
文章引用：扶苗苗, 邓军勇. 基于事件的未知输入和状态融合估计算法[J]. 运筹与模糊学, 2023, 13(1): 234-247. https://doi.org/10.12677/ORF.2023.131026

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