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Many transportation systems for routing flows between several origin-destination pairs of demand nodes have been widely designed as hub-and-spoke networks. To improve the provided service level of these networks, service time requirements are here considered during modeling, giving rise to a multiple allocation incomplete hub location problem with service time requirements. The problem consists of designing a hub and spoke network by locating hubs, establishing inter-hub arcs, and routing origin-destination demand flows at minimal cost while meeting some service time requirements. As travel times are usually uncertain for most real cases, the problem is approached via a binary linear programming robust optimization model, which is solved by two specialized Benders decomposition algorithms. The devised Benders decomposition framework outperforms a general purpose optimization solver on solving benchmark instances of the hub location literature. The achieved results also show how the probability of violating the travel time requirements decreases with the prescribed protection level, at the expense of the higher costs of the optimal solution for the robust optimization model. [ABSTRACT FROM AUTHOR]

In this paper the following selection problem is discussed. A set of n items is given and we wish to choose a subset of exactly p items of the minimum total cost. This problem is a special case of 0–1 knapsack in which all the item weights are equal to 1. Its deterministic version has an O ( n ) -time algorithm, which consists in choosing p items of the smallest costs. In this paper it is assumed that the item costs are uncertain. Two robust models, namely two-stage and recoverable ones, under discrete and interval uncertainty representations, are discussed. Several positive and negative complexity results for both of them are provided. [ABSTRACT FROM AUTHOR]

The maximum capability of feedback control for discrete-time systems under a nonparametric Lipschitz uncertainty was first established in Xie and Guo (2000) for the simplest dynamical control system. It was shown that the necessary and sufficient condition for the adaptive stabilizability is of the form L < 3 2 + 2 , where L is the Lipschitz constant of the uncertainty. This result was extended in Huang and Guo (2012) to a basic class of scalar discrete-time minimum-phase control systems under an additional parametric uncertainty, and the adaptive stabilization was achieved with the use of an impracticable infinite memory feedback based on a brute-force search over a sufficiently fine grid in the prior set of unknown parameters. In the present paper, the adaptive stabilization problem is considered for a subclass of parameter-affine minimum-phase systems. The solution of the problem is based on new recurrent objective inequalities enabling to estimate and validate the closed loop system online and to formulate a simple projection-type parameter estimation algorithm instead of the brute-force search used in Huang and Guo (2012). The computational tractability of the proposed finite-memory stabilizing feedback is illustrated via simulations. [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]

Abstract: This paper presents a new method for implementation and realization of an optimal robust control algorithm in the real-time hardware-in-the-loop simulation environment for a mathematical model of the dynamics of the BULLIT micro-aircraft, with consideration of non-linearity, uncertainty, and non-stationarity of its parameters. The robust optimal control method, -Synthesis, applied to the autonomous flight dynamics control system of the unmanned aerial vehicle (UAV) meets desired control performances. The serial connection between the Gumstix micro-computer and the Kestrel autopilot extends the ability to implement high order robust controllers. The code of the control algorithm implemented (in the C++ language) in the memory of a Gumstix Verdex Pro single-chip micro-computer enables optimization of the threads-based approach. The hardware-in-the-loop (HIL) simulation mode was implemented in the Kestrel autopilot inner loop, and simulations of all stages of flight were performed in real-time using the actual model of the aircraft and autopilot. Finally, HIL simulations and tests were conducted in order to verify the developed control algorithm. [Copyright &y& Elsevier]

Abstract: We solve a linear robust problem with mixed-integer first-stage variables and continuous second stage variables. We consider column wise uncertainty. We first focus on a problem with right hand-side uncertainty which satisfies a “full recourse property” and a specific definition of the uncertainty. We propose a solution based on a generation constraint algorithm. Then we give some generalizations of the approach: for left-hand side uncertainty and for uncertainty sets defined by a polytope. Finally we solve the problem when the “full recourse property” is not satisfied. [Copyright &y& Elsevier]

Abstract: In this paper, a new method is introduced to enhance feedback robustness against communication gain uncertainties. The method employs a fundamental property in stochastic differential equations to add a scaled stochastic dither under which tolerable gain uncertainties can be much enlarged, beyond the traditional deterministic optimal gain margin. Algorithms, stability, convergence, and robustness are presented for first-order systems. Extension to higher-dimensional systems is further discussed. Simulation results are used to illustrate the merits of this methodology. [Copyright &y& Elsevier]

Abstract: A robust circle criterion observer is designed and applied to neural mass models. At present, no existing circle criterion observers apply to the considered models, i.e. the required linear matrix inequality is infeasible. Therefore, we generalise available results to derive a suitable estimation algorithm. Additionally, the design also takes into account input uncertainty and measurement noise. We show how to apply the observer to estimate the mean membrane potential of neuronal populations of a popular single cortical column model from the literature. [Copyright &y& Elsevier]

Abstract: In this paper we propose a new methodology to synthesize and implement robust monotonically convergent ILC for lifted systems, with the computational complexity that is linear in the trial length. Starting from the model uncertainty of the local sample to sample LTI or LTV models, and using the randomized algorithm, we compute the bound on the model uncertainty of the ILC system representation in the trial domain (lifted ILC). Based on this computed uncertainty bound, we design weighting matrices of the Norm Optimal ILC, such that the robust monotonic convergence condition is satisfied. Since we compute the uncertainty bound, rather than assuming its value in the trial domain, we reduce the conservatism of the robust design. The linear computational complexity of the algorithms for computation of the uncertainty bound and implementation of the Norm Optimal ILC law, is achieved through exploiting the sequentially semi-separable structure of the lifted system matrices. Therefore the framework proposed in this paper is especially suitable for the LTI and LTV uncertain systems with a large number of samples in the trial. We have performed numerical experiments to demonstrate the robustness and linear computational complexity of the proposed method. [Copyright &y& Elsevier]

Abstract: There has been a challenging work for using conventional techniques to model and control pneumatic artificial muscle (PM) due to poor knowledge and uncertainty of the process and/or complexity of the resulting mathematical model. Trying to deal with these problems, this study proposes a novel framework—Echo State Network (ESN) as a basis to implement the tasks in the PM''s modeling and control. To describe the system dynamics and the external disturbance changes with time, the online ESN adaptation scheme is presented based on the recursive least squares (RLS) algorithm. Both simulation and experimental results show that the proposed procedure has better dynamic performance and strong robustness over the other typical/classical approaches. [Copyright &y& Elsevier]

Abstract: Robust controller synthesis of Multi-Input–Multi-Output (MIMO) systems is of great practical interest and their automation is a key concern in control system design. The synthesis problem consists of obtaining a controller that ensures stability and meets a given set of performance specifications, in spite of the disturbance and model uncertainties. In addition to perform the above tasks, a MIMO controller also has to perform the difficult task of minimizing the interaction between the various control loops. Unlike existing manual or convex optimization based Quantitative Feedback Theory (QFT) design approaches, the proposed method gives a controller which meets all performance requirements in QFT, without going through the conservative and sequential design stages for each of the multivariable sub-systems. In this paper, a new, simple, and reliable automated MIMO QFT controllers design methodology is proposed. A fixed structure MIMO QFT controller has been synthesized by solving QFT quadratic inequalities of robust stability and tracking specifications. The quadratic inequalities (constraints) are posed as Interval Constraint Satisfaction Problem (ICSP). The constraints are solved by constraint solver — RealPaver. The main feature of this method is that the algorithm finds all the solutions to within the user-specified accuracy. The designed MIMO QFT controllers are tested on the experimental setup designed by Educational Control Product (ECP) Magnetic Levitation Setup ECP 730. From the experimental results presented, it is observed that, the designed controller satisfies the desired performance specifications. It is also observed that, the interactions between the loops are within the specified limits. The robustness of the designed controllers are verified by putting extra weights on the magnets. [Copyright &y& Elsevier]

Abstract: In this paper, we investigate the problem of synthesizing performance weights and a controller that maximize the level of robust performance for a plant subject to both complex and real uncertainties. In particular, in this work the formulation proposed in and is manipulated in order to include the possibility to handle both real and complex uncertainties. Additionally, we introduce a novel solution algorithm that presents performance and computational advantages with respect to those described in and . [Copyright &y& Elsevier]

Abstract: In this paper, we present a new robust iterative learning control (ILC) design for a class of linear systems in the presence of time-varying parametric uncertainties and additive input/output disturbances. The system model is described by the Markov matrix as an affine function of parametric uncertainties. The robust ILC design is formulated as a min–max problem using a quadratic performance criterion subject to constraints of the control input update. Then, we propose a novel methodology to find a suboptimal solution of the min–max optimization problem. First, we derive an upper bound of the worst-case performance. As a result, the min–max problem is relaxed to become a minimization problem in the form of a quadratic program. Next, the robust ILC design is cast into a convex optimization over linear matrix inequalities (LMIs) which can be easily solved using off-the-shelf optimization solvers. The convergences of the control input and the error are proved. Finally, the robust ILC algorithm is applied to a physical model of a flexible link. The simulation results reveal the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents a comprehensive review of Uncertainty-Based Multidisciplinary Design Optimization (UMDO) theory and the state of the art in UMDO methods for aerospace vehicles. UMDO has been widely acknowledged as an advanced methodology to address competing objectives of aerospace vehicle design, such as performance, cost, reliability and robustness. However the major challenges of UMDO, namely the computational complexity and organizational complexity caused by both time-consuming disciplinary analysis models and UMDO algorithms, still greatly hamper its application in aerospace engineering. In recent years there is a surge of research in this field aiming at solving these problems. The purpose of this paper is to review these existing approaches systematically, highlight research challenges and opportunities, and help guide future efforts. Firstly, the UMDO theory preliminaries are introduced to clarify the basic UMDO concepts and mathematical formulations, as well as provide a panoramic view of the general UMDO solving process. Then following the UMDO solving process, research progress of each key step is separately surveyed and discussed, specifically including uncertainty modeling, uncertainty propagation and analysis, optimization under uncertainty, and UMDO procedure. Finally some conclusions are given, and future research trends and prospects are discussed. [Copyright &y& Elsevier]

Abstract: This paper presents a general framework for synchronized multiple spacecraft rotations via consensus-based virtual structure. In this framework, attitude control systems for formation spacecrafts and virtual structure are designed separately. Both parametric uncertainty and external disturbance are taken into account. A time-varying sliding mode control (TVSMC) algorithm is designed to improve the robustness of the actual attitude control system. As for the virtual attitude control system, a behavioral consensus algorithm is presented to accomplish the attitude maneuver of the entire formation and guarantee a consistent attitude among the local virtual structure counterparts during the attitude maneuver. A multiple virtual sub-structures (MVSSs) system is introduced to enhance current virtual structure scheme when large amounts of spacecrafts are involved in the formation. The attitude of spacecraft is represented by modified Rodrigues parameter (MRP) for its non-redundancy. Finally, a numerical simulation with three synchronization situations is employed to illustrate the effectiveness of the proposed strategy. [Copyright &y& Elsevier]

Abstract: The use of feature enhancement techniques to obtain estimates of the clean parameters is a common approach for robust automatic speech recognition (ASR). However, the decoding algorithm typically ignores how accurate these estimates are. Uncertainty decoding methods incorporate this type of information. In this paper, we develop a formulation of the uncertainty decoding paradigm for Frequency Filtered (FF) parameters using spectral subtraction as a feature enhancement method. Additionally, we show that the uncertainty decoding method for FF parameters admits a simple interpretation as a spectral weighting method that assigns more importance to the most reliable spectral components. Furthermore, we suggest combining this method with SSBD-HMM (Spectral Subtraction and Bounded Distance HMM), one recently proposed technique that is able to compensate for the effects of features that are highly contaminated (outliers). This combination pursues two objectives: to improve the results achieved by uncertainty decoding methods and to determine which part of the improvements is due to compensating for the effects of outliers and which part is due to compensating for other less deteriorated features. [Copyright &y& Elsevier]

Abstract: In recent years, there has been a growing interest in developing statistical learning methods to provide approximate solutions to “difficult” control problems. In particular, randomized algorithms have become a very popular tool used for stability and performance analysis as well as for design of control systems. However, as randomized algorithms provide an efficient solution procedure to the “intractable” problems, stochastic methods bring closer to understanding the properties of the real systems. The topic of this paper is the use of stochastic methods in order to solve the problem of control robustness: the case of parametric stochastic uncertainty is considered. Necessary concepts regarding stochastic control theory and stochastic differential equations are introduced. Then a convergence analysis is provided by means of the Chernoff bounds, which guarantees robustness in mean and in probability. As an illustration, the robustness of control performances of example control systems is computed. [Copyright &y& Elsevier]

Abstract: This paper develops a two-loop robust control strategy for systems associated with uncertainty. The inner-loop is designed offline for robust stability. The outer-loop is used online to improve the control performance based on predicted information, taking uncertainties and constraints into account. The structure of the two-loop robust controller is illustrated. A practical control algorithm is developed for the systems which can be described using an uncertain first-order plus time delay model. The benefits of the two-loop robust control strategy are illustrated by presenting the validation results and comparing with an anti-windup PI controller. [Copyright &y& Elsevier]