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The problem of decentralised robust stabilisation is considered for a class of uncertain large-scale time-delay interconnected dynamical systems. In the paper, the upper bounds of delayed state perturbations, uncertainties, interconnection terms, and external disturbances are assumed to be completely unknown, and the delays are assumed to be any non-negative constants. For such a class of uncertain large-scale time-delay interconnected systems, a new method is presented whereby a class of adaptation-free decentralised local robust state feedback controllers can be constructed. In addition, it is also shown that the solutions of uncertain large-scale time-delay interconnected systems can be guaranteed to be uniformly ultimately bounded. Finally, as an application to the practical mechanical systems, some simulations of a numerical example are provided to demonstrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

In network analysis, a community can be defined as a group of nodes of a network (or clusters) that are densely interconnected with each other but only sparsely connected with the rest of the network. Several algorithms have been used to obtain a convenient partition allowing extracting the communities in a given network, based on their topology and, possibly, the weights of links. These weights usually represent specific characteristics for example: distance, reactance, reliability. Even if the optimum partitions could be derived, there are uncertainties associated to the network parameters that affect the network partition. In this paper, the authors extend a previous approach for assessing the effects of weight uncertainties on community structures and propose a global approach for (a) understanding the global similarity among the partitions; (b) analyzing the robustness of the communities derived without uncertainty; and (c) quantifying the robustness of the inter-community links. To this aim an uncertainty propagation analysis, based on the Monte Carlo technique is proposed. The approach is illustrated through analyzing the topology of an electric power system. [ABSTRACT FROM AUTHOR]

This paper addresses a design method of controller networks, i.e., networked controllers which cooperatively determine the control inputs by exchanging the information with their neighbors. The problem considered here is to design the controllers stabilizing the resulting feedback system for an unknown network topology. As a solution to the problem, we propose controllers such that the entire network acts as a state feedback controller through a consensus protocol, and derive gain conditions to stabilize the resulting feedback system. This enables us to obtain a controller network that is robust against uncertainties of the network topology. [ABSTRACT FROM AUTHOR]

For a class of linear discrete-time uncertain systems, a feedback feed-forward iterative learning control (ILC) scheme is proposed, which is comprised of an iterative learning controller and two current iteration feedback controllers. The iterative learning controller is used to improve the performance along the iteration direction and the feedback controllers are used to improve the performance along the time direction. First of all, the uncertain feedback feed-forward ILC system is presented by an uncertain two-dimensional Roesser model system. Then, two robust control schemes are proposed. One can ensure that the feedback feed-forward ILC system is bounded-input bounded-output stable along time direction, and the other can ensure that the feedback feed-forward ILC system is asymptotically stable along time direction. Both schemes can guarantee the system is robust monotonically convergent along the iteration direction. Third, the robust convergent sufficient conditions are given, which contains a linear matrix inequality (LMI). Moreover, the LMI can be used to determine the gain matrix of the feedback feed-forward iterative learning controller. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM PUBLISHER]

We present a fast and online human-robot interaction approach that progressively learns multiple object classifiers using scanty human supervision. Given an input video stream recorded during the human-robot interaction, the user just needs to annotate a small fraction of frames to compute object specific classifiers based on random ferns which share the same features. The resulting methodology is fast (in a few seconds, complex object appearances can be learned), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier. We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in indoor and outdoor scenarios containing a multitude of different objects. We show that with little human assistance, we are able to build object classifiers robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds. [ABSTRACT FROM AUTHOR]

In this paper a robust and optimal attitude control design that uses the minimal kinematic parameters and angular velocities feedback is presented for the three-axis attitude stabilization of spacecraft with inertia uncertainties. After proposing a new class of robust attitude control laws for the three-axis attitude stabilization of spacecraft with inertia uncertainties, it is shown that the proposed robust attitude control laws are optimal with respect to performance indices. A numerical example is given to illustrate the theoretical results presented in this paper. [ABSTRACT FROM AUTHOR]

Uncertainties, parameter changes, and disturbances lie among the most frequently encountered problems in practical control applications. Sliding mode control (SMC) is one of the robust control methods developed to provide a certain control performance under such circumstances. SMC can also be achieved in relay control systems. The aim is to obtain an overall system that is robust to disturbances, noise, and parameter changes by forcing the relay element to operate in sliding mode. While SMC methods have been traditionally developed in state-space, in relay control systems, it is possible to define SMC based on the input-output model of a system. This way, sliding motion can be achieved by only utilizing the output signal, without the need to know or measure the system states. In this study, the relay sliding mode control method based on the input-output model proposed in previous studies is revisited. Sliding conditions under ideal operating conditions are reformulated to fortify the theoretical background. Some additional issues, namely the effects of disturbances and measurement noise on the sliding mode conditions, are addressed and analyzed in detail. Finally, the theoretical results are put to the test by detailed simulation examples. [ABSTRACT FROM AUTHOR]

Mathematical models of biochemical reaction networks are important tools in systems biology and systems medicine, e.g., to analyze disease causes or to make predictions for the development of effective treatments. Models are also used in synthetic biology for the design of circuits that perform specialized tasks. Prediction, analysis and design require plausible and reliable models, that is, models must reflect the properties of interest of the considered biochemical networks. One remarkable property of biochemical networks is robust functioning over a wide range of perturbations and environmental conditions. The intrinsic robustness of a network should be reflected into its associated mathematical model. The description and analysis of robustness in biochemical reaction networks are challenging, however, because accounting explicitly for the various types of structural, parametric and data uncertainty in the description of the models is not straightforward. Furthermore, system properties are typically inherently uncertain and often only given by qualitative or verbal descriptions that impede a straightforward and comprehensive mathematical analysis. In the first part of this overview article, network functions and behaviors of interest are formally defined, and different classes of uncertainties and perturbations are consistently described. The second part reviews frequently used mathematical formulations and presents the authors’ recent developments for robustness analysis, estimation, and model-based prediction. One biochemical network model is used to illustrate the capabilities of various methods to deal with the different types of uncertainties and robustness requirements. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the robustness and fragility problem for piecewise affine (PWA) control laws for discrete-time linear system dynamics in the presence of parametric uncertainty of the state space model. A generic geometrical approach will be used to obtain robustness/fragility margins with respect to the positive invariance properties. For PWA control laws defined over a bounded region in the state space, it is shown that these margins can be described in terms of polyhedral sets in parameter space. The methodology is further extended to the fragility problem with respect to the partition defining the controller. Finally, several computational aspects are presented regarding the transformation from the theoretical formulations to explicit representations (vertex/halfspace representation of polytopes) of these sets. [ABSTRACT FROM AUTHOR]

This paper considers minimum cost flow problem in dynamic networks with uncertain costs. First, we present a short introduction of dynamic minimum cost flow. Then, we survey discrete and continuous dynamic minimum cost flow problems, their properties and relationships between them. After that, the minimum cost flow problem in discrete dynamic network with uncertainty in the cost vector is considered such that the arc cost can be changed within an interval. Finally, we propose an algorithm to find the optimal solution of the proposed model. [ABSTRACT FROM AUTHOR]

To solve optimization problems with matrix uncertainty, a novel optimization approach is proposed based on chance-constrained and robust optimization, which focuses on constraints with continuous uncertainty, especially with matrix uncertainty. In chance-constrained approach, constraints with matrix uncertainty are always regarded as joint chance constraints, which can be simplified into individual chance constraints and can be further reformulated into algebraic constraints by robust methods. Motivated by reformulation of chance constraints with right-hand side uncertainty, a novel formulation of constraints with left-hand side uncertainty is proposed, where the uncertainty is described as intervals related to the confidence level of chance constraints. Through using kernel density estimation, confidence sets of uncertain parameters are built to approximate unknown true probability density functions. The approach is illustrated with a motivating and process industry scheduling example with energy consumption uncertainties. [ABSTRACT FROM AUTHOR]

The problems of stationary robust L ∞ -induced filtering and deconvolution are addressed for discrete-time linear systems with deterministic and stochastic uncertainties in the state–space model. Stochastic uncertainties are in the form of state- and input-dependent multiplicative white noises which appear in both state and the measurement equations. The deterministic part of the system matrices and covariance matrices of the stochastic parameters is unknown and resides in a given polytopic-type domain. For this system a new lemma is derived which characterizes the induced L ∞ norm disturbance attenuation performance by linear matrix inequalities (LMIs). According to this lemma, the problem of estimator design is solved for stochastic uncertain systems based on the notion of quadratic stability. To further reduce the overdesign in the quadratic framework, this paper also proposes a parameter-dependent design procedure, which is much less conservative than the quadratic approach. The proposed estimators guarantee the mean-square exponential stability of estimation error dynamics and satisfy the prescribed induced L ∞ performance index. Two examples are used to demonstrate the proposed methods and their effectiveness. [ABSTRACT FROM AUTHOR]

This paper investigates 2D mixed continuous–discrete-time systems whose coefficients are polynomial functions of an uncertain vector constrained into a semialgebraic set. It is shown that a nonconservative linear matrix inequality (LMI) condition for ensuring robust stability can be obtained by introducing complex Lyapunov functions depending polynomially on the uncertain vector and a frequency. Moreover, it is shown that nonconservative LMI conditions for establishing upper bounds of the robust H ∞ and H 2 norms can be obtained by introducing analogous Lyapunov functions depending rationally on the frequency. Some numerical examples illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper deals with the development of a robust model predictive control strategy with guarantee of stability, applicable to the stable and unstable processes. The model uncertainty is assumed to be described by a discrete set of linear models (multi-plant uncertainty), and the robustness is achieved by assembling cost-contracting constraints for all the possible models in the uncertainty domain. On the basis of a suitable state-space model description, an offset free control law is obtained by means of a one-step optimization formulation. The usefulness of the method proposed here is illustrated with control simulations of an unstable reactor system taken from the literature. [ABSTRACT FROM AUTHOR]

In recent years, the penetration of renewable resources in electrical power systems has increased. These renewable resources add more complexities to power system operations, due to their intermittent nature. As a result, operators must acquire additional reserves in order to maintain reliability. However, one persistent challenge is to determine the optimal location of reserves and this challenge is exacerbated by the inability to predict key transmission bottlenecks due to this added uncertainty. This paper presents robust corrective topology control as a congestion management tool to manage power flows and the associated renewable uncertainty. The proposed day-ahead method determines the maximum uncertainty in renewable resources in terms of do-not-exceed limits combined with corrective topology control. The day-ahead topology control formulation is based on the direct current optimal power flow; therefore, topology control solutions obtained from these algorithms are tested for AC feasibility and system stability. The numerical results provided are based on the IEEE-118 bus test case and the Tennessee Valley Authority (TVA) test system. [ABSTRACT FROM AUTHOR]

This paper investigates the non-fragile and robust finite-time H ∞ control problem for a class of uncertain Markovian jump nonlinear systems with bounded parametric uncertainties and norm-bounded disturbance. By employing stochastic analysis and linear matrix inequality techniques, sufficient criteria of stochastic finite-time boundedness and stochastic H ∞ finite-time boundedness are first provided for the class of stochastic jump systems. Then, a controller is designed such that the class of stochastic nonlinear dynamics are stochastically finite-time bounded and have an H ∞ attention performance level by utilizing matrix decomposition approach. Furthermore, the analysis and design of non-fragile and robust finite-time controller are provided to guarantee that the class of uncertain stochastic systems are stochastically finite-time boundeded with a prescribed attention index by using non-fragile control technique. In addition, we also deal with the analysis and design of stochastic finite-time stability and stochastic finite-time stabilization. All criterions can be characterized in terms of linear matrix inequalities. Finally, two examples are also given to illustrate the effectiveness of obtained results. [ABSTRACT FROM AUTHOR]

In this study, we consider the robust uncapacitated multiple allocation p -hub median problem under polyhedral demand uncertainty. We model the demand uncertainty in two different ways. The hose model assumes that the only available information is the upper limit on the total flow adjacent at each node, while the hybrid model additionally imposes lower and upper bounds on each pairwise demand. We propose linear mixed integer programming formulations using a minmax criteria and devise two Benders decomposition based exact solution algorithms in order to solve large-scale problems. We report the results of our computational experiments on the effect of incorporating uncertainty and on the performance of our exact approaches. [ABSTRACT FROM AUTHOR]

Accurate measurement of the convective heat transfer coefficient h c at external surfaces, e.g. at building facades and roofs, is of fundamental importance for heat transfer studies of the built environment. There are two basic methods for measurement of h c , the Loveday and Ito methods, which use one and two heated sensor units, respectively. To guide in selection of method and operating conditions, and in design of the sensor, we performed an error analysis. This included estimation of systematic errors, comparison between methods, and to established Nusselt number correlations, sensitivity analysis, and an evaluation of the measurement uncertainty. The main conclusion was that both methods, at forced convection, yielded measurement uncertainties at the 4% level, provided that the Ito method was operated under the new condition, where one of its sensors remained unheated. However, at natural convection conditions, the Ito method cannot be operated with one of its sensors unheated, since h c is then zero at that sensor surface, which violates the method assumption that h c is the same at both sensors. Sensitivity analysis showed that systematic errors will be reduced by decreasing the sensor surface emissivity. The major source of measurement uncertainty was the conductive heat flux estimate. [ABSTRACT FROM AUTHOR]

This paper studies a numerical optimization-based extremum seeking scheme for systems with unmodeled dynamics and unknown disturbances without using dither signals. The robust extremum seeking scheme is composed of a numerical gradient estimator, a numerical optimizer and an extended-state observer based state regulator. A conjugate gradient method is adopted to achieve extremum seeking. A nonlinear extended-state observer based state regulator is proposed to regulate the states. The robust extremum seeker is based on the numerical optimization based extremum seeking framework. Sufficient conditions are derived to ensure the convergence of the overall extremum seeking scheme. The ultimate convergence bound is quantified for a class of strongly convex functions. A numerical example is presented to verify the proposed method. [ABSTRACT FROM AUTHOR]

Essential ingredients for robust control are the ability to cope with different types of system behavior following modeling imperfections and the ability to assure a certain performance level. In this paper, we propose to use an actuator fault-tolerant control law to govern, during experiments, the stabilization of a bicycle robot with an inertial wheel in order to take into account unmodeled uncertainty introduced by using a linearized model in an LQR fashion. Our proposal is illustrated by signal plots and the values of performance indices obtained from a set of experiments. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a robust proportional derivative (PD) controller based on linear matrix inequality (LMI) for the control of a hub angular position and end-point deflection of a planar two-link flexible manipulator. The dynamics of the manipulator is uncertain and time varying due to the variation of payloads that result in large variations in the excitation of flexible modes. Practical design steps are presented in which the LMI-based conditions are formulated to obtain a robust PD gains to control the flexible manipulator. The robust controller has an advantage as compared to the Ziegler-Nichols tuned PD controller as the identified PD gains can be used to control the system under various loading conditions. The performances of the proposed controller are evaluated in terms of input tracking capability of the hub angular position response and level of deflection of both links of the flexible manipulator. Experimental results show that despite using the same sets of PD gains, LMI-PD control provides better robustness and system performance. [ABSTRACT FROM AUTHOR]

Uncertainty quantification has recently been receiving much attention from aerospace engineering community. With ever-increasing requirements for robustness and reliability, it is crucial to quantify multidisciplinary uncertainty in satellite system design which dominates overall design direction and cost. However, coupled multi-disciplines and cross propagation hamper the efficiency and accuracy of high-dimensional uncertainty analysis. In this study, an uncertainty quantification methodology based on active subspaces is established for satellite conceptual design. The active subspace effectively reduces the dimension and measures the contributions of input uncertainties. A comprehensive characterization of associated uncertain factors is made and all subsystem models are built for uncertainty propagation. By integrating a system decoupling strategy, the multidisciplinary uncertainty effect is efficiently represented by a one-dimensional active subspace for each design. The identified active subspace is checked by bootstrap resampling for confidence intervals and verified by Monte Carlo propagation for the accuracy. To show the performance of active subspaces, 18 uncertainty parameters of an Earth observation small satellite are exemplified and then another 5 design uncertainties are incorporated. The uncertainties that contribute the most to satellite mass and total cost are ranked, and the quantification of high-dimensional uncertainty is achieved by a relatively small number of support samples. The methodology with considerably less cost exhibits high accuracy and strong adaptability, which provides a potential template to tackle multidisciplinary uncertainty in practical satellite systems. [ABSTRACT FROM AUTHOR]

The problem of robust adaptive predictive control for a class of discrete-time nonlinear systems is considered. First, a parameter estimation technique, based on an uncertainty set estimation, is formulated. This technique is able to provide robust performance for nonlinear systems subject to exogenous variables. Second, an adaptive MPC is developed to use the uncertainty estimation in a framework of min–max robust control. A Lipschitz-based approach, which provides a conservative approximation for the min–max problem, is used to solve the control problem, retaining the computational complexity of nominal MPC formulations and the robustness of the min–max approach. Finally, the set-based estimation algorithm and the robust predictive controller are successfully applied in two case studies. The first one is the control of anonisothermal CSTR governed by the van de Vusse reaction. Concentration and temperature regulation is considered with the simultaneous estimation of the frequency (or pre-exponential) factors of the Arrhenius equation. In the second example, a biomedical model for chemotherapy control is simulated using control actions provided by the proposed algorithm. The methods for estimation and control were tested using different disturbances scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a control strategy for achieving a robust minimum variance controller (MVC) by modelling uncertainty using a type-2 fuzzy set and satisfying H∞ norm specifications. In this paper, an MVC is designed considering three types of structural, parametric and algorithmic uncertainties. Thus, an interval type-2 fuzzy set is utilized. This paper utilizes one method to construct interval type-2 fuzzy set models by the uncertain interval data and model error modelling approach. In addition, a sufficient condition is developed that guarantees the stability and robustness of the controller. The design of a robust generalized MVC by the identified model is presented. Finally, an experimental test is used to clarify the effectiveness of the proposed control scheme. The proposed method (omitting uncertainty of the linear model) considerably decreases output variance from 0.15 to 0.0018. [ABSTRACT FROM AUTHOR]

A realistic control paradigm should concurrently account for different sources of uncertainty such as those in modeling parameters, external disturbances and noise, as well as operational cost. Yet, this is a daunting task for which many current control approaches lack in one aspect or the other. In particular, the consumed control energy is an important aspect of controller design that is often ignored. In this paper, we propose a stable robust adaptive interval type-2 fuzzy H 2 /H ∞ controller (RAIT2FH 2 H ∞ C) for a class of uncertain nonlinear systems that aims to address the above concerns through its hybrid robust/adaptive structure. In particular, the H 2 energy and tracking cost function is minimized with respect to a H ∞ disturbance attenuation constraint, while the adaptive interval fuzzy logic system (IT2FLS) handles the uncertainties in approximating the unknown nonlinear dynamics of the system. In principle, the interval fuzzy logic approach aims to manage portions of uncertainty that could not be precisiated, leading to improved error performance. Several simulation studies, with or without disturbance and noisy measurements, as well as actual experimental implementation on a 3-PSP (prismatic-spherical-prismatic) parallel robot confirm this assessment. More specifically, in comparison with a competing methodology as well as its type-1 counterpart, the proposed interval type-2 strategy expends better or comparative control effort while reaching considerably better tracking performance. [ABSTRACT FROM AUTHOR]

This paper presents new results on linear transceiver designs in a multiple-input–multiple-output (MIMO) link. By considering the minimal total mean-square error (MSE) criterion, we prove that the robust optimal linear transceiver design has a channel-diagonalizing structure, which verifies the conjecture in the previous work. Based on this property, the original design problem can be transformed into a scalar problem, whose global optimal solution is first obtained in this work. Simulation results show the performance advantages of our solution over the existing schemes. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of robust stability analysis for discrete-time neural networks with time-varying coupling delays, random parameter uncertainties and time-varying leakage delays. The uncertainties enter into the system parameters in a random way and such randomly occurring uncertainties obey certain mutually uncorrelated Bernoulli-distributed white noise sequences. The important feature of the results reported here is that the probability of occurrence of the parameter uncertainties are known a priori. Constructing suitable Lyapunov–Krasovskii functional (LKF) terms, sufficient conditions ensuring the stability of the discrete-time neural networks are derived in terms of linear matrix inequalities (LMIs). Finally, numerical examples are rendered to exemplify the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

Nowadays, the number of Web services on the Internet is quickly increasing. Meanwhile, different service providers offer numerous services with the similar functions. Quality of Service (QoS) has become an important factor used to select the most appropriate service for users. The most prominent QoS-based service selection models only take the certain attributes into account, which is an ideal assumption. In the real world, there are a large number of uncertain factors. In particular, at the runtime, QoS may become very poor or unacceptable. In order to solve the problem, a global service selection model based on uncertain QoS was proposed, including the corresponding normalization and aggregation functions, and then a robust optimization model adopted to transform the model. Experiment results show that the proposed method can effectively select services with high robustness and optimality. [ABSTRACT FROM AUTHOR]

GPS/INS integrated system is very subject to uncertainties due to exogenous disturbances, device damage, and inaccurate sensor noise statistics. Conventional Kalman filer has no robustness to address system uncertainties which may corrupt filter performance and even cause filter divergence. Based on the INS error dynamic equation, a robust Kalman filter is analyzed and applied in loosely coupled GPS/INS integration system. The norm bounded robust Kalman filter, with recursive form by solving two Riccati equations, guarantees a estimation variance bound for all the admissible uncertainties, and can evolve into the conventional Kalman filter if no uncertainties are considered. This paper will analyze the suitable case for the robust Kalman filter in GPS/INS system, the filter characteristics including parameter setting, parameter meaning, and filter convergence condition are discussed simutaneously. The robust filter performance will be compared with conventional Kalman filter through simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a new sum-of-squares (SOS, for brevity) design framework for robust control of polynomial fuzzy systems with uncertainties. Two kinds of robust stabilization conditions are derived in terms of SOS. One is global SOS robust stabilization conditions that guarantee the global and asymptotical stability of polynomial fuzzy control systems. The other is semiglobal SOS robust stabilization conditions. The latter is available for very complicated systems that are difficult to guarantee the global and asymptotical stability of polynomial fuzzy control systems. The main feature of all the SOS robust stabilization conditions derived in this paper are to be expressed as nonconvex formulations with respect to polynomial Lyapunov function parameters and polynomial feedback gains. Since a typical transformation from nonconvex SOS design conditions to convex SOS design conditions often results in some conservative issues, the new design framework presented in this paper gives key ideas to avoid the conservative issues. The first key idea is that we directly solve nonconvex SOS design conditions without applying the typical transformation. The second key idea is that we bring a so-called copositivity concept. These ideas provide some advantages in addition to relaxations. To solve our SOS robust stabilization conditions efficiently, we introduce a gradient algorithm formulated as a minimizing optimization problem of the upper bound of the time derivative of an SOS polynomial that can be regarded as a candidate of polynomial Lyapunov functions. Three design examples are provided to illustrate the validity and applicability of the proposed design framework. The examples demonstrate advantages of our new SOS design framework for the existing linear matrix inequality approaches and the existing convex SOS approach. [ABSTRACT FROM PUBLISHER]

This study is concerned with the problem of global robust finite-time stabilisation of a class of unknown purefeedback systems with input dead-zone non-linearities. The pure-feedback non-linear system under consideration has a pseudo-affine property and is with non-linearly parameterised uncertainties. By adopting and improving the existing finite-time backstepping control approach, a robust finite-time stabiliser is given, which could ensure the global finite-time stability of the trivial solution of resulting closed-loop system. A numerical and a realistic examples are employed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a robust approach for scheduling of smart appliances and electrical energy storages (EESs) in active apartments with the aim of reducing both the electricity bill and the \CO2 emissions. The proposed robust formulation takes the user behavior uncertainty into account so that the optimal appliances schedule is less sensitive to unpredictable changes in user preferences. The user behavior uncertainty is modeled as uncertainty in the cost function coefficients. In order to reduce the level of conservativeness of the robust solution, we introduce a parameter allowing to achieve a trade-off between the price of robustness and the protection against uncertainty. Mathematically, the robust scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which is solved by using standard algorithms. The numerical results show effectiveness of the proposed approach to increase both the electricity bill and \CO2 emissions savings, in the presence of user behavior uncertainties. Mathematical insights into the robust formulation are illustrated and the sensitivity of the optimum cost in the presence of uncertainties is investigated. Although home appliances and EESs are considered in this work, we point out that the proposed scheduling framework is generally applicable to many use cases, e.g., charging and discharging of electrical vehicles in an effective way. In addition, it is applicable to various scenarios considering different uncertainty sources, different storage technologies and generic programmable electrical loads, as well as different optimization criteria. [ABSTRACT FROM AUTHOR]

Azimuthal angles and Doppler frequencies of radar targets usually suffer from estimation uncertainty in practice, which poses a big challenge in target detection. In this work, we consider the problem of target detection in the presence of uncertainty in the azimuthal angle and Doppler frequency. This uncertainty is characterized by a subspace model. Precisely, the spatial and temporal steering vectors of the target are assumed to lie in certain known subspaces but with unknown coordinates. Two detectors are devised in the framework of generalized likelihood ratio test. Moreover, the properties of the proposed detectors are derived for the case of sufficiently large training data. It is shown by numerical examples that the detector derived according to the one-step design procedure has higher probability of detection than its counterparts in the absence of signal mismatch, while the detector devised according to the two-step design procedure is most robust to the signal mismatch. [ABSTRACT FROM AUTHOR]

The fixed-time master–slave synchronization of Cohen–Grossberg neural networks with parameter uncertainties and time-varying delays is investigated. Compared with finite-time synchronization where the convergence time relies on the initial synchronization errors, the settling time of fixed-time synchronization can be adjusted to desired values regardless of initial conditions. Novel synchronization control strategy for the slave neural network is proposed. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, some sufficient schemes are provided for selecting the control parameters to ensure synchronization with required convergence time and in the presence of parameter uncertainties. Corresponding criteria for tuning control inputs are also derived for the finite-time synchronization. Finally, two numerical examples are given to illustrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

We address a stochastic dynamic distribution problem where a family of products needs to be shipped from a warehouse to a distribution center (DC). Uncertainty is on carriers' availability and demand at the DC. Internal, external and spot carriers must be optimally selected to minimize the expected discounted cost of transportation, inventories and shortages. We numerically prove that an optimal selection policy, SDMBSP, is based on three thresholds of the available inventory in the DC. A simulation model is proposed and proves the robustness of the SDMBSP and its outperformance over two other carrier selection policies. [ABSTRACT FROM AUTHOR]

Many multiple-criteria decision-making (MCDM) methods have been proposed for decision-making environments. However, the performance of these methods is degraded by the uncertainty and inaccuracy which characterizes most practical decision-making environments as a result of the inherent prejudices and preferences of the decision-makers or experts and an insufficient volume of multiple inputs and outputs (MIO) information. Accordingly, the present study proposes an enhanced MIO classification method to address these limitations of existing MCDM methods. The proposed MIO classification method designated as the FVM-index method integrates fuzzy set theory ( F ST), variable precision rough set ( V PRS) theory, and a modified cluster validity index ( M CVI) function, and is designed specifically to filter out the uncertainty and inaccuracy inherent in the surveyed MIO real-valued dataset; thereby improving the classification performance. The effectiveness of the proposed approach is first demonstrated by comparing the MIO classification results obtained for three relating UCI datasets: (1) the original dataset; (2) a dataset with a large amount of inaccurate instances; and (3) an FVM-index filtered dataset extracted from the original dataset using a statistical approach. Then, the validity of the proposed approach is illustrated by using an Augmented Reality product design and a hospital related datasets. The results confirm that the proposed FVM-index method provides a good classification performance even in the presence of inaccuracy and uncertainty. As a result, it provides a robust approach for the extraction of reliable decision-making rules. [ABSTRACT FROM AUTHOR]

We propose an approach to the timing of markdowns in a continuous-time finite-horizon setting that does not require the precise knowledge of the underlying probabilities, instead relying on range forecasts for the arrival rates of the demand processes, and that captures the degree of the manager's risk aversion through intuitive budget of uncertainty functions. These budget functions bound the cumulative deviation of the arrival rates from their nominal values over the lengths of time for which a product is offered at a given price. A key issue is that using lengths of time as decision variables introduces non-convexities when budget functions are concave. In the single-product case, we describe a tractable and intuitive framework to incorporate uncertainty on customers' arrival rates, formulate the resulting robust optimization model, describe an efficient procedure to compute the optimal sale times, and provide theoretical insights. We then describe how to use the solution of the static robust optimization model to implement a dynamic markdown policy. We also extend the robust optimization approach to multiple products and suggest the idea of constraint aggregation to preserve performance for this type of problem structure. [ABSTRACT FROM AUTHOR]

This paper presents a method which combines the bilinear control technique with a set-point reset technique to control the space temperature when its cooling is provided by a GSHP equipped with an on/off capacity control, aiming to (i) improve the robustness of the space temperature control by taking account of load uncertainty; and (ii) to reduce the frequency of the GSHP on/off cycling by using the indoor space temperature set-point reset. When the GSHP is on, a smaller space temperature set point is used; while the GSHP is off, a larger set point is used. The proposed control was tested on a simulation platform, which consists of a ground source heat exchanger, a GSHP, an air-handling unit (AHU) and a middle-sized room. The test results show that the proposed control method is able to achieve a good control performance and has the potential to be applied to a real GSHP integrated air conditioning system to improve the robustness of space temperature control and simultaneously reduce the on/off frequency of the GSHP. [ABSTRACT FROM AUTHOR]

Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing anuncertainVFDTtree withclassifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty. [ABSTRACT FROM PUBLISHER]

As an example of one-class classification methods, support vector data description (SVDD) offers an opportunity to improve the performance of outlier detection and reduce the loss caused by outlier occurrence in many real-world applications. However, due to limited outliers, the SVDD model is built only by using the normal data. In this situation, SVDD may easily lead to over fitting when the normal data contain noise or uncertainty. This paper presents two types of new SVDD methods, named R-SVDD and ε NR-SVDD, which are constructed by introducing cutoff distance-based local density of each data sample and the ε -insensitive loss function with negative samples. We have demonstrated that the proposed methods can improve the robustness of SVDD for data with noise or uncertainty by extensive experiments on ten UCI datasets. The experimental results have shown that the proposed ε NR-SVDD is superior to other existing outlier detection methods in terms of the detection rate and the false alarm rate. Meanwhile, the proposed R-SVDD can also achieve a better outlier detection performance with only normal data. Finally, the proposed methods are successfully used to detect the image-based conveyor belt fault. [ABSTRACT FROM AUTHOR]

This study presents the design of a robust active disturbance rejection (ADR) controller in order to improve lowvoltage ride-through (LVRT) capability of wind farms connected with doubly fed induction generator (DFIG). The ADR controller is particularly effective in real-time estimation and mitigation of the total effect of various uncertainties against a wide range of parameter variations, model uncertainties and large disturbances. The performance evaluation of the designed controller is performed on an IEEE system under different test cases. The simulation results show that the proposed controller is robust against uncertainties in operating conditions and successfully improves the damping and voltage stability and thus the LVRT capability of DFIGs. [ABSTRACT FROM AUTHOR]

In this paper, we study a robust single-machine scheduling problem under four alternative optimization criteria: minimizing total completion time, minimizing total weighted completion time, minimizing maximum lateness, and minimizing the number of late jobs. We assume that job processing times are subject to uncertainty. Accordingly, we construct three alternative uncertainty sets, each of which defines job processing times that can simultaneously occur. The robust optimization framework assumes that, given a job schedule, a worst-case set of processing times will be realized from among those allowed by the uncertainty set under consideration. For each combination of objective function and uncertainty set, we first analyze the problem of identifying a set of worst-case processing times with respect to a fixed schedule, and then investigate the problem of selecting a schedule whose worst-case objective is minimal. [ABSTRACT FROM AUTHOR]

We first investigate the fundamental properties of the mechanical system related to the control design. Then, a new optimal robust control is proposed for mechanical systems with fuzzy uncertainty. Fuzzy set theory is used to describe the uncertainty in the mechanical system. The desirable system performance is deterministic (assuring the bottom line) as well as fuzzy (enhancing the cost consideration). The proposed control is deterministic and is not the usual if–then rule based. The resulting controlled system is uniformly bounded and uniformly ultimately bounded proved via the Lyapunov minimax approach. A performance index (the combined cost, which includes average fuzzy system performance and control effort) is proposed based on the fuzzy information. The optimal design problem associated with the control can then be solved by minimizing the performance index. The resulting control design is systematic and is able to guarantee the deterministic performance, as well as minimizing the cost. In the end, a mechanical system is chosen for demonstration. [ABSTRACT FROM PUBLISHER]

This paper provides a solution for the position tracking and attitude synchronization problem of the close proximity phase in spacecraft rendezvous and docking. The chaser spacecraft must be driven to a certain fixed position along the docking port direction of the target spacecraft, while the attitude of the two spacecraft must be synchronized for subsequent docking operations. The kinematics and dynamics for relative position and relative attitude are modeled considering dynamic coupling, parametric uncertainties and external disturbances. The relative motion model has a new form with a novel definition of the unknown parameters. An original robust adaptive control method is developed for the concerned problem, and a proof of the asymptotic stability is given for the six degrees of freedom closed-loop system. A numerical example is displayed in simulation to verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper analyzes the influence of urban development density on transit network design with stochastic demand by considering two types of services, rapid transit services, such as rail, and flexible services, such as dial-a-ride shuttles. Rapid transit services operate on fixed routes and dedicated lanes, and with fixed schedules, whereas dial-a-ride services can make use of the existing road network, hence are much more economical to implement. It is obvious that the urban development densities to financially sustain these two service types are different. This study integrates these two service networks into one multi-modal network and then determines the optimal combination of these two service types under user equilibrium (UE) flows for a given urban density. Then we investigate the minimum or critical urban density required to financially sustain the rapid transit line(s). The approach of robust optimization is used to address the stochastic demands as captured in a polyhedral uncertainty set, which is then reformulated by its dual problem and incorporated accordingly. The UE principle is represented by a set of variational inequality (VI) constraints. Eventually, the whole problem is linearized and formulated as a mixed-integer linear program. A cutting constraint algorithm is adopted to address the computational difficulty arising from the VI constraints. The paper studies the implications of three different population distribution patterns, two CBD locations, and produces the resultant sequences of adding more rapid transit services as the population density increases. [ABSTRACT FROM AUTHOR]

In this paper, we study the transit itinerary planning problem with incorporation of randomness that arises in transit vehicle arrival/departure and passenger transfer. We investigate two approaches to address the uncertainty: a minmax robust approach and an expectation-based probabilistic approach. We adapt a two-phase framework to mitigate computational challenges in large-scale planning problems. In phase I, we compute candidate route connections offline and store them into a database. Although expensive computation is required in phase I, it is typically performed only once over a period of time (e.g., half a year). Phase II takes place whenever a request is received, for which we query candidate route connections from the database, build a stochastic shortest-path model based on either approach listed above, and solve the model in real time. With phase I, computational requirement in phase II is substantially reduced so as to ensure real-time itinerary planning. To demonstrate the practical feasibility of our two-phase approach, we conduct extensive case studies and sensitivity analyses based on a large real-world transit network. [ABSTRACT FROM AUTHOR]

This paper studies the fixed-time composite robust tracking control problem of a class of marine surface vessel (MSV) systems with uncertainty items and external disturbances. First, the tracking error is restricted within the bounded range by the tan-type barrier Lyapunov function (TT-BLF). Second, a fixed-time disturbance observer (FTDO) is designed to offset the lumped disturbances caused by system uncertainty items and external disturbances. Then, relying on the characteristics of H ∞ control, the error is limited within the L 2 norm bounded by suppressing the interference of the observation error. Furthermore, considering the importance of saving network resources, an event-triggered controller based on relative thresholds is proposed, which guarantees excellent robustness while greatly reducing the communication bandwidth. Finally, it is shown by Lyapunov stability analysis that all error signals of the nonlinear system converge to a small region close to zero in fixed time under the guidance of the robust control law. The effectiveness of the proposed composite robust controller is verified by numerical simulation results. • A fixed-time observer is employed to offset the lumped disturbances caused by system uncertainty items and external disturbances. • An H∞ control strategy is proposed to improve robustness, which has L 2 gain less than or equal to given γ. • A composite robust controller is proposed to ensure the error signal converge to a small range near zero within a predetermined time and robustness. • A ETS based on relative threshold is developed to greatly reduce the communication bandwidth of the controller. [ABSTRACT FROM AUTHOR]

A robust approach to nondestructive test (NDT) design for material characterization and damage identification in solids and structures is presented and numerically evaluated. The generally applicable approach combines maximization of test sensitivity with minimization of test information redundancy, while simultaneously minimizing the effects of uncertain system parameters to determine optimal NDT parameters for robust nondestructive evaluation. In addition, to maintain reasonable computational expense while also allowing for general applicability, a stochastic collocation technique is presented for the quantification of uncertainty in the robust design metrics. The robust NDT design approach was tested through simulated case studies based on the characterization of localized variations in Young's modulus distributions in aluminum structural components utilizing steady-state dynamic surface excitation and localized measurements of displacement and compared with a purely deterministic NDT design approach. The robust design approach is shown to produce substantially different NDT designs than the analogous deterministic strategy. More importantly, the robust NDT designs are shown to provide significant improvements in the ability to accurately nondestructively evaluate structural properties for the cases considered, when there is significant uncertainty in system parameters and/or aspects of the NDT implementation. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes an uncertainty compensator to design a novel robust control for mobile robots with dynamic and kinematic uncertainties. A novel gradient-based adaptive fuzzy estimator is developed to compensate uncertainties with minimum required feedback signals. As a novelty, the proposed approach uses the tracking error and its first time derivative to form the estimation error of uncertainty, and guarantees that both the estimation error and tracking error converge asymmetrically to ignorable value. Advantages of the proposed robust control are simplicity in design, robustness against uncertainties, guaranteed stability, and good control performance. The control approach is verified by stability analysis. Simulation results and experimental results illustrate the effectiveness of the proposed control. Experimental evaluation of the proposed controller is expressed for two different low-cost nonholonomic wheeled mobile robots. The proposed control design is compared with an adaptive control approach to confirm the superiority of the proposed approach in terms of precision, simplicity of design, and computations. [ABSTRACT FROM AUTHOR]

This paper is concerned with the robust distributed H∞ filtering problem for nonlinear systems subject to sensor saturations and fractional parameter uncertainties. A sufficient condition is derived for the filtering error system to reach the required H∞ performance in terms of recursive linear matrix inequality method. An iterative algorithm is then proposed to obtain the filter parameters recursively by solving the corresponding linear matrix inequality. A numerical example is presented to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]