Stochastic Event-triggered Schedule Research Articles

Remote State Estimation With Posterior-Based Stochastic Event-Triggered Schedule

This paper aims to study the state estimation problem under the stochastic event-triggered (SET) schedule. A posterior-based SET mechanism is proposed, which determines whether to transmit data by the effect of the measurement on the posterior estimate. Since this SET mechanism considers the whole posterior probability density function, it has better information screening capability and utilization than the existing SET mechanisms that only consider the first-order moment information of measurement and prior estimate. Then, based on the proposed SET mechanism, the corresponding exact minimum mean square error estimator is derived by Bayes rule. Moreover, the prediction error covariance of the estimator is proved to be bounded under moderate conditions. Meanwhile, the upper and lower bounds on the average communication rate are also analyzed. Finally, two different systems are employed to show the effectiveness and advantages of the proposed methods.

Resource-efficient and secure distributed state estimation over wireless sensor networks: a survey

Wireless sensor networks (WSNs) are extensively adopted for remote monitoring and tracking scenarios, such as battlefield surveillance, target detection and tracking, traffic condition detection, power system monitoring and health monitoring, thanks to their promising benefits in terms of flexibility, reliability and cost-effectiveness. However, some critical WSN applications, such as intelligent transportation and smart grid monitoring, have stringent requirements in terms of resource budget and security. This paper provides a survey of the trending resource-efficient and secure techniques currently used with distributed estimation algorithms over WSNs. Recent progresses on these two major research trends are reviewed, respectively, for WSN-based monitoring systems. More specifically, the first part of the survey covers the state-of-the-art in resource-efficient distributed state estimation. The main results along this line of research are classified into protocol-based scheduling, static event-triggered scheduling, dynamic event-triggered scheduling and stochastic event-triggered scheduling. Then, in the second part, the latest results on secure distributed state estimation are reviewed, where secure distributed state estimation under data integrity attacks and data available attacks, and distributed attack detection are examined, respectively. Finally, several challenging issues in the context of distributed state estimation are discussed for potential future research.

Stochastic Event-Triggered Cubature Kalman Filter for Power System Dynamic State Estimation

Accurate dynamic state estimation (DSE) plays an important role in power systems. Although various filtering methods, such as unscented Kalman filter (UKF) and particle filter (PF), have been applied for DSE based on phasor measurement units, they occupy a huge communication bandwidth without specific concern. In order to alleviate this communication burden, the event-triggered cubature Kalman filter (CKF) is proposed based on the stochastic event-triggered schedule in this brief. Based on the developed nonlinear event-triggered schedule, the CKF further provides more accurate estimation than UKF and has lower computational complexity than PF. The proposed filter can effectively reduce the communication rate while ensuring the accuracy of filtering. Finally, the standard IEEE 145-bus system is utilized to verify the feasibility and performance of the proposed method.

Predictive cloud control for multiagent systems with stochastic event-triggered schedule

This paper addresses a predictive cloud control problem for a linear multiagent system with random network delays and noises. To reduce communication cost, a stochastic event-triggered schedule is introduced to decide whether current measurements need to be transmitted. An optimal state estimation algorithm is designed to compensate random network delays in the feedback channel. Subsequently, a predictive cloud control scheme is proposed for the multiagent system to achieve both stability and consensus. Simultaneously, random network delays in the forward channel is compensated actively. Sufficient and necessary conditions of stability and consensus for the closed-loop multiagent system are derived. Finally, a numerical example is provided to verify correctness and effectiveness of the proposed methods.

Remote Nonlinear State Estimation With Stochastic Event-Triggered Sensor Schedule.

This paper concentrates on the remote state estimation problem for nonlinear systems over a communication-limited wireless sensor network. Because of the non-Gaussian property caused by nonlinear transformation, the unscented transformation technique is exploited to obtain approximate Gaussian probability distributions of state and measurement. To reduce excessive data transmission, uncontrollable and controllable stochastic event-triggered scheduling schemes are developed to decide whether the current measurement should be transmitted. Compared with some existing deterministic event-triggered scheduling schemes, the newly developed ones possess a potential superiority in maintaining Gaussian property of innovation process. Under the proposed schemes, two nonlinear state estimators are designed based on the unscented Kalman filter. Stability and convergence conditions of these two estimators are established by analyzing behaviors of estimation error and error covariance. It is shown that an expected compromise between communication rate and estimation quality can be achieved by properly tuning event-triggered parameter matrix. Numerical examples are provided to testify the validity of the proposed results.

An Optimal LQG Controller for Stochastic Event-triggered Scheduling over a Lossy Communication Network

Abstract We consider a networked control loop in which the sensors acquire partial state information and communicate to a remote controller through a lossy communication network. A scheduler, collocated with the sensors, decides to transmit a locally estimated state to the controller based on an event-triggered transmission policy with stochastic thresholds. Assuming that the local estimator either senses the communication channel or receives an ideal acknowledgment from the remote estimator, then the optimal control law can be shown to be a linear function of the conditional expectation of the state. However, the probability distribution of the state conditioned on the information available to the controller based on the mentioned transmission policy and network is not Gaussian, but rather described by a sum of Gaussians with an increasing number of terms at every time-step. We show that the optimal LQG control law can be determined without tracking this probability distribution for finding its expected value. Moreover, we establish that the stochastic event-triggered scheduler can be appropriately regulated in order to achieve a desired triggering probability at every time-step.