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This paper investigates noise attenuation problems for systems with unmodelled dynamics and unknown noise characteristics. A unique methodology is introduced that employs signal estimation in one phase, followed by control design for noise rejection. The methodology enjoys certain advantages in its simple control design process, accommodation of unmodelled dynamics, and non-conservative noise rejection performance. Under mild information on unmodelled dynamics, we first derive robust performance bounds on noise attenuation with respect to unmodelled dynamics without noise estimation errors. Then more general results are presented for systems that are subject to both stochastic signal estimation errors and unmodelled dynamics. Examples are also presented to demonstrate our findings. [ABSTRACT FROM AUTHOR]

This paper studies identification of linear systems with binary-valued observations generated via fixed thresholds. In addition to stochastic measurement noises, the systems are also subject to structural uncertainties, including deterministic unmodelled dynamics, nonlinear model mismatch, and sensor observation bias. Since binary-valued observations can supply only limited information on the signals, truncated empirical measures are introduced to extract further information for system identification. An effective identification algorithm is constructed based on the proposed empirical measures. Optimal identification errors, time complexity, optimal input design, and impact of disturbances, unmodelled dynamics, observation bias, and nonlinear model mismatch are thoroughly investigated in a stochastic information framework. Asymptotic upper and lower bounds are established on identification errors. Numerical experiments are presented to demonstrate the effectiveness of the algorithms and the main results. [ABSTRACT FROM PUBLISHER]

Although the mean–variance control was initially formulated for financial portfolio management problems in which one wants to maximize the expected return and control the risk, our motivations stem from highway vehicle platoon controls that aim to maximize highway utility while ensuring zero accident. This paper develops near-optimal mean–variance controls of switching diffusion systems. To reduce the computational complexity, with motivations from earlier work on singularly perturbed Markovian systems [Sethi and Zhang,Hierarchical Decision Making in Stochastic Manufacturing Systems, Birkhäuser, Boston, MA, 1994; Yin and Zhang,Continuous-Time Markov Chains and Applications: A Singular Pertubation Approach, Springer-Verlag, New York, 1998 and Yin et al.,Ann. Appl. Probab.10 (2000), pp. 549–572], we use a two-time-scale formulation to treat the underlying system, which is represented by the use of a small parameter. As the small parameter goes to 0, we obtain a limit problem. Using the limit problem as a guide, we construct controls for the original problem, and show that the control so constructed is nearly optimal. [ABSTRACT FROM PUBLISHER]

This paper introduces distributed adaptive algorithms for optimal step size selection in a distributed constrained optimization problem that involves stochastic target variations and noisy observations. The limit behavior of the step size sequences reflects fundamental impact that must be balanced between tracking the target changes and attenuating observation noises. Algorithms for simultaneously estimating target variation, tracking the global optimal solution, and finding the optimal step size are derived, which are shown to achieve convergence on all the sequences simultaneously to their respective optimal values. [ABSTRACT FROM AUTHOR]

SUMMARY This work focuses on tracking and system identification of systems with regime-switching parameters, which are modeled by a Markov process. It introduces a framework for persistent identification problems that encompass many typical system uncertainties, including parameter switching, stochastic observation disturbances, deterministic unmodeled dynamics, sensor observation bias, and nonlinear model mismatch. In accordance with the 'frequency' of the parameter switching process, we divide the problems into two classes. For fast-switching systems, the switching parameters are stochastic processes modeled by irreducible and aperiodic Markov chains. Because accurately tracking real-time parameters in such systems is not possible because of the uncertainty principles, the effect of parameter switching is evaluated on their average by the stationary distribution of the Markovian chain and estimated by the least squares algorithms. We derive upper and lower bounds on identification errors, which characterize how identification accuracy depends on the earlier uncertainty terms. When the system parameters switch their values infrequently in a probabilistic sense, their values can be tracked based on input/output observations. Stochastic approximation algorithms with adaptive step sizes are used for such systems. Simulation studies are carried out to demonstrate that slowly varying parameters could be tracked with reasonable accuracy.Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Facing the pressing needs of many applications in network and control systems, this paper introduces a class of nonlinear systems with random time delays and derives conditions on moment exponential stability of the underlying systems. The system model is versatile and can accommodate a wide variety of situations. The stability analysis to date in the literature is mostly delay independent. To highlight the role of random delay for stability, this paper focuses on delay-dependent stability. Dependence of stability on random time delays introduces technical difficulties beyond the existing literature. We model the random time delays by a continuous-time Markov chain involving two-time scales defined by a small parameter . leading to a two-time scale framework. The random delays change their values with a fast varying mode and a slowly evolving effect. Under broad conditions, the stability of the system is studied using a limit system in the sense of weak convergence of probability measures. Using the limit system as a bridge, this paper establishes the Razumikhin-type criteria on the moment exponential stability. These criteria show that the mean of the random time delay with respect to the stationary distribution of the fast changing part of the Markov chain plays an important role in the moment exponential stability, which presents a novel feature of our work. In particular, we show that the overall system may be stabilized by the Markov switching even when some of the underlying subsystems are unstable, which shows that the Markov chain may serve as a stabilization factor. Explicit conditions for moment exponential stability are derived when the system is linear. Examples are given to illustrate our results. [Copyright &y& Elsevier]

Abstract: This work examines almost sure stability of a pure random delay system whose delay time is modeled by a finite state continuous-time Markov chain with two-time scales. The Markov chain contains a fast-varying part and a slowly-changing part. Using the properties of the weighted occupation measure of the Markov chain, it is shown that the overall systemʼs almost-sure-asymptotic stability can be obtained by using the “averaged” delay. This feature implies that even if some longer delay times may destabilize the system individually, the system may still be stable if their impact is balanced. In other words, the Markov chain becomes a stabilizing factor. Numerical results are provided to demonstrate our results. [Copyright &y& Elsevier]

Abstract: This paper is concerned with asymptotic properties of consensus-type algorithms for networked systems whose topologies switch randomly. The regime-switching process is modeled as a discrete-time Markov chain with a finite state space. The consensus control is achieved by using stochastic approximation methods. In the setup, the regime-switching process (the Markov chain) contains a rate parameter in the transition probability matrix that characterizes how frequently the topology switches. On the other hand, the consensus control algorithm uses a stepsize that defines how fast the network states are updated. Depending on their relative values, three distinct scenarios emerge. Under suitable conditions, we show that when , a continuous-time interpolation of the iterates converges weakly to a system of randomly switching ordinary differential equations modulated by a continuous-time Markov chain. In this case a scaled sequence of tracking errors converges to a system of switching diffusion. When , the network topology is almost non-switching during consensus control transient intervals, and hence the limit dynamic system is simply an autonomous differential equation. When , the Markov chain acts as a fast varying noise, and only its averaged network matrices are relevant, resulting in a limit differential equation that is an average with respect to the stationary measure of the Markov chain. Simulation results are presented to demonstrate these findings. [Copyright &y& Elsevier]

Dynamic remapping is critical to the performance of bulk synchronous computations that have non-deterministic behaviors because of the need of barrier synchronization between phases. There are two basic issues in remapping: when and how to remap. This paper presents a formal analysis of the issue of when to remap for dynamic computations with a priori known statistical behaviors, with an objective of finding optimal remapping frequencies for a given tolerance of load imbalance. The problem is formulated as two complement sequential stochastic optimization. Since general optimization techniques tend to reveal stationary properties of the workload process, they are not readily applicable to the analysis of the effect of periodic remapping. Instead, this paper develops new analytical approaches to precisely characterize the transient statistical behaviors of the workload process on both homogeneous and heterogeneous machines. Optimal remapping frequencies are derived for various random workload change processes with known or unknown probabilistic distributions. They are shown accurate via simulations. [Copyright &y& Elsevier]

Predictable reliability of inter-vehicle communications is a basis for the paradigm shift from single-vehicle-oriented safety and efficiency control to networked vehicle control. The lack of predictable interference control in existing vehicular communication mechanisms, however, makes them incapable of ensuring predictable communication reliability. To address the gap, we propose the Cyber-Physical Scheduling (CPS) framework that leverages the PRK interference model and addresses the challenges of vehicle mobility and broadcast to PRK-based scheduling. To address vehicle mobility, CPS leverages the physical locations of vehicles to define the gPRK interference model, a geometric approximation of the PRK model, for lightweight control signaling and effective interference relation estimation, and CPS leverages cyber-physical structures of vehicle traffic flows for effective instantiation and use of the gPRK model. To support predictable broadcast, CPS controls the interactions between gPRK model adaptations of the links of the same sender to ensure predictable broadcast reliability in the presence of vehicle mobility. Through experimental analysis with high-fidelity ns-3 and SUMO simulation, we observe that CPS enables predictable reliability while achieving high throughput and low delay in communication. To the best of our knowledge, CPS is the first field-deployable method that ensures predictable interference control and thus reliability in inter-vehicle communications. [ABSTRACT FROM AUTHOR]

The implementation of Personal Carbon Trading (PCT) holds promise in facilitating a noteworthy contribution towards the attainment of emissions reduction predicated on consumption patterns and consequently motivating lifestyle modifications. As individual consumption behaviors usually lead to continuous changes in carbon emissions, it is crucial to rethink PCT from a systematic perspective. This review employed a bibliometric analysis of 1423 papers related to PCT, highlighting the key themes of carbon emissions from energy consumption, climate change, and public opinion on policies in the context of PCT. Most of the existing PCT researches focus on theoretical assumptions and public attitudes, while the quantification of carbon emissions and simulation of PCT require further investigation. Furthermore, the concept of Tan Pu Hui is seldom addressed in PCT studies and case analyses. Moreover, there are limited PCT schemes worldwide that can be directly implemented in practice, leading to a scarcity of large-scale, high-participation case studies. To address these gaps, this review proposes a framework to clarify how PCT can stimulate individual emission reductions on the consumption side, comprising two phases, from motivation to behavior and behavior to target. Future endeavors should prioritize the enhancement of the systematic study of the theoretical foundation of PCT, encompassing carbon emissions accounting and policy design, the incorporation of cutting-edge technology, and the reinforcement of integrated policy practice. This review serves as a valuable reference for future research endeavors and policymaking efforts. • Emissions accounting and simulation for Personal Carbon Trading (PCT) research require further improvements. • Lacking large-scale and high-participation PCT practice cases. • A framework consisting of two phases has been proposed to clarify PCT. [ABSTRACT FROM AUTHOR]

• A sufficient-necessary condition is given to design a delay controller for one area. • A criterion is established for simplifying delay control design in multi-area LFC. • Proposed method has better performance compared with conventional approaches. Load frequency control (LFC) has been considered as one of the most important frequency regulation mechanisms in modern power system. One of the inevitable problems involved in LFC over a wide area is communication delay. Not only can the delay deteriorate the system performance but also cause system instability. In this paper, an alternative design method is proposed to devise delay compensators for LFC in one or multiple control areas. For one-area LFC, a sufficient and necessary condition is given for designing a delay compensator. For multi-area LFC with area control errors (ACEs), it is demonstrated that each control area can have its delay controller designed as that in a one-area system if the index of coupling among the areas is below the threshold value determined by the small gain theorem. Effectiveness of the proposed method is verified by simulation studies on LFCs with communication delays in one and multiple interconnected areas with and without time-varying delays, respectively. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) plays an important role in economic operation of the microgrid (MG). However, the communication links among distributed generators (DGs) may practically be corrupted by additive noise, resulting in erroneous deviations from ED commands. To deal with such issues, this study proposes a distributed noise‐resilient ED strategy for islanded MG. Owing to consensus‐based implementation, the proposed ED strategy is implemented in a fully distributed manner, which enables peer‐to‐peer communication among DGs without the necessity of a central controller. In addition, the proposed ED strategy involves post‐iterate averaging technique to further enhance its convergence under additive communication noise. In this way, different from the existing ED strategies, the proposed ED strategy is fully distributed and resilient to the communication noise. Furthermore, the effectiveness of the proposed ED strategy is evaluated on an islanded MG, where the communication links are corrupted by different levels of additive noise. Finally, in order to fully take account of the uncertainty and stochastic nature of different operation scenarios, the Monte‐Carlo simulations are carried out, of which the simulation results demonstrate that the proposed ED strategy is superior to the existing ED strategies, in terms of the convergence property under additive communication noise. [ABSTRACT FROM AUTHOR]

This paper provides a further step towards popularizing the proposed Butler-Volmer (BV) equation-based model and its implementation on state of power (SOP) prediction at various temperatures, which is based on the relationship between state of charge and state of useful charge. The actual 10 s SOP of battery is obtained using the constant current pulse when the restriction of voltage is exactly managed. The COMPLEX method is taken to determine the coefficients of the simplified form of BV equation, enabling online estimation of battery states. Robustness analysis of the proposed model and algorithm on SOP prediction over a large temperature range is analyzed and verified, showing their reliability and accuracy in estimating the terminal voltage and predicting power capability. [ABSTRACT FROM AUTHOR]

The constant polarization voltage is managed for battery heating to achieve a good tradeoff between short heating time and less damage to battery lifetime based on an electro-thermal coupled model. The optimal frequency for maximum heat generation rate at a certain temperature is determined, which is different from the frequency for minimum total impedance. Heating under variable frequency is almost the same as under a constant frequency in terms of heating time and efficiency. However, engineering realization for variable frequency is more difficult, implying that constant frequency heating is a more promising candidate. The optimal frequency during the overall heating process, which is always lower than that at the initial temperature, can be evaluated from the intermediate temperature with low computational effort. Experimental results demonstrate that the heating time at the optimal frequency, corresponding to the maximum heat generation during the overall heating process, is the shortest with high efficiency. The battery is heated from −15.4 °C to 5.6 °C within 338 s, an average temperature-rise rate of 3.73 °C/min with an essentially uniform temperature distribution. The proposed heating strategy, which is experimentally verified with no apparent detrimental effect on battery health, is of great potential for rapidly improving operating performance of electric vehicles in cold weather. [ABSTRACT FROM AUTHOR]

A low-temperature electro-thermal coupled model, which is based on the electrochemical mechanism, is developed to accurately capture both electrical and thermal behaviors of batteries. Activation energies reveal that temperature dependence of resistances is greater than that of capacitances. The influence of frequency on polarization voltage and irreversible heat is discussed, and frequency dependence of polarization resistance and capacitance is obtained. Based on the frequency-dependent equation, a reduced low-temperature electro-thermal coupled model is proposed and experimentally validated under different temperature, frequency and amplitude conditions. Simulation results exhibit good agreement with experimental data, where the maximum relative voltage error and temperature error are below 2.65% and 1.79 °C, respectively. The reduced model is demonstrated to have almost the same accuracy as the original model and require a lower computational effort. The effectiveness and adaptability of the proposed methodology for model reduction is verified using batteries with three different cathode materials from different manufacturers. The reduced model, thanks to its high accuracy and simplicity, provides a promising candidate for development of rapid internal heating and optimal charging strategies at low temperature, and for evaluation of the state of battery health in on-board battery management system. [ABSTRACT FROM AUTHOR]

The lithium titanate battery, which uses Li 4Ti 5O 12 (LTO) as its anode instead of graphite, is a promising candidate for fast charging and power assist vehicular applications due to its attractive battery performance in rate characteristics and chemical stability. Unfortunately, commonly used battery models, including a large number of enhanced electrical models, become problematic when describing current–voltage characteristics of lithium titanate batteries. In this paper, a novel Butler–Volmer equation-based electric model is employed to outline unique phenomena induced by changing rates for high-power lithium titanate batteries. The robustness of the proposed model for three types of lithium titanate batteries under varying loading conditions, including galvanostatic test and Federal Urban Dynamic Schedule test, is evaluated and compared against experimental data. The experimental results of three types of lithium titanate batteries with common anode materials but differentiated cathode materials show good agreement with the model estimation results with maximum voltage errors below 2%. [ABSTRACT FROM PUBLISHER]

This paper establishes ergodic properties for Markovian semigroups generated by segment processes associated with several classes of retarded stochastic differential equations (SDEs) with constant/variable/distributed time lags. It derives exponential ergodicity for (a) retarded SDEs by the Arzelà–Ascoli tightness characterization of the spaceequipped with the uniform topology, (b) neutral SDEs with continuous sample paths by a generalized Razumikhin-type argument and a stability-in-distribution approach, and (c) retarded SDEs driven by jump processes using the Kurtz criterion of tightness for the spaceendowed with the Skorohod topology. [ABSTRACT FROM AUTHOR]

In most voltage stability indices, generators are normally modeled as either PV buses or ideal voltage sources. These assumptions cannot cover the entire process of systems losing voltage stability, especially when PV generators have converted to PQ nodes in case of reactive power shortage. In this paper, an alternative generator equivalent model (GEM) is proposed for voltage stability analysis. Based on their dynamic models and real-time measurements, generators are modeled by time-varying internal voltages and impedances to capture the dynamic behaviors of generators. Furthermore, the traditional L-index is extended to incorporate GEMs for system voltage stability analysis. Simulation studies have been carried out to verify the effectiveness of the proposed new index and to show the impacts of GEM on voltage stability analysis. The simulation results of four test systems show that, in certain scenarios, incorporating GEM is necessary and can significantly improve the accuracy of voltage stability analysis. [ABSTRACT FROM PUBLISHER]

Human noroviruses (NoVs) are an important cause of epidemic acute gastroenteritis. Their role in sporadic cases, however, is less clear. In this study, we performed a two-year surveillance (September 2005 to August 2007) of NoV gastroenteritis in outpatient clinics in a southern city of China, Jiangmen City. NoVs were detected in 115 patients (115/881, 13.1%) with 30 (26.1%) co-infections with rotaviruses. Sequence analysis showed that all 115 NoVs belonged to genogroup II, with GII.4 being the most predominant (87.8%). NoV-associated infection can be seen year-around, with autumn and winter peaks. This study provides basic information on sporadic cases of major NoV gastroenteritis in children in different seasons, which is valuable for future disease control and prevention. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Abstract: This paper is concerned with persistent system identification for plants that are equipped with binary sensors whose unknown parameter is a random process represented by a Markov chain. We treat two classes of problems. In the first class, the parameter is a stochastic process modeled by an irreducible and aperiodic Markov chain with transition rates sufficiently faster than adaptation rates of identification algorithms. In this case, an averaged behavior of the parameter process can be derived from the stationary measure of the Markov chain and can be estimated with empirical measures. Upper and lower error bounds are established that explicitly show impact of unmodeled dynamics. In the second class of problems, the state switches values infrequently. A moving-window maximum a posterior (MAP) algorithm is introduced for tracking the time-varying parameters. Numerical results are presented to illustrate the tracking performance of the MAP algorithm and compare it with the widely used Viterbi algorithm. [Copyright &y& Elsevier]