In this work, we address the problem of event-based data scheduling for multiple heterogeneous LTI control loops over a shared resource-constrained communication network. We introduce a novel bi-character scheduling scheme, which dynamically prioritizes the channel access at each time-step according to an error-dependent priority measure. Given local error thresholds for each control loop, the scheduling policy deterministically blocks the transmission from sub-systems with lower error values. The scheduler then allocates the limited communication resource probabilistically among the eligible sub-systems based on a prioritized measure. We prove stochastic stability of the networked control system under the proposed scheduler in terms of f-ergodicity of the overall network-induced error. Uniform analytical performance bounds are further derived for an average cost function comprised of a quadratic error term and transmission penalty. The simulation results show that our approach results in a significant reduction of the aggregate network-induced error variance compared to the conventional scheduling protocols. 

This paper investigates the optimal design of event-triggered estimation for linear systems. The synthesis approach is posed as a team decision problem where the decision makers are given by the event trigger and the estimator. The event-trigger decides upon its available measurements whether the estimator shall obtain the current state information by transmitting it through a resource constrained channel. The objective is to find the optimal tradeoff between the mean square estimation error and the expected number of transmissions over a finite horizon. After deriving basic characteristics of the optimal solution, we propose an iterative algorithm that alternates between optimizing one decision maker while fixing the other and vice versa. By analyzing the dynamical behavior of the iterative method, it is shown that the algorithm converges to a symmetric threshold policy for first-order systems if the statistics of the uncertainties are even and unimodal. In the case of bimodal distributions, we show numerically that the iterative method may find asymmetric threshold policies that outperform symmetric rules.

Next generation mobile networks are envisioned to provide support for real-time control applications. One of the main aspects of these systems is that the location of the controller may be separated from the location of sensing and actuation. This promises benefits in terms of an increased flexibility, lower costs due to resource sharing, and higher computational capabilities. This paper focuses on one aspect of such systems, specifically, the controller handover. During a controller handover, a control process is moved from one point of computation to another at runtime. A possible reason for performing such a handover is to move the control process to a controller with better channel conditions. The safety of the handover is analyzed using a probabilistic reachability analysis by modeling the handover procedure as a stochastic hybrid system. Based on this safety analysis, a safety-oriented handover triggering rule is proposed. This triggering rule is shown to be dependent on the instantaneous state of the plant, in contrast to handover in mobile networks where it is only dependent on the state of the communication links. A vehicle platoon is considered as an example scenario, which is controlled by a base station of a mobile network. While driving, the platoon will move out of the communication range of the base station, so the control process needs to be moved to the next base station. Simulations illustrate the conditions for a safe execution of so called hard and soft handover protocols.

This paper analyzes the joint impact of uncertainties in the input data on the power system state estimator. The approach is based on the sensitivity analysis of the estimated telemetry data with respect to the measurement data and the branch parameters with the main goal of locating relevant input components. In order to find relevant inputs, we analyze the normalized sensitivity matrix by sparse principal component analysis (PCA). The non-zero entries of the loading vectors related to the dominant principal components are considered to be the relevant inputs to the state estimator as they mainly contribute to the amplification of the estimated values. It turns out that PCA shows an elementary structure of the sensitivity matrix: All non-zero entries of a loading vector corresponding to a positive singular value belong either to the telemetry data or to the branch data. We show that this property is also valid for PCA with different sparsity-promoting constraints on the loading vector. The proposed analysis method is demonstrated by a numerical study.

This paper investigates the design of event-triggered scheduling and medium access control for the real-time coordination of multiple vehicles through an infrastructure node. The key motivation of our proposed event-triggered mechanism is to concurrently address safety aspects of the vehicle control and the efficient usage of network resources of the vehicle-to-infrastructure (V2I) protocol. While the real-time guarantees needed for safety are achieved by a novel coordination scheme in the medium access layer, the event-triggered mechanism improves the real-time performance of the control task. The coordination scheme enabled through the topology of the V2I network limits the number of successive data dropouts and we prove stability of the estimator at the infrastructure that monitors the state of the vehicle group. Numerical studies on a platooning case study validate our theoretical results.

This paper is concerned with linear state estimation in sensor networks with an event-triggered exchange of information. It is assumed that each sensor node transmits its local estimate to a fusion center whenever an appropriately chosen error norm exceeds a threshold. The fusion rule is a modified version of the Covariance Intersection Algorithm. We investigate how to incorporate the event information of not having transmitted at the fusion center such that the filter remains unbiased and consistent with regard to its error covariance. An upper bound on the error covariance matrix is derived by exploiting the structure of the posterior probability distribution. This enables us to replace the event information by the virtual transmission of consistent local estimates. Based on the consistency-preserving property of the proposed scheme, we show stability of the event-triggered state estimator in terms of a bounded mean square error.

We present an innovations-based prioritization mechanism to efficiently use network resources for data gathering, without compromising the real-time decision making capability of the control systems. In the envisioned protocol, each sensor assigns the Value of Information (VoI) contained in its current observations for the network as the priority. Tournaments are used to compare priorities and assign transmission slots, like in the CAN bus protocol. By using a rollout strategy, we derive feasible algorithms for computing the VoI-based priorities for the case of coupled and decoupled systems. In the case of decoupled systems, performance guarantees with regard to the control cost of the VoI-based strategy are identified. We illustrate the efficiency of the proposed approach on a platooning example in which the vehicles receive measurements from multiple sensors.