This paper develops control design algorithms to achieve almost sure stabilization for Markovian randomly switched linear systems (RSLSs) involving uncontrollable subsystems. Under the conditions of irreducible and aperiodic Markovian switching processes, a controller design method is introduced that utilizes the stationary distribution of the Markov Chain (MC) and stabilizes the overall system almost surely. This proposed method addresses the intricacies arising from uncontrollable subsystems within the context of Markovian RSLSs, and offers a constructive solution for achieving system stability by coordinating subsystem controllers. Almost sure stability of the closed-loop system is established. In addition, a simulation case study on an IEEE 5-Bus system illustrates model development, controller design procedures, and convergence properties.

This paper studies the impact of stochastic load variations on distributed optimal load tracking and allocation (OLTA) problems in cyber-physical DC microgrids (MGs) for transportation electrification. Without load variations, the distributed optimization strategies developed in our earlier work can achieve convergence to global optimal solutions in a multi-objective optimization that balances fair load allocation and power loss reduction. Under persistent stochastic load variations, this paper develops distributed optimal strategies to track time-varying loads under noisy observations and establishes their convergence properties and error bounds. The limiting behavior of the errors characterizes the fundamental impact of the step size on irreducible errors due to conflict between attenuating observation noises and tracking load changes. Optimality conditions and algorithms for selecting the optimal step size are introduced to guide step size selection in practical applications. Simulation studies on real-world systems demonstrate the effectiveness of the proposed algorithms and validate the theoretical results.

This article considers a general model of distributed convex optimization with possibly local constraints, coupled equality constraints, and coupled inequality constraints, where the coupled equality constraints are affine and the coupled inequality constraints can be nonaffine. To solve this problem, we present two algorithms. The first algorithm is similar to a dual subgradient algorithm that requires a center node in the network. The main advantage of the first algorithm is that it achieves the optimal convergence rate O([1/√k]) . Moreover, it does not require additional treatment for the primal recovery. These merits are achieved by using an iterate-averaging feedback technique on the basis of the dual subgradient method. The second algorithm further removes the requirement of a center node by employing consensus tracking iterates. As a result, the second algorithm is fully distributed at the price of achieving an O([lnk/√k]) convergence rate.

The mission-critical nature of wireless networked sensing and control (WSC) systems, such as the control of industrial plants, requires stringent real-time delivery of packets. Due to inherent dynamics and uncertainties in wireless communication, real-time communication guarantees are probabilistic in nature. In this paper, a probabilistic framework is therefore proposed for per-packet real-time delivery guarantee. The notion of real-time in this paper differs from the existing work in the sense that it ensures, in an execution history of arbitrary length, every packet is successfully delivered before its deadline with a probability no less than a user-specified threshold (e.g., 99%). The framework has several novel building blocks: First, “R3 (requirement-reliability-resource) mapping” translates the upper layer probabilistic real-time communication requirement, and the lower layer links reliability into the resource (i.e., optimal number of transmission opportunities) reserved for each packet. Second, “EDF (earliest deadline first) based real-time scheduling” as well as the “admission test” and “traffic load optimization” maximize system utility while satisfying per-packet real-time communication requirements. The proposed admission test is proved to be both sufficient and necessary, and the simulation results show that the proposed framework ensures probabilistic per-packet real-time communication.

One of the fundamental and challenging issues in microgrids is to guarantee fairness of load sharing while realizing voltage regulation of distributed generations. In order to address this issue, a new multiobjective optimization problem with tunable weighting coefficients is first formulated for dc microgrids. Second, a new distributed control scheme, which only requires local communications among neighbors, is proposed to solve the optimization problem. It is theoretically proved that the distributed control scheme can exponentially achieve the global optimal outputs of voltages and currents at distributed generations. Compared with a centralized control scheme, the proposed distributed control scheme provides remarkable advantages in improving reliability and scalability of microgrids. Third, the distributed control scheme is extended to accommodate a constant communication delay. The effects of the communication delay on the stability of microgrids are explicitly characterized. Finally, the performance of the proposed control schemes is evaluated by a modified six-bus microgrid with dc-powered trolleybus systems in terms of their convergence, robustness to load variations, plug-and-play functionality, tradeoff ability, and effects of communication delays.

The platooning of automated vehicles has the potential to significantly benefit road traffic. This paper presents a distributed$\text{H}_{\mathrm {\infty }}$control method for multi-vehicle systems with identical dynamic controllers and rigid formation geometry. After compensating for the powertrain nonlinearity, the node dynamics in a platoon is mathematically described by a multiplicative uncertainty model. The platoon control system is then decomposed into an uncertain part and a diagonal nominal system through linear transformation and eigenvalue decomposition of the information-exchange-topology matrix. Robust stability, string stability, and distance tracking performance of the designed platoons are analyzed theoretically under the decoupled$\text{H}_{\mathrm {\infty }}$framework. A comparative simulation with non-robust controllers is used to demonstrate the effectiveness of this method.

This paper introduces new models to describe pedestrian crowd dynamics in a typical unidirectional environment, such as corridors, pathways, and railway platforms. Pedestrian movements are represented in a two-dimensional space that is further divided into narrow virtual lanes. Consequently, pedestrians either move in a lane following each other or change lanes, when it is desirable. Within this framework, the motions of pedestrians are modeled as a two-dimensional and two-time-scale hybrid system. A pedestrian's movement along the crowd direction is labeled as the x direction and modeled by a real-valued process, a solution of a differential equation in continuous time, the lane change is labeled as the y direction. In contrast to the x direction dynamics, the movements in the y direction only happen at some time epoch. Although the movements are still on the same time horizon as the x direction movements, with a slight abuse of notation and for simplicity and convenience, we use discrete time as the time indicator, and model the movements by a recursive equation taking values in a finite set. Under common assumptions of crowd movements, we prove that the crowd movements in the x direction will converge to a uniform distance distribution and the convergence rate is exponential. Furthermore, by using a velocity-distance function to represent the common crowd and traffic congestion scenarios, we show that all pedestrians will asymptotically move with a uniform group speed. In the y direction, when pedestrians naturally wish to change to faster lanes, we show that the numbers in each virtual lanes converge to a balanced distribution and hence achieves asymptotic consensus as shown typically in a crowd behavior. Stability and convergence analysis is carried out rigorously by using properties of circular matrices, stability of networked systems, and stochastic approximations. Simulation studies are used to demonstrate the main properties of our modeling approach and establish its usefulness in representing pedestrian dynamics.

This paper investigates optimal power management in dc microgrids, with its applications to dc dual-source trolleybus systems. High mobility of buses and their impact on power supply networks introduce challenging power management issues. This paper incorporates line power losses in power management strategies and introduces a new distributed optimal power management methodology. A multi-objective optimization model is developed. Using only neighborhood information exchange among feeder lines in the network, the new consensus-type control accommodates both feeder current allocation and power loss reduction with fast convergence. One critical finding of this paper is that our local recursive optimization algorithms achieve the global optimal solution asymptotically. Under random noise on information exchange, convergence and optimality of the proposed method are established rigorously. The power system configurations of the Beijing dual-source trolleybus system are used for simulation case studies on the new power management methods. Feasibility, accuracy, and comparison with global optimization results are demonstrated.

Sentiment lexicon is an important tool for identifying the sentiment polarity of words and texts.How to automatically construct sentiment lexicons has become a research topic in the field of sentiment analysis and opinion mining.Recently there were some attempts to employ representation learning algorithms to construct a sentiment lexicon with sentiment-aware word embedding.However, these methods were normally trained under documentlevel sentiment supervision.In this paper, we develop a neural architecture to train a sentiment-aware word embedding by integrating the sentiment supervision at both document and word levels, to enhance the quality of word embedding as well as the sentiment lexicon.Experiments on the SemEval 2013-2016 datasets indicate that the sentiment lexicon generated by our approach achieves the state-of-the-art performance in both supervised and unsupervised sentiment classification, in comparison with several strong sentiment lexicon construction methods.

Predictable link reliability is required for wireless networked control, yet co-channel interference remains a major source of uncertainty in wireless link reliability. Formulated specifically for distributed predictable control of co-channel interference, the physical-ratio-K (PRK) interference model integrates the protocol model's locality and the physical model's high fidelity while addressing their weaknesses, and it transforms interference control in arbitrary networks to a problem involving coordination between close-by nodes only. To apply the PRK model in real-world settings, we design protocol PRKS that addresses the challenges of model instantiation and protocol signaling in PRK-based scheduling. In particular, PRKS uses a control-theoretic approach to instantiate the PRK model in dynamic uncertain networks, uses local signal maps to address the challenges of large interference range and anisotropic asymmetric wireless communication, and leverages the different timescales of PRK model adaptation and data transmission to decouple protocol signaling from data transmission. Through testbed-based measurement study, we show that, unlike existing scheduling protocols where link reliability is unpredictable and the ratio of links whose reliability meets application requirements can be as low as 0%, PRKS enables predictably high link reliability (e.g., 95%) for all the links in different network and environmental conditions without a priori knowledge of these conditions. Through local distributed coordination, PRKS also achieves a channel spatial reuse very close to what is enabled by the state-of-the-art centralized scheduler while ensuring the required link reliability. By ensuring the required link reliability in scheduling, PRKS also enables a lower communication delay and a higher network throughput than existing scheduling protocols.

Dual-source trolleybuses powered by onboard battery and grid electricity offer unique advantages in fuel economy, cost reduction, and passenger capacity, which are particularly appealing for public transportation in populated cities. Their mobility and power supply network configurations introduce challenging power management issues on their dedicated supply power grids. To ensure safe, reliable, and efficient operation of trolleybuses, their high-current and dynamic loads must be distributed to the feeders properly and promptly. Based on certain emerging networks of feeders and supply stations for city trolleybus systems, this paper introduces a new framework for current flow balancing based on recently developed weighted-and-constrained consensus control methods to manage the power supply network. Using only neighborhood information exchange among feeder lines in the network, the consensus control can achieve global current balancing with fast convergence to a balanced state, robustness to load perturbations, reconfiguration with feeder addition and deletion, and rebalancing under feeder capacity variation. The methodology is scalable in the sense that system expansion will not substantially increase the control system complexity. The power system configurations of the Beijing dual-source trolleybus system are used for simulation case studies on the new power management methods. Robustness and scalability are demonstrated, together with discussions on the feasibility, flexibility, and implementation issues of the methodology.

Predictable link reliability is required for wireless networked control, yet co-channel interference remains a major source of uncertainty in wireless link reliability. Integrating the protocol model's locality and the physical model's high fidelity, the physical-ratio-K (PRK) interference model has the potential to enable distributed, predictable control of co-channel interference and thus predictable link reliability. To realize the potential of the PRK model, we design protocol PRKS that addresses the challenge of instantiating the PRK model in the presence of network and environmental uncertainties. Formulating the PRK-model-instantiation problem as a minimum-variance regulation control problem, in particular, PRKS uses a control-theoretic approach to instantiating the PRK model on the fly. Through testbed-based measurement study, we show that, unlike existing scheduling protocols where link reliability is unpredictable and the ratio of links whose reliability meets application requirements can be as low as 0%, PRKS enables predictably high link reliability (e.g., 95%) for all the links in different network and environmental conditions without a priori knowledge of these conditions. Through local, distributed coordination, PRKS also achieves a channel spatial reuse very close to what is enabled by the state-of-the-art centralized scheduler while ensuring the required link reliability. By ensuring the required link reliability in scheduling, PRKS also enables a lower communication delay and a higher network throughput than existing scheduling protocols.

Packet loss in block erasure channels creates a randomly switching networked system that impacts control performance significantly. This paper employs safety of highway vehicle platoons as a platform to study such an impact. By autonomous intervehicle coordination, a platoon can potentially enhance safety, improve highway utility, increase fuel economy, and reduce emission. By comparing different information structures that utilize radar distance sensors and wireless communication channels, we characterize some intrinsic relationships between communication resources and control performance. The findings of this paper provide useful guidelines in communication resource allocations and vehicle coordination in vehicle safety problems.

Packet loss in block erasure channels creates a randomly switching networked system that impacts control performance significantly. This paper employs safety of highway vehicle platoons as a platform to study such impact. By autonomous inter-vehicle coordination, a platoon can potentially enhance safety, improve highway utility, increase fuel economy, and reduce emission. By comparing different information structures which utilize radar distance sensors and wireless communication channels, we are able to characterize some intrinsic relationships between communication resources and control performance. The findings of this paper provide useful guidelines on communication resource allocations and vehicle coordinations in vehicle safety problems.

We address the challenge of designing predictable real-time systems in an unpredictable thermal environment where environmental temperature may dynamically change (e.g., implantable medical devices). Towards this challenge, we propose a control-theoretic design methodology that permits a system designer to specify a set of hard real-time performance modes under which the system may operate. The system automatically adjusts the real-time performance mode based on the external thermal stress. We show (via analysis, simulations, and a hardware testbed implementation) that our control design framework is stable and control performance is equivalent to previous real-time thermal approaches, even under dynamic temperature changes. A crucial and novel advantage of our framework over previous real-time control is the ability to guarantee hard deadlines even under transitions between modes. Furthermore, our system design permits the calculation of a new metric called thermal resiliency that characterizes the maximum external thermal stress that any hard real-time performance mode can withstand. Thus, our design framework and analysis may be classified as a thermal stress analysis for real-time systems.

Multicore processor based system designs are increasingly utilized as the processing platform for complex hard-real-time and embedded applications. These real-time systems need to operate under various physical and design constraints. Much research has focused on thermal-aware real-time systems designs. However, no results exist to investigate the resource allocation and the system degradation under external thermal constraints in a predictable manner. This paper proposes a control-theoretic framework to ensure hard-real-time deadlines on a multiprocessor platform in a dynamic thermal environment. We use real-time performance modes to permit the system to adapt to changing conditions. Also, we show how the system designer can use our framework to allocate asymmetric processing resources upon a multicore CPU and still maintain thermal constraints. We develop analysis for determining what modes the system can support for a given external thermal condition. Our system design extends the derivation of thermal-resiliency (originally proposed for uniprocessor systems) to multicore systems and determines the limitations of external thermal stress that any hard-real-time performance mode can withstand. Simulations and physical test bed results show that our algorithm predicts how a system will gracefully and predictably degrade under external thermal stress.

We address the challenge of designing predictable real-time systems in an unpredictable thermal environment where environmental temperature may dynamically change (e.g., implantable medical devices). Towards this challenge, we propose a control-theoretic design methodology which permits a system designer to specify a set of hard-real-time performance modes under which the system may operate. The system automatically adjusts the real-time performance mode based on the external thermal stress. We show (via analysis, simulations, and a hardware testbed implementation) that our control-design framework is stable and control performance is equivalent to previous real-time thermal approaches, even under dynamic temperature changes. A crucial and novel advantage of our framework over previous real-time control is the ability to guarantee hard deadlines even under transitions between modes. Furthermore, our system design permits the calculation of a new metric called thermal resiliency which characterizes the maximum external thermal stress that any hard-real-time performance mode can withstand. Thus, our design framework and analysis may be classified as a thermal stress analysis for real-time systems.

Internet traffic often exhibits a structure with rich high-order statistical properties like self-similarity and long-range dependency (LRD). This greatly complicates the problem of server performance modeling and optimization. Existing tools like queuing models in most cases only hold in mean value analysis under the assumption of simplified traffic structures. In this paper, we present a filter model to characterize the relationship among the factors of server capacity, request scheduling, and service quality for general input traffic. By the model, a server scheduler operates as an finite-duration impulse response (FIR) filter that transforms request processes into workload processes with the objective of minimizing load variation or overload probability, and meanwhile, without violating request response deadlines as defined in service-level agreements. We present a design and analysis of the filter for traffic with strong LRD in the frequency domain. Most Internet traffic has monotonically decreasing strength of variation functions over frequency. For this type of input traffic, we prove that optimal schedulers must have a convex structure. Uniform resource allocation is an extreme case of the convexity and is proved to be optimal for Poisson traffic. We integrate the convex structural principle with the Generalized Processor Sharing (GPS) discipline and show that the enhanced GPS policy improves the service quality significantly. Furthermore, we show that the presence of LRD in the input traffic results in shift of variation strength from high frequency to lower frequency bands and consequently leads to a degradation of the service quality.

A bulk synchronous computation proceeds in phases that are separated by barrier synchronization. For dynamic bulk synchronous computations that exhibit varying phase-wise computational requirements, remapping at runtime is an effective approach to ensure parallel efficiency. The paper introduces a novel remapping strategy for computations whose workload changes can be modeled as a Markov chain. The use of a Markovian model allows us to treat statistical dependence and more complex structure than the usual independent identically distributed random variable assumptions. Our models are quite general and we do not need to impose conditions on the dynamics of the underlying process other than the transition probability matrix. It is shown that optimal remapping can be formulated as a binary decision process: remap or not at a given synchronizing instant. The optimal strategy is then developed for long lasting computations by employing optimal stopping rules in a stochastic control framework. The existence of optimal controls is established. Necessary and sufficient conditions for the optimality are obtained. Furthermore, a policy iteration algorithm is devised to reduce computational complexity and enhance fast convergence to the desired optimal control.

We consider the problem of execution time prediction for non-deterministic bulk synchronous computations on multiprocessors. We formulate the evolution of the execution time of a processor as two random workload change models: additive and multiplicative. These two models reflect parallel commutations in which the workload change of a processor is independent of its workload and proportional to its workload, respectively. We take advantage of their salient features and show that approaches based a central limiting theorem in statistics are viable to approximate the execution time after a certain transient time interval. By an elegant coordination of some results from order statistics and convergence rates in the central limiting theorem, we derive explicit bounds on the execution time under some mild assumptions on distributions. The accuracy of the prediction is analyzed rigorously and verified by simulations.

For bulk synchronous computations that have nondeterministic behaviors, dynamic remapping is an effective approach to ensure parallel efficiency. There are two basic issues in remapping: when and how to remap. This paper presents a formal treatment of the first issue for dynamic computations with a priori known statistical behaviors. We have formulated the problem as two complement sequential stochastic optimization, with an objective of finding optimal remapping frequencies for a given tolerance of load imbalance on multiprogrammed distributed systems. We have developed analytical approaches to precisely characterize the transient statistical behaviors of the workload process and derived optimal remapping frequencies for various random workload change processes.