In this study, we present a method for online estimation of the mean performance output in microgrid systems subject to high-dimensional and dynamic uncertainties. We integrate an efficient Multivariate Probabilistic Collocation Method (MPCM) based sampling strategy with a Copula-based conditional probability distribution. This integrated method enables online evaluation of system outputs with high estimation accuracy and efficiency. The online evaluation algorithm is developed, and its theoretical analysis is provided. Real Time Digital Simulator (RTDS) experiments validate the method, demonstrating its feasibility for practical applications.

This abstract describes the project funded by the IEEE SMCS on Transforming Educational Assets and Materials (TEAM) in Systems, Man, and Cybernetics. The project develops an open course on Cyber Physical Systems (CPS) Foundation and Design for Unmanned Aerial Vehicles (UAVs). The course will be available to the public and serve the need of researchers, students and professionals who are interested in conducting UAVs related research. The open course contains integrated modules on control, communication and networking, computing, and artificial intelligence (AI) to provide trainees a comprehensive knowledge needed for UAVs. The course is self-paced and contains quizzes in each module for help students assess the quality of learning and also allow course designers to evaluate the effectiveness of the course materials for continuous improvement. The open course promotes CPS which is a SMCS technical field. It will also attract students and professionals to the SMC community.

In this study, we investigate the resilience of DC microgrids in the face of disturbances that could induce boost converter failures. We associate the converter failure conditions with disturbances and implement a power buffer control system, which prevents voltage collapse and promotes system stability. A new resilience model is proposed that considers general power mismatches for a comprehensive resilience evaluation. We further evaluate the resilience of an interconnected DC microgrid where the stability of the system is ensured through proofs and examine the role of power buffer control in enhancing resilience against disturbances. The results validate the significance of power buffer control in augmenting DC microgrid resilience. The hardware-in-the-loop experiment study demonstrates over 32% improvement of resilience using the proposed control.

The influence model (IM) is a reduced-order stochastic network model that captures the spatiotemporal dynamics in a network of interactive Markov chains. Identifiability and reduced-order estimation of the IM from observation data are crucial for IM applications. Despite the tractability of IM analysis with its reduced-order representation, the identifiability and estimation of IM are challenging due to the tight coupling of both network and local level interactions. The limited identifiability studies in the literature only apply to homogeneous IMs and existing methods for IM estimation incur high-computational cost. In this article, we solve the identifiability problem by providing succinct if-and-only-if conditions for both the homogeneous and heterogeneous IMs. This is obtained through a structural analysis that establishes a novel connection between the high-order and low-order representations of the IMs. The identifiability analysis further leads to reduced-order parameter estimation algorithms of the homogeneous and heterogeneous IMs with reduced computation.

We study the model-based dynamic event-triggered distributed control for linear physically interconnected systems. For each subsystem, a distributed event-triggered control law, along with a model-based dynamic event-triggering mechanism, is proposed. The resulting closed-loop system is shown to be exponentially stable. A positive minimum interevent time excludes the Zeno behavior for each subsystem and is shown to be larger than the one guaranteed by the conventional zero-order-hold approach. Numerical studies on coupled inverted pendulums and experimental results on networked power buffers validate the proposed methodology.

This article considers autonomous systems whose behaviors seek to optimize an objective function. This goes beyond standard applications of condition-based maintenance, which seeks to detect faults or failures in nonoptimizing systems. Normal agents optimize a known accepted objective function, whereas abnormal or misbehaving agents may optimize a renegade objective that does not conform to the accepted one. We provide a unified framework for anomaly detection and correction in optimizing autonomous systems described by differential equations using inverse reinforcement learning (RL). We first define several types of anomalies and false alarms, including noise anomaly, objective function anomaly, intention (control gain) anomaly, abnormal behaviors, noise-anomaly false alarms, and objective false alarms. We then propose model-free inverse RL algorithms to reconstruct the objective functions and intentions for given system behaviors. The inverse RL procedure for anomaly detection and correction has the training phase, detection phase, and correction phase. First, inverse RL in the training phase infers the objective function and intention of the normal behavior system using offline stored data. Second, in the detection phase, inverse RL infers the objective function and intention for online observed test system behaviors using online observation data. They are then compared with that of the nominal system to identify anomalies. Third, correction is executed for the anomalous system to learn the normal objective and intention. Simulations and experiments on a quadrotor unmanned aerial vehicle (UAV) verify the proposed methods.

This article investigates differential graphical games for linear multiagent systems with a leader on fixed communication graphs. The objective is to make each agent synchronize to the leader and, meanwhile, optimize a performance index, which depends on the control policies of its own and its neighbors. To this end, a distributed adaptive Nash equilibrium solution is proposed for the differential graphical games. This solution, in contrast to the existing ones, is not only Nash but also fully distributed in the sense that each agent only uses local information of its own and its immediate neighbors without using any global information of the communication graph. Moreover, the asymptotic stability and global Nash equilibrium properties are analyzed for the proposed distributed adaptive Nash equilibrium solution. As an illustrative example, the differential graphical game solution is applied to the microgrid secondary control problem to achieve fully distributed voltage synchronization with optimized performance.

Consensus-based distributed Kalman filters for estimation with targets have attracted considerable attention. Most of the existing Kalman filters use the average consensus approach, which tends to have a low convergence speed. They also rarely consider the impacts of limited sensing range and target mobility on the information flow topology. In this article, we address these issues by designing a novel distributed Kalman consensus filter (DKCF) with an information-weighted consensus structure for random mobile target estimation in continuous time. A new moving target information-flow topology for the measurement of targets is developed based on the sensors' sensing ranges, targets' random mobility, and local information-weighted neighbors. Novel necessary and sufficient conditions about the convergence of the proposed DKCF are developed. Under these conditions, the estimates of all sensors converge to the consensus values. Simulation and comparative studies show the effectiveness and the superiority of this new DKCF.

In this paper, a three-level decision-making framework is developed to generate safe and effective decisions for autonomous vehicles (AVs). A key component in this decision framework is a normal-form game to capture the interactions between the ego vehicle and its surrounding vehicles. The payoffs in the normal-form game are designed to capture both safety reward and the reward gained by obeying (or the price paid by violating) “soft” traffic rules, e.g., first-come-first-go. This game formulation enables the ego to 1) make appropriate decisions considering the payoffs and possible actions of its surrounding vehicles, and 2) take intelligent actions in emergencies that may sacrifice some soft traffic rules to ensure safety. Moreover, we introduce parameters in the payoff matrix to tune the ego vehicle’s behavior, e.g., aggressiveness level. A neural network is developed to learn the tuning parameters via supervised learning. In addition, to enable the ego to respond timely to different surrounding vehicles’ driving styles, driving style characterization is incorporated into the payoff design for the normal-form game. Simulation studies are conducted to demonstrate the performance of the developed algorithms in two-vehicle intersection-crossing and lane-changing scenarios.

This article investigates distributed event-triggered control (ETC) of power buffers in a direct current (DC) microgrid. In order to facilitate the control design, a linear interconnected system model is derived that captures the physical coupling among power buffers. Then, a distributed ETC law regulates the stored energy and input impedance of each power buffer, and a decentralized dynamic event-triggering mechanism determines when each power buffer communicates with its neighboring buffers. This strategy eliminates the need for both continuous controller updates and continuous communication among the power buffers. The resulting closed-loop system is shown to be exponentially stable under a mild assumption on the communication network. The proposed event-triggering mechanism guarantees not only the exclusion of the Zeno behavior but also the existence of a positive minimum interevent time that can be adjusted by the control design parameters. Simulation studies validate the effectiveness of the proposed theoretical results for a multibuffer DC microgrid.

In this article, a general approach for directed graph clustering and two new density-based clustering objectives are presented. First, using an equivalence between the clustering objective functions and a trace maximization expression, the directed graph clustering objectives are converted into the corresponding weighted kernel$k$-means problems. Then, a nonspectral algorithm, which covers both the direction and weight information of the directed graphs, is thus proposed. Next, with Rayleigh’s quotient, the upper and lower bounds of clustering objectives are obtained. After that, we introduce a new definition of weak links to characterize the effectiveness of clustering. Finally, illustrative examples are given to demonstrate effectiveness of the results. This article provides a glance at the potential connection between density-based and pattern-based clustering. Compared with other approaches for directed graph clustering, the method proposed in this article naturally avoids the loss of the nonsymmetric edge data because there is no need for any additional symmetrization.

Federated learning (FL) is a promising machine learning paradigm because it allows distributed edge devices to collaboratively train a model without sharing their raw data. In practice, a major challenge to FL is that edge devices are heterogeneous, so slow devices may compromise the convergence of model training. To address such a challenge, several recent studies have suggested different solutions, in which a promising scheme is to utilize coded computing to facilitate the training of linear models. Nevertheless, the existing coded FL (CFL) scheme is limited by a fixed coding redundancy parameter, and a weight matrix used in the existing design may introduce unnecessary errors. In this paper, we tackle these issues and propose a novel framework, namely CFL-HC, to facilitate CFL in heterogeneous computing scenarios. In our framework, we consider a computing system consisting of a central server and multiple computing devices with original or coded datasets. Then we specify an expected number of input-output pairs that are used in one round. Within such a framework, we formulate an optimization problem to find the best deadline of each training round and the optimal size of the computing task allocated to each computing device. We then design a two-step optimization scheme to obtain the optimal solution. To evaluate the proposed framework, we develop a real CFL system using the message passing interface platform. Based on this system, we conduct numerical experiments, which demonstrate the advantages of the proposed framework, in terms of both accuracy and convergence speed.

Mobile ad hoc computing (MAHC), which allows mobile devices to directly share their computing resources, is a promising solution to address the growing demands for computing resources required by mobile devices. However, offloading a computation task from a mobile device to other mobile devices is a challenging task due to frequent topology changes and link failures because of node mobility, unstable and unknown communication environments, and the heterogeneous nature of these devices. To address these challenges, in this paper, we introduce a novel coded computation scheme based on multi-agent reinforcement learning (MARL), which has many promising features such as adaptability to network changes, high efficiency and robustness to uncertain system disturbances, consideration of node heterogeneity, and decentralized load allocation. Comprehensive simulation studies demonstrate that the proposed approach can outperform state-of-the-art distributed computing schemes.

Random mobility models (RMMs) capture the random mobility patterns of mobile agents, and have been widely used as the modeling framework for the evaluation and design of mobile networks. All existing RMMs in the literature assume independent movements of mobile agents, which does not hold for unmanned aircraft systems (UASs). In particular, UASs must maintain a safe separation distance to avoid collision. In this paper, we propose a new modeling framework of random mobility models equipped with physical sense-and-avoid protocols to capture the flexible, variable, and uncertain movement patterns of UASs subject to separation safety constraints. For the random direction (RD) RMM equipped with a commonly used sense-and-avoid (S&A) protocol, named sense-and-stop (S&S), we provide its statistical properties including stationary location distribution and stationary inter-vehicle distance distribution, using the Markov analysis. This study provides knowledge on the impact of S&A protocols to critical UAS networking statistics. In addition, we define collision probabilities and airspace capacity concepts for UASs based on the inter-vehicle distance distribution, and derive their closed-form expressions. This analytical framework mathematically bridges local autonomy with global airspace capacity, and allows the impact analysis of local autonomy configurations for effective UAS airspace capacity management.

Advances of unmanned aircraft system (UAS) technology have spurred a rapid investment of commercial UAS use in broad public domains, such as cargo transport, agriculture support, emergency response, on-demand communication, and infrastructure health monitoring. Urban unmanned aerial transportation that can transport passengers over short distances is also on the way. With the forthcoming dense operations of UAS particularly over urban regions, ensuring airspace safety becomes an urgent issue.

The influence model is a discrete-time stochastic model that succinctly captures the interactions of a network of interacting Markov chains. The model produces a reduced-order representation of stochastic networks, and can be used to describe and tractably analyze probabilistic spatiotemporal spread dynamics, and hence has found broad usage in network applications, such as social networks, traffic management, and failure cascades in power systems. This article provides sufficient and necessary conditions for the identifiability of the influence model, and also develops estimators for model structures through exploiting the model's special properties. In addition, we analyze conditions for the identifiability of the partially observed influence model (POIM), for which not all of the sites can be measured. We develop an expectation-maximization (EM) algorithm-based estimator for POIMs.

In recent years, we have witnessed fast‐growing unmanned aerial systems (UAS) based applications. To better facilitate these applications, many efforts have been made to enhance the capability of UAS from various aspects, including communications, control and networking, and so on. Nevertheless, most of these studies neglect the computation aspect. Recently, the UAS‐enabled mobile edge computing (MEC) has attracted increasing research attention, which utilises UAS with onboard computing capability to provide on‐demand computing services for mobile users. However, existing research on UAS‐enabled MEC remains at the theory stage and how to design a UAS platform with advanced onboard computing capability has not been addressed. In this study, the authors aim to fill this research gap and design an open UAS‐based airborne computing platform with advanced onboard computing capability. This platform was designed from three aspects: hardware, software, and applications. In particular, feasible computing hardware to perform UAS onboard computing is first considered and a prototype is then designed. To enhance the flexibility and programmability of the platform, two key virtualisation techniques are then investigated. Finally, they test the performance of their prototype by executing real UAS onboard computing tasks, the results of which verify the feasibility and potentials of the proposed airborne computing platform.

In this paper, the robust formation control problem is investigated for a group of quadrotors. Each quadrotor dynamics exhibits the features of underactuation, high nonlinearities and couplings, and disturbances in both the translational and rotational motions. A distributed robust controller is developed, which consists of a position controller to govern the translational motion for the desired formation and an attitude controller to control the rotational motion of each quadrotor. Theoretical analysis and simulation studies of a formation of multiple uncertain quadrotors are presented to validate the effectiveness of the proposed formation control scheme.

In this paper, the fault-tolerant time-varying formation control problem for a group of tail-sitters with multiple actuator faults and uncertainties is studied. A robust distributed fault-tolerant formation control strategy is developed to achieve aggressive time-varying formation flying in flight mode transitions. For each tail-sitter, the designed controller can be divided into an inner attitude controller and an outer position controller to govern the rotational and translational motions, respectively. The information of the actuator faults does not need to be identified online and the tracking errors of the global closed-loop control system can converge into a given neighborhood of the origin in a finite time. Simulation results are presented to show the effectiveness of the proposed control strategy.

Control-theoretic differential games have been used to solve optimal control problems in multiplayer systems. Most existing studies on differential games either assume deterministic dynamics or dynamics corrupted with additive noise. In realistic environments, multidimensional environmental uncertainties often modulate system dynamics in a more complicated fashion. In this article, we study stochastic multiplayer differential games, where the players' dynamics are modulated by randomly time-varying parameters. We first formulate two differential games for systems of general uncertain linear dynamics, including the two-player zero-sum and multiplayer nonzero-sum games. We then show that optimal control policies, which constitute the Nash equilibrium solutions, can be derived from the corresponding Hamiltonian functions. Stability is proven using the Lyapunov type of analysis. In order to solve the stochastic differential games online, we integrate reinforcement learning (RL) and an effective uncertainty sampling method called the multivariate probabilistic collocation method (MPCM). Two learning algorithms, including the on-policy integral RL (IRL) and off-policy IRL, are designed for the formulated games, respectively. We show that the proposed learning algorithms can effectively find the Nash equilibrium solutions for the stochastic multiplayer differential games.

Aerial communication using directional antennas (ACDA) is a promising solution to enable long-distance and broad-band unmanned aerial vehicle (UAV)-to-UAV communication. The automatic alignment of directional antennas allows transmission energy to focus in certain direction and hence significantly extends communication range and rejects interference. In this paper, we develop reinforcement learning (RL)-based on-line directional antennas control solutions for the ACDA system. The novel stochastic optimal control algorithm integrates RL, an effective uncertainty evaluation method called multivariate probabilistic collocation method (MPCM), and unscented Kalman Filter (UKF) for the nonlinear random switching dynamics. Simulation studies are conducted to illustrate and validate the proposed solutions.

In this paper, we study consensus in distributed sensor networks (DSN) with communication time delays. In particular, we focus on the DSN with multi-layer multi-group (MLMG) communication structures and show that consensus can be achieved even in the presence of arbitrarily large but bounded time-varying communication delays. Moreover, we explicitly characterize the final consensus value for the case where communication time delays are fixed.

In this paper, we study the design and development of airborne WiFi networks through directional antennae. Specifically, we conduct an experimental study to investigate the feasibility of transmitting WiFi signals over two unmanned aerial vehicles (UAVs). WiFi has become the de facto configuration for most communication devices, from personal smartphones to industrial instruments. The integration of WiFi signals with airborne networks will enable a fast deployment of WiFi infrastructures, which provide real-time communication for disaster scenarios where a communication infrastructure does not exist. One unique feature of this work is the use of directional antennae for long range WiFi signal transmission. Directional antennae are considered because they not only boost the signal strength, but also have the potential to reduce interference with other WiFi channels. However, the performance of communication using directional antennae depends highly on the alignment of facing directions, which requires online control in response to the movement of UAVs. In our experimental study, we develop two hexacopters that are equipped with NanoStations. They maintain correct facing and thus connectivity through an automatic mechanical heading control. Field tests are conducted to understand how distance impacts the WiFi signal throughput. The experimental study suggests the promising use of directional antennas for WiFi aerial communication, and also discloses challenges to enable robust WiFi airborne networks.

Traditional consensus approaches involve a high communication cost because every node in the network will participate the information exchange with its neighbors. In this paper, we propose a new distributed average consensus with consideration of the network topology. More specifically, the information exchange only involves the connected dominating set (CDS) of the original graph. The nodes not in the CDS will only passively update their states according to the information received from the nodes in CDS. With the introducing of CDS, the overall power consumption will be reduced significantly because of less nodes transmitting signals. CDS determination algorithm has fixed number of procedures, which facilitates the estimation of the additional power consumption for the distributed wireless sensor networks. Numerical results show that the number of nodes involved in the consensus is reduced by half. It is also shown that the new consensus schemes over CDS not only achieve the similar performance as that over the original graph, but also demonstrate potential of faster convergence.

In this paper, we study properties of distributed consensus in layered sensor networks of the multi-layer multi-group (MLMG) structure. We show that properly designed MLMG networks maintain decentralized communication, whereas show the advantage of centralized structures. In particular, they require less number of transmissions required to reach consensus. This feature is critical for efficient distributed computing in large-scale sensor network applications. For typical classes of MLMG networks, we mathematically characterize the reduced number of transmissions compared to equivalent egalitarian decentralized structures of the same consensus dynamics. This explicit characterization based on simple graphical characteristics of MLMG structures permits an efficient design of large-scale network structures to meet desired performance requirements. In addition, we characterize the asymptotic and transient properties of consensus in MLMG networks of limited channel rates, using the probabilistic quantization schemes.

Modern large-scale infrastructure systems have typical complicated structure and dynamics, and extensive simulations are required to evaluate their performance. The probabilistic collocation method (PCM) has been developed to effectively simulate a system's performance under parametric uncertainty. In particular, it allows reduced-order representation of the mapping between uncertain parameters and system performance measures/outputs, using only a limited number of simulations; the resultant representation of the original system is provably accurate over the likely range of parameter values. In this paper, we extend the formal analysis of single-variable PCM to the multivariate case, where multiple uncertain parameters may or may not be independent. Specifically, we provide conditions that permit multivariate PCM to precisely predict the mean of original system output. We also explore additional capabilities of the multivariate PCM, in terms of cross-statistics prediction, relation to the minimum mean-square estimator, computational feasibility for large dimensional parameter sets, and sample-based approximation of the solution. At the end of the paper, we demonstrate the application of multivariate PCM in evaluating air traffic system performance under weather uncertainties.

We introduce a queuing network model that can comprehensively represent traffic flow dynamics and flow management capabilities in the U.S. National Airspace System (NAS). We envision this model as a framework for tractably evaluating and designing coordinated flow management capabilities at a multi-Center or even NAS-wide spatial scale and at a strategic (2-15 h) temporal horizon. As such, the queuing network model is expected to serve as a critical piece of a strategic flow contingency management solution for the Next Generation Air Traffic System (NextGen). Based on this perspective, we outline, in some detail, the evaluation and design tasks that can be performed using the model, as well as the construction of the flow network underlying the model. Finally, some examples are presented, including one example that replicates traffic in Atlanta Center on an actual bad-weather day, to illustrate simulation of the model and interpretation/use of model outputs.

We study how the dependence of a simulation output on an uncertain parameter can be determined when simulations are computationally expensive and so can only be run for very few parameter values. Specifically, the methodology that is developed-known as the probabilistic collocation method (PCM)-permits selection of these few parameter values, so that the mapping between the parameter and the output can be approximated well over the likely parameter values, using a low-order polynomial. Several new analyses are developed concerning the ability of PCM to predict the mapping structure, as well as output statistics. A holistic methodology is also developed for the typical case where the uncertain parameter's probability distribution is unknown, and instead, only depictive moments or sample data (which possibly depend on known regressors) are available. Finally, the application of PCM to weather-uncertainty evaluation in air traffic flow management is discussed.

As congestion in the United States National Airspace System (NAS) increases, coordination of en route and terminal-area traffic flow management procedures is becoming increasingly necessary to prevent controller workload excesses without imposing excessive delay on aircraft. Here, we address the coordination of flow management procedures in the presence of realistic uncertainties by developing a family of abstractions for implementable flow restrictions (e.g., miles-in-trail restrictions, ground delay programs, and slot-based policies). Using these abstractions, we are able to evaluate the impact of multiple restrictions on generic (uncertain) traffic flows and, hence, to design practical flow management strategies. We use the developed methodology to address several common design problems, including the design of multiple restrictions along a single major traffic stream and the design of multiple flows entering a congested terminal area or sector. For instance, we find that multiple restrictions along a stream can be used to split the backlog resulting from a single restriction and use this observation to develop low-congestion designs. We conclude the discussion by posing a tractable NAS-wide flow management problem using a simple algebraic model for a restriction.

This paper proposes a flexible stochastic automaton-based network partitioning algorithm that is capable of #nd-ing the optimal k-way partition with respect to a broad range of cost functions, and given various constraints, in directed and weighted graphs. Further, this iterative algorithm requires only local computation, with respect to the graph. Hence, by incorporating a distributed stopping criterion, we have been able to solve certain partitioning problems in a totally distributed manner. In this article, this influence model-based partitioning algorithm is motivated and introduced, and is shown to #nd the optimal partition for a large class of problems. Also, a conceptual discussion of why the algorithm might be expected to #nd good partitions quickly is included, and the performance of the algorithm is illustrated through examples. Applications in partitioning distributed communicating-agent networks, sensor systems, and power grids are discussed.