Event-triggered recursive state estimation for dynamical networks under randomly switching topologies and multiple missing measurements

• Published in: Automatica (Oxford) 2020-05, Vol.115, p.108908, Article 108908
• Main Authors: Hu, Jun, Wang, Zidong, Liu, Guo-Ping, Jia, Chaoqing, Williams, Jonathan
• Format: Article
• Language: English
• Subjects: Event-based communication mechanism; Monotonicity analysis; Multiple missing measurements; Randomly switching topologies; Recursive state estimation; Time-varying dynamical networks
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2020.108908

In this paper, the design problem of recursive state estimator is discussed for a class of coupled nonlinear dynamical networks with randomly switching topologies and multiple missing measurements under the event-triggered mechanism. A sequence of random variables obeying the Bernoulli distribution with certain occurrence probabilities is adopted to model the multiple missing measurements and the random change manners of the network topologies. The event-based communication protocol is introduced to adjust the transmission frequency, thereby improving the energy utilization efficiencies of the communication networks. The objective of the addressed variance-constrained estimation problem is to construct a recursive state estimator such that, in the simultaneous presence of event-based transmission strategy, randomly switching topologies as well as multiple missing measurements, a locally optimal upper bound is guaranteed on the estimation error covariance by properly determining the estimator gain, where the desired estimator gain matrix is formulated via the solutions to certain recursive matrix equations. Besides, theoretical analysis is conducted on the monotonicity regarding the missing probabilities of degraded measurements and the obtained upper bound matrix. Finally, some simulations with comparisons are carried out to demonstrate the effectiveness and feasibility of proposed event-triggered state estimation method.