Distributed State Estimation for Large-Scale Systems with State Equality Constraints
作者单位：School of Information Science and EngineeringShandong Normal University
会议名称：《第43届中国控制会议》
会议日期：1000年
学科分类：080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学]
关 键 词：Distributed Kalman filter Large-scale systems Minimum mean square error State equality constraints
摘 要：This paper investigates the design of distributed Kalman filter for large-scale systems with state equality constraints in a Gaussian environment. Firstly,we design a unconstrained distributed estimator for a large-scale systems using the local information of different subsystems. The optimal gain is obtained based on the minimum mean square error estimation ***,we study distributed Kalman filter for large-scale systems that integrate equality constraints. In this approach,the solution from the unconstrained Kalman filter is projected onto the state-constrained surface at each time step to enhance the prediction accuracy of the filter in large-scale systems. Finally,the simulation experiments demonstrate that the effectiveness of the distributed Kalman filter for large-scale systems with state equality constraints outperforms unconstrained Kalman filter.