Time-varying Loss Research Articles

Adaptive data-driven fault-tolerant control for nonlinear systems: Koopman-based virtual actuator approach

This paper proposes an adaptive data-driven fault-tolerant control scheme using the Koopman operator for unknown dynamics subjected to nonlinearities, time-varying loss of effectiveness, and additive actuator faults. The main objective of this method is to design a virtual actuator to hide actuator faults from the view of the system’s nominal controller without having any prior knowledge about the system’s underlying dynamics. The designed virtual actuator is placed between the faulty plant and the nominal controller of the system to keep the dynamical system’s performance consistent before and after the occurrence of actuator faults. Based on the Koopman operator theory, an equivalent Koopman predictor is first obtained using the process data only, without knowing the governing equations of the underlying dynamics. Koopman operator is an infinite-dimensional, linear operator which takes the nonlinear process data into an infinite-dimensional feature space where the dynamic data correlations have linear behavior. Next, based on the approximated system’s Koopman operator, a virtual actuator is designed and implemented without knowing the system’s nominal controller. Needless to use a separate fault detection, isolation, and identification module to perform fault-tolerant control, the current method leverages the adaptive framework to keep the system’s desired performance in facing time-varying additive and loss of effectiveness actuator faults. Finally, the approach’s efficacy is demonstrated using simulation on a two-link manipulator benchmark, and a comparison study is presented.

Loss modulation assisted solitonic pulse excitation in Kerr resonators with normal group velocity dispersion.

We demonstrate an emergent solitonic pulse generation approach exploiting the externally introduced or intrinsic loss fluctuation effects. Single or multiple pulses are accessible via self-evolution of the system in the red, blue detuning regime or even on resonance with loss perturbation. The potential well caused by the loss profile not only traps the generated pulses, but also helps to suppress the drift regarding high-order dispersion. Breathing dynamics is also observed with high driving force, which can be transferred to stable state by backward tuning the pump detuning. We further investigate the intrinsic free carrier absorption, recognized as unfavored effect traditionally, could be an effective factor for pulse excitation through the time-variant loss fluctuation in normal dispersion microresonators. Pulse excitation dynamics associated with physical parameters are also discussed. These findings could establish a feasible path for stable localized structures and Kerr microcombs generation in potential platforms.

Fault-Tolerant Control of One-Sided Lipschitz Nonlinear Systems

In this short paper, development of an adaptive fault tolerant control (FTC) using a virtual actuator framework is presented for one-sided Lipschitz nonlinear systems subjected to time-varying loss of effectiveness and additive actuator faults. The proposed controller does not require to have a separate unit to detect, isolate, and identify a fault, and it is completely independent from the nominal controller. Due to this important property, the virtual actuator can easily added between the faulty plant and the nominal controller and it can guarantee that the deviation of states from the nominal behavior of the system in the presence of actuator faults can be made arbitrary small. Simulation results are provided to show the efficacy of the proposed adaptive fault tolerant controller.

Time-Varying Loss Aversion of Mutual Fund Managers as a Response to Incentives

Time-Varying Loss Aversion of Mutual Fund Managers as a Response to Incentives

Investors’ attention and information losses under market stress

The paper proposes a novel point-wise entropy approach to measure the time-varying losses in the value of information that investors associate with market signals, financial and economic indicators, and news. We cast our approach in a Bayesian framework and assume that market agents update their beliefs to incoming signals based on a prior information set. By exploiting the distribution rather than the time-series properties of information signals, our method is able to construct univariate signal-specific, but also composite proxies of information loss, with the latter being more efficient in reducing misleading effects and interpretation errors. As an empirical illustration, we construct information loss proxies for the US equity market from several mainstream information signals and find that the majority of information loss indicators can influence investors’ attention, which then intermediates the impact of information signals on market outcomes. Finally, we show that, by relying on composites rather than univariate proxies, market agents can diversify and thus reduce their information losses when interpreting signals associated with the same underlying event.

Decentralized online convex optimization based on signs of relative states

In this paper, we study a class of decentralized online convex optimization problems with time-varying loss functions over multi-agent networks. We propose a decentralized online subgradient method by using only the signs of the relative states of neighbors, which considerably reduces the sensing and communication requirements for the agents. We show that, despite the loss of information, the proposed algorithm can still achieve O(T) regret bound (T is the time horizon), which matches that of the standard distributed online subgradient method with exact relative state information. We further investigate the scenario of using noisy signs, where the measurements of the directions of the relative states are perturbed by noise. We show that the regret bound is not affected as long as the noise is not too large, which manifests certain noise-tolerance property of the proposed algorithm. Additionally, we extend the algorithm to the case of bandit feedback, where only the values of the local loss functions at two points are revealed to agents at each time. We demonstrate that the regret bound is not influenced by bandit feedback in order sense. Finally, numerical experiments on decentralized online least squares and logistic regression are conducted to corroborate the efficacy of the proposed algorithms.

Fault-tolerant control of nonlinear heterogeneous multi-agent systems

In this paper, time-varying loss of effectiveness and time-varying additive actuator faults in nonlinear heterogeneous multi-agent systems (MAS) are considered and an adaptive fault tolerant-control (FTC) scheme based on the virtual actuator framework is proposed. The proposed FTC scheme does not need any separate fault detection, isolation, and identification module. Furthermore, the design of the proposed FTC is independent of the mission of MAS. It is shown that before and after the occurrence of actuator faults, the difference between states of each agent can be arbitrarily reduced and the ultimate bounds of the state error are also calculated. A network of manipulators is considered as a simulation example to illustrate the effectiveness of the proposed FTC methodology.

Fuzzy multi-period portfolio selection model with time-varying loss aversion

This paper deals with multi-period portfolio selection problem in a fuzzy environment, in which the effects of investors’ time-varying asymmetric attitudes to losses and gains on portfolio selection are taken into consideration. In the paper, a definition of time-varying loss aversion is presented to describe the aforementioned time-varying asymmetric attitudes to losses and gains. A fuzzy multi-period portfolio selection model based on this new definition of time-varying loss aversion is formulated. Then, an improved co-evolutionary particle swarm optimization (ICPSO) algorithm is designed to solve the proposed model. The comparison analysis between the proposed model and the fixed-parameter loss aversion model is provided to demonstrate the fact that the proposed model can significantly affect the performance of portfolio selection. Finally, a numerical example is given to illustrate the application of the proposed model and the efficiency of the designed algorithm.

Dynamic surface control for discrete-time strict-feedback systems with network-induced time-varying delays and packets loss

In this paper, a dynamic surface control (DSC) strategy for discrete-time strict-feedback multiple-input-multiple-output systems is proposed. State variables are estimated by Luenberger observer using latest outputs received in controller. The control signals, bearing the same time-stamps as the target system, are constructed based on DSC methodologies. The control mechanism also considers the network-induced time-varying delays, packet drops and disorder; these parameters are systematically stacked into the network and transmitted into an actuator where the control function is performed. In order to establish all the design parameters, a simplified methodology of utilising linear matrix inequality and homotopy algorithm is proposed. The simulation results prove that the proposed method is effective.

Neural network and GBSM based time-varying and stochastic channel modeling for 5G millimeter wave communications

In this work, a frame work for time-varying channel modeling and simulation is proposed by using neural network (NN) to overcome the shortcomings in geometry based stochastic model (GBSM) and simulation approach. Two NN models are developed for modeling of path loss together with shadow fading (SF) and joint small scale channel parameters. The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell. The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment. Moreover, the sparse feature of clusters, delay and angular spread, channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.

Bandit Convex Optimization for Scalable and Dynamic IoT Management

The present paper deals with online convex optimization involving both time-varying loss functions, and time-varying constraints. The loss functions are not fully accessible to the learner, and instead only the function values (a.k.a. bandit feedback) are revealed at queried points. The constraints are revealed after making decisions, and can be instantaneously violated, yet they must be satisfied in the long term. This setting fits nicely the emerging online network tasks such as fog computing in the Internet-of-Things (IoT), where online decisions must flexibly adapt to the changing user preferences (loss functions), and the temporally unpredictable availability of resources (constraints). Tailored for such human-in-the-loop systems where the loss functions are hard to model, a family of bandit online saddle-point (BanSaP) schemes are developed, which adaptively adjust the online operations based on (possibly multiple) bandit feedback of the loss functions, and the changing environment. Performance here is assessed by: i) dynamic regret that generalizes the widely used static regret; and, ii) fit that captures the accumulated amount of constraint violations. Specifically, BanSaP is proved to simultaneously yield sub-linear dynamic regret and fit, provided that the best dynamic solutions vary slowly over time. Numerical tests in fog computation offloading tasks corroborate that our proposed BanSaP approach offers competitive performance relative to existing approaches that are based on gradient feedback.

Distributed Control Strategy of Microgrid Based on the Concept of Cyber Physical System

For distributed energy resources (DERs) in islanded microgrid (MG), to improve the control effect on the output voltages and frequency from droop control, a novel distributed control strategy is proposed in this article based on the concept of cyber physical system (CPS). The novel control structure is consisted by the cyber and the physical layers. In the cyber layer: firstly, the communication structures of overall microgrid and single DER are constructed; then, based on the communication structures, the influence of time-delay (delay) and packet loss (loss) on consensus control is analyzed in this article; And then, an event-triggered mechanism combined with adapt virtual leader-following consensus control (AVLFCC) and predictive compensation is proposed in this layer. It is used to solve the problem of time-varying delay and loss rate in the process of consensus control. In the physical layer: firstly, the droop control is used as the primary control on the output voltages and frequency of DERs; then, a novel secondary control method is proposed by using the event-triggered mechanism. And finally, the simulation results confirm the effectiveness of the novel distributed control strategy under communication problems (delay and loss) and during plug-and-play operation.

Heterogeneous Online Learning for “Thing-Adaptive” Fog Computing in IoT

Internet of Things (IoT) is featured with its seamless connectivity of billions of smart devices, which offer different functionalities and serve various personalized tasks. To meet the task-specific requirements such as latency and privacy, the fog computing emerges to extend cloud computing services to the edge of the Internet backbone. This paper deals with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online fog computing</i> emerging in IoT, where the goal is to balance computation and communication at fog networks on-the-fly to minimize service latency. Due to heterogeneous devices and human participation in IoT, the online decisions here need to flexibly adapt to the temporally unpredictable user demands and availability of fog resources. By generalizing the classic online convex optimization (OCO) framework, the low-latency fog computing task is first formulated as an OCO problem involving both time-varying loss functions and time-varying constraints. These constraints are revealed after making decisions, and allow instantaneous violations yet they must be satisfied in the long term. Tailored for heterogeneous tasks in IoT, a “thing-adaptive” online saddle-point (TAOSP) scheme is developed, which automatically adjusts the stepsize to offer desirable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">task-specific</i> learning rates. It is established that without prior knowledge of the time-varying parameters, TAOSP simultaneously yields near-optimality and feasibility, provided that the best dynamic solutions vary slowly over time. Numerical tests corroborate that our novel approach outperforms the state-of-the-art in minimizing network latency.

TCP Network Coding with Adapting Parameters for Bursty and Time-Varying Loss

TCP Network Coding with Adapting Parameters for Bursty and Time-Varying Loss

Fault-tolerant control considering time-varying bounds on faults

This paper presents a novel fault-tolerant control strategy to compensate the time-varying loss of actuators’ effectiveness. It considers intermediate situations where the fault is not determined precisely (unlike active approaches) but overall estimations about its rate and final value are available through the previous experiences and/or experiments. Based on the estimations, two upper and lower time-varying bounds on the actuators’ effectiveness are established to be exploited in the procedure of controller design. In a special case, where these bounds are constant, the method will be reduced to the conventional passive approach. Also, actuator saturation and the effects of [Formula: see text] disturbances are considered in the research. To tackle the conflict between attenuating the effect of L∞ disturbances and enlarging the domain of attraction, a linear matrix inequality optimization framework is suggested. The proposed method is implemented on a model of an unmanned aerial vehicle and the results are discussed.

Adaptive fault-tolerant shape control for nonlinear Lipschitz stochastic distribution systems

This paper addresses the problem of adaptive fault estimation and fault-tolerant control for a class of nonlinear non-Gaussian stochastic systems subject to time-varying loss of control effectiveness faults. In this work, time-varying faults, Lipschitz nonlinear property and general stochastic characteristics are taken into consideration in a unified framework. Instead of using the system output signal, the output distribution is adopted for shape control. Both the states and faults are simultaneously estimated by an adaptive observer. Then, a fault tolerant shape controller is designed to compensate for the faults and realize stochastic output distribution tracking. Both the fault estimation and the fault tolerant control schemes are designed based on linear matrix inequality (LMI) technique. Satisfactory performance has been obtained for a numerical simulation example. Furthermore the proposed scheme is successfully tested in a case study of particle size distribution control for an emulsion polymerization reactor.

Durability of Bridge Structural Elements

Due to environmental actions evocated by the atmospheric behaviour, the material degradation processes are started. The effects of material deterioration due to corrosion cause the loss of cross-sectional or member resistance, but the limit state method does not directly reflect design and verification allowing for failures caused by this influence. The time-variant loss of steel and reinforced concrete moment cross-sectional resistances were computed respecting specific corrosion models to express the time-dependent approximation function taking into account effects of corrosion degradation. The paper presents the general principles of structural elements design for durability based on the limit state concept and their application in the case of moment cross-sectional resistance respecting the effects of corrosion degradation.

Time-varying losses in material flows of steel using dynamic material flow models

A method for annual evaluation of recycling rates in material flows was established to enable a consistent analysis of resource utilizations. The algorithm to calculate the time-varying losses was derived based on a sound statistical approach that would be viable for both historical data and future predictions. This method eliminates the need for adjustable parameters and is solely based on input data of the material consumption and scrap collection. This article describes the model methodology and the calculation procedures to classify the societal scrap reserve from the amounts of losses, based on statistics. These statistical models contribute to establish a standardized method to obtain consistent results. Based on the method the lifetime of steel data was for the first time calculated on an annual basis for the steel usage as well as for the end of life scrap amount. This was done for the Swedish steel consumption and the global steel consumption between 1900 and 2013 as well as for future predictions between 2013 and 2060. The lifetime of steel was calculated to be higher in an industrialized country such as Sweden compared to the global average value. More specifically, the service lifetimes of EOL steel in Sweden and in the World were calculated to be 35 and 28 years in 2012, respectively. This novel approach of using system specific data on the lifetime of steel on an annual basis enables a possibility to evaluate recycling trends and potentials to increase the recycling rate.

Probabilistic Time-Dependent Multihazard Life-Cycle Assessment and Resilience of Bridges Considering Climate Change

AbstractClimate change and an increase in the number of hazards and/or their intensities may increase the probability of failure associated with civil infrastructure systems. Understanding how natural hazards affect the life-cycle performance of highway bridges can lead to improved preparedness prior to extreme disasters and can ultimately benefit society. In this paper, a framework for time-variant loss and resilience assessment of highway bridges under time-dependent multiple hazards is presented. The effects of earthquakes and floods on bridges are both investigated. The life-cycle hazard losses with and without aging effects and climate change are computed. Additionally, the probabilistic changes in the hazard intensity and frequency resulting from climate change on the total life-cycle hazard loss are also investigated. The proposed framework is applied to a highway bridge located in California.

Mean Square Stability Based on H∞ Control for Uncertain Networked Control Systems with Random Data Loss

This paper studies the design of uncertain linear discrete-time Networked Control Systems over imperfect communication channels with time-varying data loss. This leakage is modeled as stochastic parameter and norm-bounded uncertainties. In addition, this paper defines the necessary conditions for the design of the dynamic output feedback controller; subsequently, an iterative LMI approach is specified to compute the controller state parameters. The simulation results show that the designed controller improves the H∞ disturbance attenuation performance of the concerned uncertain system.