In the design of cyber-physical systems (CPS) where multiple physical systems are coupled via a communication network, a key aspect is to study how network services are distributed. In this letter, we first describe a cross-layer model for CPS to explicitly capture the coupling between control and networking and the time-sensitive requirements of each physical system. Physical systems processes are coupled via a shared network that provides a diverse range of cost-prone and capacity-limited services with distinct latency characteristics. Service prices are given such that low latency services incur higher communication cost, and prices remain fixed over a constant period of time but will be adjusted by the network for the future time periods. Physical systems decide to use specific services over each time interval depending on the service prices and their own time sensitivity requirements. Considering the service availability, the network coordinates resource allocation such that physical systems are serviced the closest to their preferences. Performance of individual systems are measured by an expected quadratic cost and we formulate a social optimization problem subject to time-sensitive requirements of the physical systems and the network constraints. From the formulated social optimization problem, we derive the joint optimal time-sensitive control and service allocation policies.

The paper proposes a practical method for a significant dimensionality reduction of Volterra kernels, defining a discrete nonlinear model of a signal by Volterra series of higher order. In system identification of Volterra series, the Volterra kernels and nonlinear inputs of the system can be described by super-symmetrical tensors. The reduction of their dimensionality is obtained by a tensor decomposition technique called Higher Order Singular Value Decomposition (HOSVD). The main contribution of the paper is a cascade learning algorithm for the system identification based on residuals of least squares minimization. Numerical examples for Volterra system of order four are used to illustrate the approach. 

This paper proposes an event-triggered add-on safety mechanism to adjust the control parameters for timely braking in a networked vehicular system while maintaining maneuverability. Passenger vehicle maneuverability is significantly affected by the combined slip friction effect, in which larger longitudinal tire slips result in considerable drop in lateral tire forces. This is of higher importance when unexpected dangerous situations occur on the road and immediate actions, such as braking, need to be taken to avoid collision. Harsh braking can lead to high-slip and loss of maneuverability; hence, timely braking is essential to reduce high-slip scenarios. In addition to the vehicles own active safety systems, the proposed event-triggered add-on safety is activated upon being informed about dangers by the road-side infrastructure. The aim is to incorporate the add-on safety feature to adjust the automatic control parameters for smooth and timely braking such that a collision is avoided while vehicle's maneuverability is maintained. We study two different wireless technologies for communication between the infrastructure and the vehicles, the Long-Term Evolution (LTE) and the fifth generation (5G) schemes. The safety advantages of the proposed framework is validated through high-fidelity software simulations. Copyright

This paper studies an optimal decision fusion problem with a group of human decision makers when an order effect is present. The order effect refers to situations wherein the process of decision making by a human is affected by the order of decisions. In our set-up, all human decision makers, called observers, receive the same data which is generated by a common but unknown hypothesis. Then, each observer independently generates a sequence of decisions which are modeled by employing non-commutative probabilistic models of the data and their relation to the unknown hypothesis. The use of non-commutative probability models is motivated by recent psychological studies which indicate that these non-commutative probability models are more suitable for capturing the order effect in human decision making, compared with the classical probability model. A central decision maker (CDM) receives (possibly a subset of) the observers' decisions and decides on the true hypothesis. The considered problem becomes an optimal decision fusion problem with observations modeled using a non-commutative (Von Neumann) probability model. The structure of the optimal decision rule at the CDM is studied under two scenarios. In the first scenario, the CDM receives the entire history of the observers' decisions whereas in the second scenario, the CDM receives only the last decision of each observer. The perfromance of the optimal fusion rule is numerically evaluated and compared with the optimal fusion rule derived when using a classical probability model.

Timeliness is an emerging requirement for cyber-physical systems, where the value of information can quickly diminish with time. Nevertheless, there are different imperfections and constraints that hinder the immediate access of decision makers to the latest states of such systems. This obliges the designers of these systems to study the impact of information staleness on the control performance. In this paper, we focus on control with stale information and study a trade-off between the information staleness and control performance. To this purpose, we design a test channel in which the staleness of observations is chosen deliberatively. This test channel should be regarded as an abstract model that allows us to obtain the achievable region in our trade-off analysis. Based on this trade-off, the performance of any communication channel with time-varying delay used for control applications can be assessed, and the maximum staleness that is tolerable for stability can be specified. 

A framework for the event-triggered control synthesis under signal temporal logic (STL) tasks is proposed. In our previous work, a continuous-time feedback control law was designed, using the prescribed performance control technique, to satisfy STL tasks. We replace this continuous-time feedback control law by an event-triggered controller. The event-triggering mechanism is based on a maximum triggering interval and on a norm bound on the difference between the value of the current state and the value of the state at the last triggering instance. Simulations of a multi-agent system quantitatively show the efficacy of using an event-triggered controller to reduce communication and computation efforts.

In the design of shared resource networked control systems (NCSs), resource managers play an important role to appropriately allocate limited resources across the distributed system. They are often used to fairly distribute the limited bandwidth among the medium-sharing entities at the expense of delaying or discarding unnecessary data samples. Considering the rapidly growing volume of information being exchanged, a relevant scenario for efficient resource management is state-dependent data buffering via network queues. In this paper, we propose state-dependent data buffering for shared-resource NCSs, such that the buffer state, i.e. queue length, can be controlled depending on the real-time conditions of both the control systems and the communication network. We consider that the transmission decisions at the sensor sides are taken by event-based schedulers, and those data eventually sent for transmission are queued and processed depending on the available communication resource. We derive sufficient conditions under which the NCS with the proposed cross-layer transmission scheme is stable in almost sure mean-square sense. Moreover, we show performance improvements resulting from our proposed design in comparison with its state-independent counterpart.

We study asymptotic dynamical patterns that emerge among a set of nodes interacting in a dynamically evolving signed random network, where positive links carry out standard consensus and negative links induce relative-state flipping. A sequence of deterministic signed graphs defines potential node interactions that take place independently. Each node receives a positive recommendation consistent with the standard consensus algorithm from its positive neighbors, and a negative recommendation defined by relative-state flipping from its negative neighbors. After receiving these recommendations, each node puts a deterministic weight to each recommendation, and then encodes these weighted recommendations in its state update through stochastic attentions defined by two Bernoulli random variables. We establish a number of conditions regarding almost sure convergence and divergence of the node states. We also propose a condition for almost sure state clustering for essentially weakly balanced graphs, with the help of several martingale convergence lemmas. Some fundamental differences on the impact of the deterministic weights and stochastic attentions to the node state evolution are highlighted between the current relative-stateflipping model and the state-flipping model considered in Shi et al., IEEE Transaction on Control of Network Systems, 2015.