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To improve the flowrate control accuracy of fuel control system at high supply pressure and large flowrate working condition, this paper proposes a novel dual closed-loop control for fuel metering unit (FMU) to address issues related to unknown velocity, matched/unmatched disturbances, and parameter uncertainties. Firstly, for a comprehensive assessment in flowrate feedback method, a global sensitive analysis is utilized to research the effect ratio of factors affecting the discharge flowrate. Then, a novel dual closed-loop control is proposed, in which the outer loop is flowrate loop, and a novel extended-state-observer (ESO)-based robust adaptive control without velocity sensor is used as the internal displacement loop control. Specifically, ESO can effectively estimate the unknown velocity and unmatched disturbance, and the parameter estimations can be updated by the adaptive law determined only by spool displacement and reference signal. The novel robust control can address matched/unmatched disturbances and model uncertainties effectively. Through Lyapunov method, it follows that the novel ESO-based robust adaptive control can achieve asymptotic tracking performance when occurring time-invariant disturbances and bounded tracking performance when occurring time-varying disturbances. Comparative experiments verify the superiority of the proposed control, which provides a new method and glimpse for the advanced control strategy of aeroengine. [Display omitted] [ABSTRACT FROM AUTHOR]

This article proposes a liquid level control scheme for a quadruple tank process (QTP) plant. A robust control is proposed using higher order sliding mode (HOSM) control. The four tank setup has opposite tanks actuated by a single pump and unmeasured liquid outflow from upper tanks affecting the levels in the lower tanks. This renders nonlinearity in the dynamics with uncertain behavior. A novel experimentation based estimator is proposed for identification of the unknown control valve ratios of the plant. The estimated parameter is used for robust control design and state observer model. The unmeasurable states are estimated using a sliding mode observer and the required derivatives of liquid levels in the tanks are evaluated using a HOSM differentiator. The complete schema is tested in simulation and validated experimentally on a QTP setup. • Parameter and state estimation based robust control of a cross coupled QTP. • Higher order sliding mode (HOSM) control for robust performance. • Sliding mode observer based state estimation. • Proposal of a novel parameter estimator using HOSM differentiator. • Experimentation based validation on a laboratory setup. [ABSTRACT FROM AUTHOR]

Mean–variance portfolios have been criticized because of unsatisfying out-of-sample performance and the presence of extreme and unstable asset weights. The bad performance is caused by estimation errors in inputs parameters, that is the covariance matrix and the expected return vector, especially the expected return vector. This topic has attracted wide attention. In this paper, we aim to find better portfolio optimization model to reduce the undesired impact of parameter uncertainty and estimation errors of mean–variance portfolio model. Firstly, we introduce a sparse mean–variance portfolio model, and give some insight about sparsity. Secondly, we propose two sparse and robust portfolio models by using objective function regularization and robust optimization. Finally, three empirical studies are proposed with real market data. • We introduce a sparse mean–variance portfolio model. • We propose two general sparse and robust portfolio models. • Three empirical studies with real market data are proposed. [ABSTRACT FROM AUTHOR]

Abstract This paper extends the conventional semi-parametric partial linear regression model with fuzzy predictors and fuzzy response in cases where 1—outliers occur in data set, 2—multicollinearity exists in predictors. For this purpose, the classical kernel fitting method is first extended for estimating unknown fuzzy smooth function. Then, inspired by the conventional local linear smoothing method, a kernel-based weighted criterion is suggested for determining the exact coefficients on the basis of ridge methodology. Some common goodness-of-fit criteria are also used to examine the performance of the proposed method. The effectiveness of the proposed method is then illustrated through two numerical examples including a simulation study. The proposed method is also compared with some common fuzzy multiple regression models with fuzzy predictors and fuzzy responses. The numerical results clearly indicate that 1—the proposed method is not sensitive to the outliers, 2—the proposed method provides sufficiently accurate results in cases where multicollinearity happens among predictors and 3—comparing to existing fuzzy multiple regressions, the proposed model provides more efficient predictions. Highlights • A fuzzy semi-parametric regression model is proposed when multicollinearity and/or outliers occur. • The fuzzy model contains the fuzzy predictors-responses. • The ridge methodology and a novel robust method are applied to evaluate crisp coefficients. • The method is compared with several methods using various goodness-of-fit-measures. [ABSTRACT FROM AUTHOR]

Abstract Robust constrained control of linear systems with parametric uncertainty and additive disturbance is addressed. The main contribution is the introduction of a mathematically rigorous and computationally tractable framework for stabilizing model predictive control with online parameter estimation to improve performance and reduce conservatism. Requirements for closed-loop stability and provable constraint satisfaction are considered separately, resulting in the use of online set-membership system identification combined with homothetic prediction tubes for robust constraint satisfaction, and an H ∞ optimal point estimate of the unknown parameters to achieve a finite closed-loop gain from the disturbance to the state. Extensions to time-varying parameters and persistently exciting inputs to guarantee parameter convergence are presented. A numerical example illustrates the proven properties and efficacy of the approach. [ABSTRACT FROM AUTHOR]

Abstract Robot-based rehabilitation requires not only the use of a suitable robot, but also an optimal strategy to guarantee that the interaction forces with the patient fit his or her impairment level. In this work, an inclusive and seamless control framework for upper limb rehabilitation robots is presented and validated. The proposed control framework involves 1) a complete set of training modes (assistive, corrective and resistive) that can be adapted to the needs of the different states of the patient's recovery, and 2) three different advanced controllers (position, force, impedance) to track safely the force and motion references defined by the aforementioned training modes. In addition, the proposed framework allows one to tune the parameters critical to the safety of the user, such as the maximum interaction forces or the maximum speed of the robot movement. In order to validate the proposed control framework, a set of experiments have been carried out in the Universal Haptic Pantograph (UHP) upper-limb rehabilitation robot. Results show that the proposed control framework for robot-mediated therapy works properly in terms of adaptability, robustness, and safety, which are crucial factors for use with patients. [ABSTRACT FROM AUTHOR]

Abstract Path following and lateral stability are crucial issues for autonomous vehicles. Moreover, these problems increase in complexity when handling articulated heavy-duty vehicles due to their poor manoeuvrability, large sizes and mass variation. In addition, uncertainties on mass may have the potential to significantly decrease the performance of the system, even to the point of destabilising it. These parametric variations must be taken into account during the design of the controller. However, robust control techniques usually require offline adjustment of auxiliary tuning parameters, which is not practical, leading to sub-optimal operation. Hence, this paper presents an approach to path-following and lateral control for autonomous articulated heavy-duty vehicles subject to parametric uncertainties by using a robust recursive regulator. The main advantage of the proposed controller is that it does not depend on the offline adjustment of tuning parameters. Parametric uncertainties were assumed to be on the payload, and an H ∞ controller was used for performance comparison. The performance of both controllers is evaluated in a double lane-change manoeuvre. Simulation results showed that the proposed method had better performance in terms of robustness, lateral stability, driving smoothness and safety, which demonstrates that it is a very promising control technique for practical applications. Highlights • A Robust Linear Quadratic Regulator may be applied for path following problems. • Payload uncertainties can be considered in the proposed lateral control. • Smooth and robust autonomous driving was obtained for articulated heavy-duty vehicles. • In terms of robustness, the proposal has better performance than H ∞ controller. [ABSTRACT FROM AUTHOR]

Abstract This paper compares three model-based methods for detecting and isolating stuck control surface faults on a small unmanned aircraft. The first method is parity-space based and compares a sensor measurement against a model-based prediction of the same quantity. The second method is observer-based and involves robust estimation for linear parameter-varying systems. The third method is also observer-based and involves multiple model adaptive estimation. The performance of the three methods are compared using flight data. The comparison shows that the three methods have different detection performance and runtime. The selection of a particular method depends on the application requirements and implementation constraints. [ABSTRACT FROM AUTHOR]

Highlights • A dedicate network structure is presented to regress 3D hand pose from a single depth image. • We discuss the effect of the ConvNet depth on its accuracy under the hand pose regression setting. • We introduce batch normalization and a low-dimensional embedding to help hand pose estimation. • The proposed system is efficient enough with more than 500 fps on a single GPU. • Experiments results show that our method can get competitive results to state-of-the-art methods. Abstract We propose a novel approach for articulated hand pose estimation from a single depth image using a very deep convolutional network. For the first, a very deep network structure is designed to directly maps a single depth image to its corresponding 3D hand joint locations. This approach eliminates the necessity of hand-crafted intermediate features and sophisticated post-processing stages for robust and accurate hand pose estimation. We use Batch Normalization to accelerate training and prevent the objective function from getting stuck in poor local minima. We introduce a low-dimensional embedding forcing the network to learn the inherent constraints of hand joints, which helps to reduce the cost of reconstructing 3D hand poses from high-dimension feature space. We discuss the effect of the convolutional network depth on its accuracy under the hand pose regression setting. Quantitative assessments on two challenging datasets show that our proposed method gets competitive results to state-of-the-art approaches in terms of accuracy. Moreover, qualitative results also show that our proposed method is robust to some difficult hand poses. [ABSTRACT FROM AUTHOR]

Abstract Is idiosyncratic volatility priced? The existing literature finds conflicting results on the cross-sectional relation between expected returns and idiosyncratic volatility. This paper examines the relation between idiosyncratic volatility and expected returns in Chinese Stock Market. We find there is a significantly positive relation between idiosyncratic volatility and expected returns when we use Fama–French five-factor model to estimate idiosyncratic volatility. However, the positive relation disappears when we use GARCH (1,1) model to estimate idiosyncratic volatility. This result indicates that in Chinese Stock Market, the idiosyncratic volatility premium is just an apparent phenomenon, whether the idiosyncratic volatility matter or not depend on the way we estimate the idiosyncratic volatility. The result is robust after controlling for investors' lottery preference, investors' sentiment and other factors. Highlights • A new method is applied to estimate idiosyncratic volatility. • The idiosyncratic volatility is positively related to expected return. • Positive relation disappears when using alternate estimate method. • In China, the idiosyncratic volatility premium is just an apparent phenomenon. [ABSTRACT FROM AUTHOR]

Abstract This note investigates the decentralized finite-time formation control of underactuated unmanned surface vessels (USVs) in the presence of model uncertainty and environmental disturbance. In the algorithm, a novel adaptive finite-time disturbance observer (AFTDO) is incorporated into the proposed control strategy that enhances its robustness to the environmental disturbance. By fusion of neural network (NN) and minimal learning parameterization (MLP) techniques, the AFTDO is constructed without priori information about the ship model and the upper bound of disturbance. On the basis of undirected graph, the decentralized control law is developed with local information from neighboring USVs. Specially, since the AFTDO and the decentralized control law share the same set of NN, the whole algorithm is with merits of concise form and less adaptive parameters. Such design contributes to smaller computation load and facilitates the implementation of the algorithm in ocean engineering. The semi global finite-time uniformly boundedness (SGFTUB) of the closed-loop system is proved by the Lyapunov theory. Numerical simulations and comprehensive comparisons are conducted to demonstrate the remarkable performance and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the robust fault detection problem for fuzzy singularly perturbed systems with pole assignment constraints. A finite frequency approach is proposed for the simultaneous achievement of the fault detection objectives and control objectives, which combines the computation of filter gains into a unified framework. By designing the fault detection filter, the residual signal for the detection of finite frequency faults can be produced, and the transient system behaviors can be improved to a satisfactory level based on the pole assignment. In addition, the estimated upper bound of the singular perturbation parameter is used to evaluate the performance of the proposed design method. Finally, a simulation example on an inverted pendulum controlled by a motor via a gear train is provided. [ABSTRACT FROM AUTHOR]

Highlights • WFA Pocket can predict fire spread and behaviour in a mobile device. • The application includes well-known semi-empirical fire spread models. • Estimating fire behaviour on the fireline is useful for minimizing many risks. Abstract Accurately predicting fire spread and behaviour on the fireline, in the field, is highly important in order to prevent the loss of human life, improve the success of initial attack and better understand the potential fire behaviour, minimizing many risks for firefighters. We present the Wildfire Analyst™ Pocket Edition application (WFA Pocket), a mobile tool aimed to be used by the fire fighter community. It shows punctual fire characteristics and estimated progression based on user introduced input data in an intuitive 3D map interface in real-time, allowing the user to interactively change parameters and analyze how the fire behaviour changes in relation to the inputs. The mathematical models implemented are all well-known by the scientific community and reported in this article. The application has integrated GIS capabilities, can work online and offline, and can retrieve fuel, weather and canopy data from online servers for the georeferenced ignition point. We describe the background and model foundation of WFA Pocket as well as its system design and main features. We also evaluate its robustness of results and present a case study to show the potential use of this tool in the field. Limitations and assumptions in the use of the application as well as potential improvements for the future are discussed. [ABSTRACT FROM AUTHOR]

Abstract The Expectation Maximization (EM) algorithm has been widely used for parameter estimation in data-driven process identification. EM is an algorithm for maximum likelihood estimation of parameters and ensures convergence of the likelihood function. In presence of missing variables and in ill conditioned problems, EM algorithm greatly assists the design of more robust identification algorithms. Such situations frequently occur in industrial environments. Missing observations due to sensor malfunctions, multiple process operating conditions and unknown time delay information are some of the examples that can resort to the EM algorithm. In this article, a review on applications of the EM algorithm to address such issues is provided. Future applications of EM algorithm as well as some open problems are also provided. [ABSTRACT FROM AUTHOR]

Abstract Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) to high-dimensional models because very large numbers of model runs are typically required to achieve convergence and provide a robust assessment. Paradoxically, only 30 percent of GSA applications in the environmental modelling literature have investigated models with more than 20 parameters, suggesting that GSA is under-utilized on problems for which it should prove most useful. We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to high-dimensional models. We also provide a new measure of robustness that assesses GSA stability and convergence. For two models, having 50 and 111 parameters, we show that grouping-enabled GSA provides results that are highly robust to sampling variability, while converging with a much smaller number of model runs. [ABSTRACT FROM AUTHOR]

Abstract To effectively use and manage lithium-ion batteries and accurately estimate battery states such as state of charge and state of health, battery models with good robustness, accuracy and low-complexity need to be established. So the models can be embedded in microprocessors and provide accurate results in real-time. Firstly, this paper analyzes the electrochemical impedance spectrogram of lithium-ion battery, and adopts impedance elements with fractional order characteristics such as constant phase element and Warburg element to improve the second-order RC integer equivalent circuit model based on the fractional calculus theory. Secondly, a fractional-order equivalent circuit model of lithium-ion battery is established, which can accurately describe the electrochemical processes such as charge transfer reaction, double-layer effect, mass transfer and diffusion of lithium-ion battery. Thirdly, based on the mixed-swarm-based cooperative particle swarm optimization, parameter identification of the fractional-order equivalent circuit model is conducted using the federal city driving schedule experimental data in the time domain. The simulation results show that the model has higher accuracy and better robustness against different driving conditions, different SOC ranges and different temperatures than the second-order RC equivalent circuit model. The SOC estimation accuracy based on the fractional-order equivalent circuit model of lithium-ion battery is validated. Highlights • A fractional-order equivalent circuit model of lithium-ion battery is established. • A MCPSO based parameter identification is conducted. • The model accuracy and robustness are verified by experiments. • The accuracy of proposed SOC estimator is discussed. [ABSTRACT FROM AUTHOR]

In this paper, the coupling of a float with a tuned liquid multi-column damper (TLMCD), a novel structural damping device inspired by the classical tuned liquid column damper (TLCD), is modelled using Lagrangian mechanics. We detail the tuning of the design parameters for each considered variant of the TLMCD, and compare each of them against a layout of multiple TLCDs. The results show that the proposed TLMCD is superior to multiple TLCDs for this application as it is more robust against wave incidence and it creates significantly less parasitic oscillations. [ABSTRACT FROM AUTHOR]

-->Highlights • Developed extended Kalman filter and moving window estimator that are robust w.r.t. gross errors in measurements. • Use of M-estimators to achieve robustness. • Demonstrated simultaneous state and parameter/disturbance estimation in the presence of measurement gross errors. Abstract Simultaneous occurrence of gross errors (outliers/biases/drifts) in the measured signals, and drifting disturbances/parameter variations affecting the system dynamics can lead to biased state estimates, and, in turn, can lead to deterioration in the performance of model-based monitoring and control schemes. In this work, robust recursive and moving window based Bayesian state and parameter estimators are developed that are robust w.r.t. gross errors in the measurements and can simultaneously estimate non-additive unmeasured disturbance/parameter variations. Using Bayes’ rule, the update step of Kalman filter (KF) is recast as an optimization problem. The optimization is then modified by replacing the likelihood term in the objective function with cost function defined by an M-estimator. The M-estimators considered in this work are Huber's Fair function and Hampel's redescending estimator. The reformulated KF is then used as a basis for reformulating extended Kalman filter (EKF). This re-formulated EKF is then used for developing robust simultaneous state and parameter estimation schemes. In particular, a robust version of recently proposed moving window based state and parameter estimator [1] has been developed. The resulting formulation can be viewed as a hybrid approach, in which the gross errors in the measurements are dealt with in a passive manner, with an active elimination of model plant mismatch by estimating unmeasured disturbance/parameter variations simultaneously. The efficacy of the proposed robust state and parameter estimators is demonstrated by conducting simulation studies and experimental studies. Analysis of the simulation and experimental results reveal that the proposed robust recursive and moving window based state and parameter estimators significantly reduce or completely nullify the effect of gross errors on the state estimates while simultaneously estimating drifting unmeasured disturbances/parameters. The simulation study also underscores the importance of simultaneous estimation of unmeasured disturbances/parameters while achieving robustness using the M-estimators. Moreover, Hampel's redescending estimator is found to be a better choice of M-estimator than the popular Huber's Fair function, as the redescending estimator can completely nullify the effect of gross errors on the state and parameter estimates. [ABSTRACT FROM AUTHOR]

This paper develops a singularity-free adaptive tracking control scheme for a general class of multi-input and multi-output uncertain discrete-time nonlinear systems with non-canonical control gain matrices. The estimation of the control gain matrices, especially in some non-canonical forms, may be singular during parameter adaptation, which leads to the singularity problems of the adaptive control laws. This paper employs the matrix decomposition technique to solve the problem under a linearly parameterized adaptive control framework. The state and output feedback cases are addressed, respectively, to ensure closed-loop stability and asymptotic output tracking. Compared with the existing results, the features of the proposed adaptive control scheme include: (i) the proposed control laws do not involve the high-gain issue commonly encountered in robust control methods; (ii) two different filtered tracking error signals are introduced for the state and output feedback cases, respectively. These filters are crucial to avoid causality contradiction of the adaptive control laws commonly encountered in adaptive control of discrete-time systems; and (iii) a future time signal estimation-based adaptive control law is developed to ensure asymptotic output tracking for the output feedback case without requiring the high-gain observer. Finally, an illustrative example is given to verify the validity of the proposed control scheme. [ABSTRACT FROM AUTHOR]

For constrained linear systems with bounded disturbances and parametric uncertainty, we propose a robust adaptive model predictive control strategy with online parameter estimation. Constraints enforcing persistently exciting closed loop control actions are introduced for a set-membership parameter identification scheme. The algorithm requires the online solution of a convex program, satisfies constraints robustly, and ensures recursive feasibility and input-to-state stability. Almost sure convergence to the actual system parameters is demonstrated under assumptions on stabilizability, reachability, and tight disturbance bounds. [ABSTRACT FROM AUTHOR]

Accurate cell tracking and lineage construction under microscopy has played an important role in analyzing cell migration, mitosis and proliferation. In the last decade, this labor-intensive manual analysis was gradually replaced by automated cell tracking methods; however, they are often limited to cells with certain morphologies or staining. In this paper, we propose a novel hierarchical tracking framework (Hift), which does not have these limitations. To keep the robustness and feasibility with different cell densities, we concluded several cell motion events into different tracking stages, including entry, exit, division, merge, fast motion, etc. And the fusion of global and local information is applied in both detection and tracking modules, to ensure the flexibility and expansibility of cell detection. To get a full-time cell lineage, we first introduce a conservative distance limit to obtain tracklets with high reliability in the tracking stage. Then motion events are recognized with local information for further corrections. At last, trajectories are linked and completed based on an active search area estimated by the established tracklets. The hierarchical framework designed in Hift enhances the ability of cell detection and tracking by combining the global assignment and local optimization in spatial-temporal dimension. Experimental results of Hift on three large-scale datasets with high cell densities and four sparse datasets demonstrate its efficacy. Hift is available at: http://www.csbio.sjtu.edu.cn/bioinf/Hift/ . [ABSTRACT FROM AUTHOR]

This paper investigates a composite learning controller for hypersonic longitudinal flight dynamics in presence of unknown dynamics. Different from previous designs, the controller is proposed without back-stepping through model transformation. This strategy simplifies the process of controller design and reduces the computation burden of parameter updating. For unknown dynamics, the radial basis function neural network (RBF NN) is used to approximate the lumped uncertainty. Base on the serial–parallel estimate model, the composite learning control scheme constructs a modeling error to evaluate the quality of learning. The highlight is that the composite learning includes the index of NN approximation performance which provides additional tuning of the system learning. Simulation results are presented and the effectiveness of the control strategy is verified. [ABSTRACT FROM AUTHOR]

In this work, we present a simple, robust and fast method to the perspective- n -point (P n P) problem for determining the position and orientation of a calibrated camera from known reference points. Our method transfers the pose estimation problem into an optimal problem, and only needs to solve a seventh-order and a fourth-order univariate polynomial, respectively, which makes the processes more easily understood and significantly improves the performance. Additionally, the number of solutions of the proposed method is substantially smaller than existing methods. Experiment results show that the proposed method can stably handle all 3D point configurations, including the ordinary 3D case, the quasi-singular case, and the planar case, and it offers accuracy comparable or better than that of the state-of-art methods, but at much lower computational cost. [ABSTRACT FROM AUTHOR]

The rapid development of modern wind turbine technology has led to increasing demand for improving system reliability and practical concern for robust fault monitoring scheme. This paper presents the investigation of a 5 MW Dynamic Wind Turbine Energy System that was designed to sustain condition monitoring and fault diagnosis with the goal of improving the reliability operations of universal practical control systems. A hybrid stochastic technique is proposed based on an augmented observer combined with eigenstructure assignment for the parameterisation and the genetic algorithm (GA) optimisation to address the attenuation of uncertainty mostly generated by disturbances. Scenarios-based are employed to explore sensor and actuator faults that have direct and indirect impacts on modern wind turbine system, based on monitoring components that are prone to malfunction. The analysis is aimed to determine the effect of concerned simulated faults from uncertainty in respect to environmental disturbances mostly challenged in real-world operations. The efficiency of the proposed approach will improve the reliability performance of wind turbine system states and diagnose uncertain faults simultaneously. The simulation outcomes illustrate the robustness of the dynamic turbine systems with a diagnostic performance to advance the practical solutions for improving reliable systems. [ABSTRACT FROM AUTHOR]

This paper is concerned with the robust stabilization of quantized feedback sliding-mode control (SMC) for a class of uncertain linear systems via an event-triggered approach. By introducing an event-triggered mechanism, the event-triggered state instead of the state itself is quantized. And the generated finite event-triggered quantized state signal is sent through a digital network to the decoder and used for constructing a sliding mode controller to stabilize the uncertain linear systems. The relation among the lower bound of the quantized measurement saturating parameter, the upper bound of the event-triggered threshold parameter and the robust stabilization condition of linear uncertain systems, is first presented. Then, under the relation and by the combination of the proposed event-triggered mechanism for the system state and the discrete on-line adjustment policy for the quantizer sensitivity, the established quantized and event-triggered SMC laws are shown to ensure the reachability of the desired switching surface and global robust stabilization of uncertain linear systems is achieved. The theoretical result is verified via simulation illustration finally. [ABSTRACT FROM AUTHOR]

In this paper, we propose two adaptive control schemes to solve the output tracking control problem of a class of uncertain switched nonlinear systems. The first is a robust scheme constructed based on common Lyapunov function method, aiming to achieve the insensitivity of closed-loop system to parameter switches by estimating the bound on switching parameters. It is analyzed that the system stability under arbitrary switching is established with such a scheme, but the control input required may be relatively large since the estimate of the bound on switching parameters is utilized. To avoid this, we further propose a direct adaptive control scheme by estimating the switching parameters directly, and derive a dwell-time condition for parameter switches based on an extended multiple Lyapunov functions method newly developed, such that the global boundedness of all the closed-loop signals and the convergence of tracking error to a residual around zero are ensured. The effectiveness of the proposed control schemes is illustrated through simulation studies. [ABSTRACT FROM AUTHOR]

The robustness of complex networks under attacks largely depends on the structure of a network and the nature of the attacks. Previous research on interdependent networks has focused on two types of initial attack: random attack and degree-based targeted attack. In this paper, a deliberate attack function is proposed, where six kinds of deliberate attacking strategies can be derived by adjusting the tunable parameters. Moreover, the robustness of four types of interdependent networks (BA–BA, ER–ER, BA–ER and ER–BA) with different coupling modes (random, positive and negative correlation) is evaluated under different attacking strategies. Interesting conclusions could be obtained. It can be found that the positive coupling mode can make the vulnerability of the interdependent network to be absolutely dependent on the most vulnerable sub-network under deliberate attacks, whereas random and negative coupling modes make the vulnerability of interdependent network to be mainly dependent on the being attacked sub-network. The robustness of interdependent network will be enhanced with the degree–degree correlation coefficient varying from positive to negative. Therefore, The negative coupling mode is relatively more optimal than others, which can substantially improve the robustness of the ER–ER network and ER–BA network. In terms of the attacking strategies on interdependent networks, the degree information of node is more valuable than the betweenness. In addition, we found a more efficient attacking strategy for each coupled interdependent network and proposed the corresponding protection strategy for suppressing cascading failure. Our results can be very useful for safety design and protection of interdependent networks. [ABSTRACT FROM AUTHOR]

In this paper, strength of fractional adaptive signal processing is exploited for parameter identification of control autoregressive autoregressive (CARAR) systems using normalized version of fractional least mean square (FLMS) and its recently introduced modification of type 1 and 2. The adaptation performance of the proposed normalized FLMS methods is compared with standard counterparts for CARAR identification model by taking different noise levels as well as fractional orders. The results of the statistical analyses are used to validate the consistency of the proposed normalized fractional adaptive methodologies in terms of convergence, accuracy and robustness. The reliability and effectiveness of the design schemes is further validated through consistently approaching the desired identification parameters based on performance metrics of mean square error, variance account for and Nash–Sutcliffe efficiency. [ABSTRACT FROM AUTHOR]

Traditional depth estimation from stereo images is usually formulated as a patch-matching problem, which requires post-processing stages to impose smoothness and handle depth discontinuities and occlusions. While recent deep network approaches directly learn a regressor for the entire disparity map, they still suffer from large errors near the depth discontinuities. In this paper, we propose a novel method to refine the disparity maps generated by deep regression networks. Instead of relying on ad hoc post-processing, we learn a unified deep network model that predicts a confidence map and the disparity gradients from the learned feature representation in regression networks. We integrate the initial disparity estimation, the confidence map and the disparity gradients into a continuous Markov Random Field (MRF) for depth refinement, which is capable of representing rich surface structures. Our disparity MRF model can be solved via efficient global optimization in a closed form. We evaluate our approach on both synthetic and real-world datasets, and the results show it achieves the state-of-art performance and produces more structure-preserving disparity maps with smaller errors in the neighborhood of depth boundaries. [ABSTRACT FROM AUTHOR]

Optimal control can ensure system safe operation with a high efficiency. However, only a few papers discuss optimal control strategies for solid oxide fuel cell (SOFC) systems. Moreover, the existed methods ignore the impact of parameter uncertainty on system instantaneous performance. In real SOFC systems, several parameters may vary with the variation of operation conditions and can not be identified exactly, such as load current. Therefore, a robust optimal control strategy is proposed, which involves three parts: a SOFC model with parameter uncertainty, a robust optimizer and robust controllers. During the model building process, boundaries of the uncertain parameter are extracted based on Monte Carlo algorithm. To achieve the maximum efficiency, a two-space particle swarm optimization approach is employed to obtain optimal operating points, which are used as the set points of the controllers. To ensure the SOFC safe operation, two feed-forward controllers and a higher-order robust sliding mode controller are presented to control fuel utilization ratio, air excess ratio and stack temperature afterwards. The results show the proposed optimal robust control method can maintain the SOFC system safe operation with a maximum efficiency under load and uncertainty variations. [ABSTRACT FROM AUTHOR]

Three-phase induction motors are extensively used in the industry due to their robustness characteristics, low cost and easy maintenance. Usually, it is necessary to implement drive and control systems for such motors, which requires the knowledge of their mechanical and electrical parameters. However, in some cases, these data are not immediately available, or the values of the parameters may change due to the wear of motor components. Such problems can be circumvented if an efficient parameter estimation technique is available. In order to automatically estimate the parameters efficiently, the present work proposes a method, based on the differential evolution algorithm, aimed at the estimation of the electrical and mechanical parameters of three-phase induction motors. Such algorithm is capable of estimating the parameters of the equivalent electrical circuit, such as stator and rotor resistances and leakage inductances, the magnetizing inductance, and also mechanical parameters, such as moment of inertia and the friction coefficient. The performance of the proposed parameter estimation technique is evaluated for three different input signals: (i) current signal of a phase associated with the speed measured from a tachogenerator, (ii) current signal of a phase associated with the speed acquired from a torquemeter, and (iii) only the current signal of one phase. Finally, a series of simulated and experimental results are presented to validate the proposed technique, and the results show the good performance of the proposed strategies. [ABSTRACT FROM AUTHOR]

In this paper, we consider estimation problems involving a class of nonlinear systems characterized by two non-standard attributes: (i) such systems evolve over multiple stages; and (ii) the dynamics in each stage involve unknown time-delays and unknown system parameters. These unknown quantities are to be estimated such that a least-squares error function between the system output and a set of noisy measurement data from a real plant is minimized. We first present the classical parameter estimation formulation, where the expectation of the error function is regarded as the cost function. However, in practice, there exists uncertainty in the distribution of the measurement data. The optimal parameter estimate should be able to withstand this uncertainty. Accordingly, we propose a new parameter estimation formulation, in which the cost function is the variance of the error function and the constraint indicates an allowable sacrifice from the optimal expectation value of the classical parameter estimation problem. For these two estimation problems, we show that the gradients of their cost functions and the constraint function with respect to the time-delays and system parameters can be computed through solving a set of auxiliary time-delay systems in conjunction with the governing multistage time-delay system, simultaneously. On this basis, we develop gradient-based optimization algorithms to determine the unknown time-delays and system parameters. Finally, we consider two example problems to illustrate the effectiveness and applicability of our proposed algorithms. [ABSTRACT FROM AUTHOR]

An appropriate model is a prerequisite for accurate state-of-charge (SOC) estimation. The widely used equivalent circuit models (ECMs) employ a variety of forms; thus, to find the optimum ECM is a primary task for SOC estimation. In this work, we examined eleven ECMs to fulfill the following goals: (1) to compare the typical ECMs for accuracy, stability, and robustness of model and SOC estimation; (2) to compare and evaluate the robustness of the ECMs considering model and sensor errors. The results indicate that the model accuracy does not always improve by increasing the order of the RC network. Conversely, over-fitting problems appear with a certain probability. The first- and second-order RC models are the best choice owing to their balance of accuracy and reliability for LiNMC batteries. The higher-order RC model has better robustness considering the variation in model parameters and sensor errors. Independently of the ECM adopted, an accurate OCV-SOC curve and high precision sensors are essential. [ABSTRACT FROM AUTHOR]

This paper presents three innovative techniques to estimate external perturbation torques acting on an Earth-orbiting satellite. The proposed techniques overcome the problem of derivative reconstruction from noisy measurement in the estimation procedure of the environmental perturbation torques. For this purpose, in the first technique, known as nonlinear disturbances observer (NDO) technique, derivative term is eliminated in the estimation equations by employing auxiliary variable vector and by selecting appropriate design parameters. In the second proposed approach, the robust exact differentiation via second-order sliding mode theory, known as Levant differentiator, is employed to decrease derivative estimation error in the process of estimation. To improve the performance of the Levant differentiator, the final innovative approach, known as sub-band adaptive robust differentiator (SARD) technique, is proposed in which the derivative term in the estimation process is reconstructed by using Levant differentiator and sub-band adaptive filter theory. In the SARD technique, the original noisy measurement signal is divided into sub-band signals and then, the Levant differentiator is applied to each sub-band signals to construct exact differentiator from noisy measurement. The novel aspect of paper is employing second-order sliding mode theory in the sub-bands frequency to improve the performance of derivative reconstruction in the presence of measurement noise. In this regard, a nonlinear model of the earth-orbiting satellite is simulated using specific navigation disturbances and the results verified the feasibility of the proposed strategies. [ABSTRACT FROM AUTHOR]

This paper proposes a novel terrain-relative navigation (TRN) approach for the asteroid pin-point landing mission. The key concept is encapsulating efficient crater matching with active LiDAR ranging. Importantly, a novel crater to 3D crater model matching approach is proposed. Then, the line-of-sight from lander to crater is employed to guide the LiDAR ranging, and parameters of the matched 3D crater model is efficiently fused with LiDAR ranging to compensate its measurement, resulting in the robustness against the variation of terrain relief. In addition, a crater selection mechanism is proposed to reduce the estimation uncertainty. Monte Carlo simulation results show that the proposed approach is able to provide an adequate navigation performance under a high terrain relief, which is an evident improvement over the traditional methods. [ABSTRACT FROM AUTHOR]

An analytical equation is derived using influence function approximation to calculate the variance of the state estimate for traditional robust state estimators such as the Quadratic-Constant, Quadratic-Linear, Square-Root, Schweppe-Huber Generalized-M and Multiple-Segment estimator. The equation gives insights into the precision of the estimation. Using the equation, the variance of a state estimate can be expressed as a function of measurement noise variances enabling the selection of sensors for a specified estimator precision. It can also be used to search for the optimum estimator parameters to give the minimum sum of variances. The well-known Weighted-Least-Squares variance formula is a special case of the equation and simulations on the IEEE 14-bus system are given to show the usefulness of the equation. [ABSTRACT FROM AUTHOR]

The dynamics of particulate processes can be described by population balance equations which are governed by the phenomena of growth, nucleation, aggregation and breakage. Estimating the kinetics of the latter phenomena is a major challenge particularly for particle aggregation because first principle models are rarely available and the kernel estimation from measured population density data constitutes an ill-conditioned problem. In this work we demonstrate the estimation of aggregation kernels from experimental data using an inverse problem approach. This approach is based on the approximation of the aggregation kernel by use of Laurent polynomials. We show that the aggregation kernel can be well estimated from in silico data and that the estimation results are robust against substantial measurement noise. The method is demonstrated for three different aggregation kernels. Good agreement between true and estimated kernels was found in all investigated cases. [ABSTRACT FROM AUTHOR]

The performance of robust optimization is closely connected with probabilistic bounds that determine the probability of constraint violation due to uncertain parameter realizations. In Part I of this work, new a priori and a posteriori probabilistic bounds were developed for cases when robust optimization is applied to uncertain optimization problems with parameters whose probability distributions were unknown. In Part II, the focus shifted to known probability distributions and a priori bounds. In this paper, new, tight a posteriori expressions are developed for constraints containing parameters with specific known distributions, that is, those attributed normal, uniform, discrete, gamma, chi-squared, Erlang, or exponential distributions. The nature of some of the expressions requires efficient implementations, and new algorithmic methods are discussed which greatly improve applicability. These new expressions are much tighter than existing bounds and greatly reduce the conservatism of robust solutions. The theoretical and algorithmic results of Parts I, II, and III allow for wider usage of robust optimization in process synthesis and operations research applications. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid Prony-cross-correlation real-time tracking dominant mode and parameter estimation using simulated and phasor measurement unit (PMU) data. It proposes a new method for detecting the dominant mode during the transient phenomena in electrical power systems. The performance of the proposed method has been compared to some of the existing methods for estimating the frequency and damping factor of a signal under noisy conditions, such as the Prony and EKF methods. In contrast to the traditional Prony and EKF methods for estimating the modes parameters, which require the order of the model and the initial parameters of the mode, the hybrid Prony-cross-correlation does not require any previous information. Three Brazilian systems (reduced, north and NIS) have been studied for both stationary and transient phenomena in power systems to determine the robustness and the accurate estimates even in the presence of noise in the measured signal by the proposed method. [ABSTRACT FROM AUTHOR]

Real-time control systems rely on reliable estimates of states and parameters in order to provide accurate and safe control of electro-mechanical systems. The task of extracting state and parametric values from system’s partial measurements is referred to as state and parameter estimation. The main goal is minimizing the estimation error as well as maintaining robustness against the noise and modeling uncertainties. The development of estimation techniques spans over five centuries, and involves a large number of contributors from a variety of fields. This paper presents a tutorial on the main Gaussian filters that are used for state estimation of stochastic dynamic systems. The main concept of state estimation is firstly described based on the Bayesian paradigm and Gaussian assumption of the noise. The filters are then categorized into several groups based on their applications for state estimation. These groups involve linear optimal filtering, nonlinear filtering, adaptive filtering, and robust filtering. New advances and trends relevant to each technique are addressed and discussed in detail. [ABSTRACT FROM AUTHOR]

The purpose of this article is to develop a new approach to the robust regulation problem for plants which do not necessarily admit coprime factorizations. The approach is purely algebraic and allows us dealing with a very general class of systems in a unique simple framework. We formulate the famous internal model principle in a form suitable for plants defined by fractional representations which are not necessarily coprime factorizations. By using the internal model principle, we are able to give necessary and sufficient solvability conditions for the robust regulation problem and to parameterize all robustly regulating controllers. [ABSTRACT FROM AUTHOR]

One of the big challenges that system operators have always dealt with is uncertainty of different parameters in the power systems. In this paper, optimal performance of an on-grid PV/fuel cell/battery hybrid system has been evaluated in the presence of demand response program with considering electrical load uncertainty. Information gap decision theory (IGDT) has been proposed to model the uncertainty of electrical load. Utilizing different strategies obtained through the robustness and opportunity functions, operator will have several options to control the uncertainty. By shifting some percentage of load from peak periods to other periods, DRP flattens load curve and minimizes total cost of hybrid system. A sample system is simulated and the results are compared to validate the proposed techniques. [ABSTRACT FROM AUTHOR]

A novel finite-time convergent estimation technique is proposed for identifying the amplitude, frequency and phase of a biased sinusoidal signal. Resorting to Volterra integral operators with suitably designed kernels, the measured signal is processed yielding a set of auxiliary signals in which the influence of the unknown initial conditions is removed. A second-order sliding mode-based adaptation law–fed by the aforementioned auxiliary signals–is designed for finite-time estimation of the frequency, amplitude, and phase. The worst case behavior of the proposed algorithm in presence of the bounded additive disturbances is fully characterized by Input-to-State Stability arguments. The effectiveness of the estimation technique is evaluated and compared with other existing tools via extensive numerical simulations. [ABSTRACT FROM AUTHOR]

This paper presents an FPGA (field-programmable gate array) based fault emulation system for analysis of fault impact on security and robustness of RFID (radio frequency identification) tags. This emulation system that deals with any RFID protocol consists of two tag-reader pairs, a fault injection module and an emulation controller all implemented in a single FPGA. The designed approach performs single event upset (SEU) and single event transient (SET) fault injection and permits with high flexibility to set communication scenarios and related parameters. Moreover, we propose a classification of produced errors to evaluate fault impacts and identify most sensitive tag flip-flops causing large number of failures and security concerns. The proposed fault injection approach provides suitable means to increase tags' security and robustness. In our experimentation campaign, an ultra-high frequency (UHF) tag architecture has been exposed to intensive SEU and SET fault injections. The duration of the campaign including results analysis is 30 min in where 6,215,316 faults are experimented. Our results have shown that the tag has tolerated 61.82% of SEUs and 67.83% of SETs. The flip-flops that constitute the tag FSM (finite state machine) have been identified as the most sensitive parts causing large number of failures. [ABSTRACT FROM AUTHOR]

A novel integrated model predictive iterative learning control (MPILC) strategy with dynamic R-parameter for batch processes is proposed in this paper. It systematically integrates batch-axis information and time-axis information into one uniform frame, namely the iterative learning controller (ILC) in the domain of batch-axis, while a model predictive controller (MPC)with time-varying prediction horizon in the domain of time-axis. As a result, the operation policy of batch process can be regulated during one batch, which leads to superior tracking performance and better robustness against disturbance and uncertainty. Moreover, both model identification and dynamic R-parameter are employed to eliminate the model-plant mismatch and make zero-error tracking possible. Next, the convergence and tracking performance of the proposed integrated model predictive learning control system are given rigorous description and proof. Lastly, the effectiveness of the proposed method is verified by one example. [ABSTRACT FROM AUTHOR]

In this paper, a robust actuator fault tolerant control (FTC) strategy for systems with polytopic uncertainty is proposed. Two types of model descriptions are investigated in this work: convex polytopic model uncertainty and linear-parameter-varying (LPV) convex polytopic model uncertainty; where, in the latter, the varying parameter is assumed to be measured. The proposed FTC strategy combines a robust fault detection and isolation (FDI) approach based on set separation with controller reconfiguration based on the use of a bank of virtual actuators (VA). Both, FDI and controller reconfiguration modules, use the same bank of VA. The robust FDI method is based on the separation of relevant sets defined for measurable residual signals, which are computed using the VA signals and taking into account system disturbances and model uncertainty. The closed-loop system is reconfigured by means of a VA which is adapted to the fault situation detected by the FDI unit. The performance of the resulting robust FTC scheme is analysed for the two types of model descriptions by means of a simulation example. [ABSTRACT FROM AUTHOR]

The objective of this paper is first to design an Adaptive Linear Kalman Filter (ALKF) to estimate nonlinear process states and to compare the performance of the ALKF with the Extended Kalman Filter (EKF). The designed ALKF is next used to detect sensor and actuator biases which may occur either sequentially or simultaneously using a Multi Model ALKF (MMALKF). Finally the Multi Model Adaptive Linear H ∞ Filter (MMALH ∞ F) is designed to increase the robustness of bias Detection in the presence of unknown noise statistics and unmodeled dynamics. The proposed estimator is demonstrated on the variable area tank process and Continuously Stirred Tank Reactor (CSTR) process to show its efficacy. [ABSTRACT FROM AUTHOR]