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We explore how to overcome phase damping decoherence when imperfect projective measurements are available. It is demonstrated that a “relaxed” control goal may be achieved by combining simple open-loop coherent control with periodic imperfect projective measurements. Inspired by the idea of soft optimization, we propose to control the state of a qubit staying near a reference pure state with high probability for a sufficiently long time. This “relaxed” control goal is expressed in terms of three-parameter performance indexes, and it is in remarked contrast to the “hard” requirement that the state of the controlled qubit always stays at the reference pure state. We not only establish necessary conditions for relaxed robust problems, but also present sufficient conditions for the existence of a solution to the relaxed robust problems. With the help of main results, one can estimate how the maximal total time depends on the target state and the perturbation bounds. Furthermore, it is demonstrated that imperfect realization of projective measurements will worsen the three-parameter performance indexes. [ABSTRACT FROM AUTHOR]

We describe an effective and efficient strategy building steganography detector for patch synthesis based steganography, one case of which is reversible texture synthesis based steganography method proposed by Wu et al. (2015). By exploiting the observation that steganography destroys optimization of matching extent between the synthetic patch and optimal candidate patch, we reconstruct the two patches from an overlapped region to extract the existence of optimality, which are distinct between cover and stego images, to form features. Support vector machine (SVM) is implemented for classification. Meanwhile, a variant of Wu et al.’s steganographic method is proposed with reinforced security, by padding redundant regions carrying no message around the periphery of the synthesized image and generating additional candidate patches to increase capacity. Experiments demonstrate that the modified algorithm offers not only better resistance against the state-of-the-art steganalysis methods and steganalytic attack we developed, but also a larger embedding capacity. [ABSTRACT FROM AUTHOR]

We consider the problem of constructing multi-connected control of a robotic platform designed to protect technological objects and human operators from low-frequency influences on part of the moving base. The platform includes six drive mechanisms with stepper motors. The problem is solved by the methods of the modern theory of robust stabilization and optimal control based on H∞-optimization in the state space. We construct a mathematical model of the multidimensional system, taking into account the characteristics of electromechanical drives and using signals of feedback sensors as state variables. We give an example of synthesizing a multidimensional optimal stabilizing controller in the form of state feedback for a system with disturbances bounded in L2-norm. We define the feedback control structure and obtain the matrix of feedback coefficients. We also show the results of mathematical modeling. [ABSTRACT FROM AUTHOR]

In the present paper, a robust approach is used to locate hub facilities considering network risks. An additional objective function, minimax regret, is added to the classical objective function in the hub location problem. In the proposed model, risk factors such as availability, security, delay time, environmental guidelines and regional air pollution are considered using triangular fuzzy-stochastic numbers. Then an equivalent crisp single objective model is proposed and solved by the Benders decomposition method. Finally, the results of both Benders decomposition and commercial optimization software are compared for different instances. Numerical instances were developed based on the well-known Civil Aeronautics Board (CAB) data set, considering different levels of uncertainty in parameters. The results show that the proposed model is capable of selecting nodes as sustainable hubs. Also, the results confirm that using Benders decomposition is more efficient than using classical solution methods for large-scale problems. [ABSTRACT FROM AUTHOR]

Sparse domain thresholding filters operating in a sparse domain are highly effective in removing Gaussian random noise under Gaussian distribution assumption. Erratic noise, which designates non-Gaussian noise that consists of large isolated events with known or unknown distribution, also needs to be explicitly taken into account. However, conventional sparse domain thresholding filters based on the least-squares (LS) criterion are severely sensitive to data with high-amplitude and non-Gaussian noise, i.e., the erratic noise, which makes the suppression of this type of noise extremely challenging. In this paper, we present a robust sparsity-promoting denoising model, in which the LS criterion is replaced by the Huber criterion to weaken the effects of erratic noise. The random and erratic noise is distinguished by using a data-adaptive parameter in the presented method, where random noise is described by mean square, while the erratic noise is downweighted through a damped weight. Different from conventional sparse domain thresholding filters, definition of the misfit between noisy data and recovered signal via the Huber criterion results in a nonlinear optimization problem. With the help of theoretical pseudoseismic data, an iterative robust sparsity-promoting filter is proposed to transform the nonlinear optimization problem into a linear LS problem through an iterative procedure. The main advantage of this transformation is that the nonlinear denoising filter can be solved by conventional LS solvers. Tests with several data sets demonstrate that the proposed denoising filter can successfully attenuate the erratic noise without damaging useful signal when compared with conventional denoising approaches based on the LS criterion. [ABSTRACT FROM AUTHOR]

Variations in the concentrations of biomolecular species in vivo are inevitable, but regulation systems will act to maintain concentrations of certain species within proper levels. Such a ubiquitous trait in biological systems is the so-called concentration robustness. In this work, we study the concentration robustness of glycerol metabolism system, in which some mechanistic details are not clearly known and the true metabolic system need to be identified from all possible ones. We give a quantitative index to measure the concentration robustness of the considered system, which is characterized as the maximum relative variation in certain entries of steady state caused by the perturbations of operating parameters. Based on the proposed robustness index, a minimax dynamic optimization problem is developed for identifying the kinetic parameters as well as the unknown metabolic mechanisms. A scheme based on Monte-Carlo method is proposed to evaluate the robustness index approximately and convergence result is obtained. An algorithm is constructed to solve the dynamic optimization problem and numerical results are presented to show that the proposed robustness index could measure concentration robustness of the considered system properly. [ABSTRACT FROM AUTHOR]

This paper deals with robust decentralised feedback control for large-scale networked control systems (NCSs). A NCS model is extended to a class of large-scale system. Less conservative sufficient conditions are formulated in the form of a nonconvex matrix inequality, of which a feasible solution can be obtained by solving an optimisation problem in terms of linear matrix inequalities. The sufficient conditions are constructed by a set of new Lyapunov-Krasovskii functionals, which are based mainly on the lower and upper bounds of delay and packet loss. An explicit expression for the desired robust decentralised state feedback control law is also given. In order to verify the proposed method, it is applied to a numerical example and the load frequency problem in a multi-area power system. The simulation results show good performance and more robust stability against digital network communications delays, packet loss and external disturbances. [ABSTRACT FROM AUTHOR]

In recent years, there have been several L1-norm-based linear projection methods and max–min-based dimensionality reduction methods, which show robustness to outliers and noises and show large margin for discrimination. In this paper, we propose L1-norm-based max–min large margin (MLM-L1) for linear projection-based dimensionality reduction. It makes use of the robustness of L1-norm to outliers and noises and the max–min idea for large margin. A non-greedy iterative algorithm (NMLM-L1) is proposed to solve the optimization problem of the proposed MLM-L1. Experiments on several face image databases show that the proposed method has better classification performance than its closely related methods. [ABSTRACT FROM AUTHOR]

Currently, in high-performance applications, the vector control (VC) scheme of induction motor (IM) is widely employed in industry. The VC scheme has significant features of decoupling torque and flux; also, its hardware implementation is easier. Conventionally, PID control schemes are frequently used for variable speed operation. However, the performance of the VC scheme is limited over a wide range of speed operation because of de-tuning caused by parameter uncertainties. To address the aforementioned challenging problem, adaptive and robust control strategies are mostly implemented. This paper presents various novel, adaptive, and robust control strategies, namely (a) fuzzy logic controller (FLC) based on Levenberg-Marquardt algorithm (LMA), (b) FLC based on steepest descent algorithm (SDA), (c) FLC based on Newton algorithm (NA), and (d) FLC based on Gauss-Newton algorithm (GNA) for the indirect vector control (IVC) three-phase IM. The focal motive is to accomplish fast dynamic response with fault-tolerant capability, load disturbance rejection qualities, insensitivity to the parameter uncertainties, robustness to speed variation, and to acquire maximum efficiency as well as torque. The d-q modeling of the IVC IM in the synchronous reference frame and space vector pulse width modulation (SVPWM) employed in inverter are designed in MATLAB/Simulink. Our work also presents critical, analytical, and comparative assessment of the proposed controllers with traditional tuned PI control strategy for the electrical faults perturbation, load disturbances, speed variations, and parameter uncertainties. Furthermore, the simulation results of the above-mentioned designed control strategies validated robust, smooth, and faster response with permissible overshoot, undershoot, settling time, and rise time for the IVC IM drive system, compared to prior works. [ABSTRACT FROM AUTHOR]

In this paper, we present a pattern development method for soft product design. We utilize a surface fattening method based on a mass-spring model to create 2D patterns unfolding from a three-dimensional (3D) model. Multilevel meshes are proposed to expedite the flattening process, and a boundary optimization method is employed to guarantee 2D patterns can be sewn well. We apply the proposed method to the design of real soft products. Experimental results show that it can deal with complex surfaces efficiently and robustly, and manufactured products are satisfactory. [ABSTRACT FROM AUTHOR]

Interdependent networks, where two networks depend on each other, are becoming more and more significant in modern systems. From previous work, it can be concluded that interdependent networks are more vulnerable than a single network. The robustness in interdependent networks deserves special attention. In this paper, we propose a metric of robustness from a new perspective—the balance. First, we define the balance-coefficient of the interdependent system. Based on precise analysis and derivation, we prove some significant theories and provide an efficient algorithm to compute the balance-coefficient. Finally, we propose an optimal solution to reduce the balance-coefficient to enhance the robustness of the given system. Comprehensive experiments confirm the efficiency of our algorithms. [ABSTRACT FROM AUTHOR]

This study presents model predictive formation control based on an eccentricity/inclination vector separation strategy. Alternative collision avoidance can be accomplished by using eccentricity/inclination vectors and adding a simple goal function term for optimization process. Real-time control is also achievable with model predictive controller based on convex formulation. Constraint-tightening approach is address as well improve robustness of the controller, and simulation results are presented to verify performance enhancement for the proposed approach. [ABSTRACT FROM AUTHOR]

We present the new quantum chemistry program S erenity. It implements a wide variety of functionalities with a focus on subsystem methodology. The modular code structure in combination with publicly available external tools and particular design concepts ensures extensibility and robustness with a focus on the needs of a subsystem program. Several important features of the program are exemplified with sample calculations with subsystem density‐functional theory, potential reconstruction techniques, a projection‐based embedding approach and combinations thereof with geometry optimization, semi‐numerical frequency calculations and linear‐response time‐dependent density‐functional theory. © 2018 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In this work, the exponential weighted square-of-error functions are exploited for controller design. It is reported that the exponential weight in square-of-error objective function is related to the maximum sensitivity of the process. Therefore, it is possible to control the maximum sensitivity of the process without measuring the frequency response. Moreover, the existing integral-square-error- and integral-time-square-error-based proportional-integral-derivative (PID) tuning rule is modified with the proposed exponential weighted square-of-error functions that allow the user to further tune the maximum sensitivity of the process. The proposed theory is corroborated with the help of a gain optimisation example by the traditional minimisation approach and a PID controller design example by the proposed tuning rule (for the stable/unstable process) and stochastic optimisation approach. [ABSTRACT FROM AUTHOR]

Robustness plays a critical role in the controllability of complex networks to withstand failures and perturbations. Recent advances in the edge controllability show that the interaction strength among edges plays a more important role than network structure. Therefore, we focus on the effect of interaction strength on the robustness of edge controllability. Using three categories of all edges to quantify the robustness, we develop a universal framework to evaluate and analyze the robustness in complex networks with arbitrary structures and interaction strengths. Applying our framework to a large number of model and real-world networks, we find that the interaction strength is a dominant factor for the robustness in undirected networks. Meanwhile, the strongest robustness and the optimal edge controllability in undirected networks can be achieved simultaneously. Different from the case of undirected networks, the robustness in directed networks is determined jointly by the interaction strength and the network’s degree distribution. Moreover, a stronger robustness is usually associated with a larger number of driver nodes required to maintain full control in directed networks. This prompts us to provide an optimization method by adjusting the interaction strength to optimize the robustness of edge controllability. [ABSTRACT FROM AUTHOR]

Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distributed mobility robustness optimization algorithm to minimize handover failures due to radio link failures by adjusting time-to-trigger and offset parameters. According to the reason for failure, the algorithm classifies handover failure into three categories (too late, too early, and wrong cell), and simultaneously optimizes three handover parameters according to the dominant failure. Moreover, the algorithm considers handover failures to each neighboring cell and adjusts handover parameters individually. Via simulation, we show how the proposed algorithm adaptively optimizes the parameters and outperforms previous algorithms in various mobile environments. [ABSTRACT FROM PUBLISHER]

The problem of H∞ robust exponential stability and static output feedback stabilization of uncertain singular time-delay systems with saturating actuators are investigated. First, the delay-dependent condition is given to guarantee that the singular time-delay systems are not only exponential admissible, but also satisfies H∞ performance γ. Moreover, the estimation of stability region and the design method of H∞ static output feedback controller are given by solving a convex optimization problem. Some simulation examples are provided to demonstrate the merit of the proposed design methods. [ABSTRACT FROM AUTHOR]

In this paper we consider ambiguous stochastic constraints under partial information consisting of means and dispersion measures of the underlying random parameters. Whereas the past literature used the variance as the dispersion measure, here we use the mean absolute deviation from the mean (MAD). This makes it possible to use the 1972 result of Ben-Tal and Hochman (BH) in which tight upper and lower bounds on the expectation of a convex function of a random variable are given. First, we use these results to treat ambiguous expected feasibility constraints to obtain exact reformulations for both functions that are convex and concave in the components of the random variable. This approach requires, however, the independence of the random variables and, moreover, may lead to an exponential number of terms in the resulting robust counterparts.We then show how upper bounds can be constructed that alleviate the independence restriction, and require only a linear number of terms, by exploiting models in which random variables are linearly aggregated. Moreover, using the BH bounds we derive three new safe tractable approximations of chance constraints of increasing computational complexity and quality. In a numerical study, we demonstrate the efficiency of our methods in solving stochastic optimization problems under mean-MAD ambiguity. [ABSTRACT FROM AUTHOR]

The following game is played on a weighted graph: Alice selects a matching M and Bob selects a number k. Alice's payoff is the ratio of the weight of the k heaviest edges of M to the maximum weight of a matching of size at most k. If M guarantees a payoff of at least then it is called α-robust. In 2002, Hassin and Rubinstein gave an algorithm that returns a 1/ √2-robust matching, which is best possible. We show that Alice can improve her payoff to 1/ln(4) by playing a randomized strategy. This result extends to a very general class of independence systems that includes matroid intersection, b-matchings, and strong 2-exchange systems. It also implies an improved approximation factor for a stochastic optimization variant known as the maximum priority matching problem and translates to an asymptotic robustness guarantee for deterministic matchings, in which Bob can only select numbers larger than a given constant. Moreover, we give a new LP-based proof of Hassin and Rubinstein's bound. [ABSTRACT FROM AUTHOR]

An autodisturbance-rejection control (ADRC) of an induction motor based on an ant colony optimization (ACO) algorithm is proposed in this paper, in order to realize the precise decoupling of the induction motor and the disturbance compensation. A novel control method employs ACO as an automatic tune mechanism for an ADRC controller. According to the feedback information from the induction motor, an optimal solution can be achieved via the optimization mechanism and self-learning ability of ACO after the iterative calculation; therefore, the reliance of the ADRC controller on parameters can be reduced. The simulation and experimental results indicate that the robustness of the proposed optimal design method for ADRC is better than that of the conventional ADRC when the disturbances occur and that the method is feasible and effective. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles are famous for their wide range of applications. In D3 (Dirty-Dull-Dangerous) UAV applications flight conditions may vary on large scale. External disturbances like atmospheric turbulences and gusts may be subjected to UAV, and as a result, UAV flight mission might be conducted with high level of the degradation of the accuracy. Sensor noises are also present, and theirs negligence might lead to improper dynamic performances of the closed loop control systems. Uncertainties of the control systems being structured or unstructured may tend the closed loop control system to stability bounds. In worst case, uncertainties may destabilize closed loop control systems. The purpose of the author is to present a robust controller design method called H2-optimal design ensuring stability of the closed loop control systems with simultaneous dynamic performances predefined for the closed loop control system. [ABSTRACT FROM AUTHOR]

Given a classification task, an approach to improve accuracy relies on the use of abstaining classifiers. These classifiers are trained to reject observations for which predicted values are not reliable enough: these rejected observations belong to an abstaining area in the feature space. Two equivalent methods exist to theoretically compute the optimal abstaining area for a given classification problem. The first one is based on the posterior probability computed by the model and the other is based on the derivative of the ROC function of the model. Although the second method has proved to give the best results, in small-sample settings such as the one found in omics data, the estimation of posterior probabilities and derivative of ROC curve are both lacking of precision leading to far from optimal abstaining areas. As a consequence none of the two methods bring the expected improvements in accuracy. We propose five alternative algorithms to compute the abstaining area adapted to small-sample problems. The idea of these algorithms is to compute an accurate and robust estimation of the ROC curve and its derivatives. These estimation are mainly based on the assumption that the distribution of the output of the classifier for each class is normal or mixture of normal distributions. These distributions are estimated by a kernel density estimator or Bayesian semiparametric estimator. Another method works on the approximation of the convex hull of the ROC curve. Once the derivative of the ROC curve are estimated, the optimal abstaining area can be directly computed. The performance of our algorithms are directly related to their capacity to compute an accurate estimation of the ROC curve. A sensitivity analysis of our methods to the dataset size and rejection cost has been done on a set of experiments. We show that our methods improve the performances of the abstaining classifiers on several real datasets and for different learning algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an efficient and reliable evolutionary-based approach to solve palletization problem. The proposed approach employs particle swarm optimization (PSO) algorithm for minimization of cycle time of palletizing robot i.e. IRB-1600. The operator of algorithm are reviewed, focusing on how each affects search behaviour in the problem space. This paper first analyzes the impact of the parameters i.e. population size and maximum run on the performance of the particle swarm optimizer. Numerical results show that the different algorithm perform optimally for the tested instances in a reasonable computational time. The proposed approach results have been compared to those that reported in the literature recently. The results are promising and show the effectiveness and robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

We treat uncertain linear programming problems by utilizing the notion of weighted analytic centers and notions from the area of multi-criteria decision making. After introducing our approach, we develop interactive cutting-plane algorithms for robust optimization, based on concave and quasi-concave utility functions. In addition to practical advantages, due to the flexibility of our approach, we prove that under a theoretical framework proposed by Bertsimas and Sim (Oper Res 52:35-53, 2004), which establishes the existence of certain convex formulation of robust optimization problems, the robust optimal solutions generated by our algorithms are at least as desirable to the decision maker as any solution generated by many other robust optimization algorithms in the theoretical framework. We present some probabilistic bounds for feasibility of robust solutions and evaluate our approach by means of computational experiments. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study characterizations of minimal and approximate minimal solutions for a unified vector optimization problem in a Hausdorff real topological vector space. These characterizations have been obtained via scalarizations which are based on general order representing and order preserving properties. A nonlinear scalarization based on Gerstewitz function is shown to be a particular case of the proposed scalarizations. Furthermore, in the setting of normed space, characterizations are given for minimal solutions by using scalarization function based on the oriented distance function. Finally, under appropriate assumptions it is shown that this function satisfies order representing and order preserving properties. [ABSTRACT FROM AUTHOR]

Summary: This paper focuses on spatially distributed control systems where the controller sensing and actuation topology is inherited from that of the plant. Specifically, this paper considers distributed systems composed of discrete‐time linear parameter‐varying subsystems interconnected over general graph structures. These distributed systems are subject to a communication latency of one sampling period, where the information sent by a subsystem at the current time step is received by the target subsystem at the next time step. Analysis and synthesis conditions are derived for control design in this setting using a parameter‐dependent Lyapunov approach with the ℓ2‐induced norm as the performance measure. Fast and easy‐to‐implement algorithms are also provided for constructing the controller in real time. The techniques developed are finally applied to a leader‐follower formation problem with intermittent communications. [ABSTRACT FROM AUTHOR]

Summary: In this work, we study the mixed H 2 / H ∞ control for Markov jump linear systems with hidden Markov parameters. The hidden Markov process is denoted by ( θ ( k ) , θ ^ ( k ) ), where the nonobservable component θ(k) represents the mode of operation of the system, whereas θ ^ ( k ) represents the observable component provided by a detector. The goal is to obtain design techniques for mixed H 2 / H ∞ control problems, with the controllers depending only on the estimate θ ^ ( k ), for problems formulated in 3 different forms: (i) minimizing an upper bound on the H 2 norm subject to a given restriction on the H ∞ norm; (ii) minimizing an upper bound on the H ∞ norm, while limiting the H 2 norm; and (iii) minimizing a weighted combination of upper bounds of both the H 2 and H ∞ norms. We propose also new conditions for synthesizing robust controllers under parametric uncertainty in the detector probabilities and in the transition probabilities. The so‐called cluster case for the mixed H 2 / H ∞ control problem is also analyzed under the detector approach. The results are illustrated by means of 2 numerical examples. [ABSTRACT FROM AUTHOR]

Summary: This paper aims at developing a robust observer–based estimated state feedback control design method for an uncertain dynamical system that can be represented as a linear time‐invariant system connected with an integral quadratic constraint–type nonlinear uncertainty. Traditionally, in existing design methodologies, a convex semidefinite constraint is obtained at the cost of conservatism and unrealistic assumptions. This paper avoids such assumptions and formulates, the design of the robust observer state feedback controller as the feasibility problem of a bilinear matrix inequality (BMI) constraint. Unfortunately, the search for a feasible solution of a BMI constraint is an NP‐hard problem in general. The applicability of a linearization method, such as the variable change method and the congruence transformation, depends on the specific structure of the problem at hand and cannot be generalized. This paper transforms the feasibility analysis of the BMI constraint into an eigenvalue problem and applies the convex‐concave–based sequential linear matrix inequality optimization method to search for a feasible solution. Furthermore, an augmentation of the sequential linear matrix inequality algorithm to improve its numerical stability is presented. In the application part, a vehicle lateral control problem is presented to demonstrate the applicability of the proposed algorithm to a real‐world estimated state feedback control design problem and the necessity of the augmentation for numerical stability. [ABSTRACT FROM AUTHOR]

This paper deals with a novel robust current control scheme for permanent magnet synchronous machine drives that improves the conventional cascade structure. The main idea is to substitute the commonly used decoupling network, which requires the precise knowledge of the system parameters in any working condition, with a more flexible and robust observer scheme. In particular, the traditional configuration is improved by means of two proportional-integral observers that allow the preservation of the desired drive performances even in the presence of disturbances and uncertainties. The increased robustness is achieved by maintaining the reliable basic scheme with a pure proportional controller in the forward path with advantages in terms of design and tuning. A genetic algorithm is used to optimize the controller and observer parameters. This multiobjective optimization leads to good stability and an overall enhanced performance of the drive above the operative range. The choice of the optimal solution is based on a novel fitness function that takes into account both dynamical features and disturbance rejection capabilities of the system. The effectiveness of the proposed control scheme compared with a standard proportional-integral control is verified by means of numerical simulations and experiments as well. [ABSTRACT FROM AUTHOR]

A method for structural damage identification based on a modified Artificial Bee Colony algorithm is presented. A new formula is introduced to the onlooker bee phase to improve the convergence rate and the Tournament Selection Strategy is adopted instead of roulette to enhance global search ability of the algorithm. Test functions are introduced as benchmarks to verify the proposed algorithm. And then two numerical examples, including a supported beam and a plate, are conducted to investigate the efficiency and correctness of the proposed method. Final estimated results show that the present technique can produce more accurate damage identification results, comparing with other evolutionary algorithms, even with a few noise contaminated measurements. [ABSTRACT FROM PUBLISHER]

In this paper, a genetic algorithm with new multi-parent crossover (GA-MPC) is used for solving the optimal power flow problem. This method is tested on the IEEE 30-bus test system for 10 different cases where, in addition to the total fuel cost, different complexities and constraints like: emission, multi-fuels, valve-point effect and prohibited zones are considered. The effectiveness and robustness of the GA-MPC method are assessed using a statistical study and a comparative study with some well-known optimization methods. The obtained results along with the comparison study indicate that the GA-MPC method provides effective and high-quality solution when solving the optimal power flow problem even for complicated and highly constrained working conditions. [ABSTRACT FROM AUTHOR]

Summary: A new algorithm called moth‐flame optimization (MFO) algorithm is proposed to optimize a dual‐mode controller (DMC) for a multiarea hybrid interconnected power system. Initially, a 2‐area nonreheat system is considered. The optimum gains of DMC and proportional‐integral controller are optimized using the MFO algorithm. The superiority of the proposed approach is established while comparing the results with genetic algorithm, bacterial forging optimization algorithm, differential evolution, and hybrid bacterial forging optimization algorithm particle swarm optimization for the same system. The proposed approach is further extended to 2 unequal areas of a 6‐unit hybrid‐sources interconnected power system. The optimum gain of DMC and sliding mode controller (SMC) is optimized with MFO algorithm. The performance of an MFO tuned DMC is compared with particle swarm optimization and genetic algorithm tuned DMC, MFO tuned SMC, and teaching‐learning–based optimization optimized SMC for the same system. Furthermore, robustness analysis is performed by varying the system parameters from their nominal values. It is observed that the optimum gains obtained for nominal condition need not be reset for a wide variation in system parameters. [ABSTRACT FROM AUTHOR]

Summary: This study is concerned with the rigorous solution of worst‐case robust optimal control problems having bounded time‐varying uncertainty and nonlinear dynamics with affine uncertainty dependence. We propose an algorithm that combines existing uncertainty set‐propagation and moment‐expansion approaches. Specifically, we consider a high‐order moment expansion of the time‐varying uncertainty, and we bound the effect of the infinite‐dimensional remainder term on the system state, in a rigorous manner, using ellipsoidal calculus. We prove that the error introduced by the expansion converges to zero as more moments are added. Moreover, we describe a methodology to construct a conservative, yet more computationally tractable, robust optimization problem, whose solution values are also shown to converge to those of the original robust optimal control problem. We illustrate the applicability and accuracy of this approach with the robust time‐optimal control of a motorized robot arm. [ABSTRACT FROM AUTHOR]

We propose a novel edge-based visual–inertial fusion approach to address the problem of tracking aggressive motions with real-time state estimates. At the front-end, our system performs edge alignment, which estimates the relative poses in the distance transform domain with a larger convergence basin and stronger resistance to changing lighting conditions or camera exposures compared to the popular direct dense tracking. At the back-end, a sliding-window optimization-based framework is applied to fuse visual and inertial measurements. We utilize efficient inertial measurement unit (IMU) preintegration and two-way marginalization to generate accurate and smooth estimates with limited computational resources. To increase the robustness of our proposed system, we propose to perform an edge alignment self check and IMU-aided external check. Extensive statistical analysis and comparison are presented to verify the performance of our proposed approach and its usability with resource-constrained platforms. Comparing to state-of-the-art point feature-based visual–inertial fusion methods, our approach achieves better robustness under extreme motions or low frame rates, at the expense of slightly lower accuracy in general scenarios. We release our implementation as open-source ROS packages. [ABSTRACT FROM AUTHOR]

Robust optimization is a powerful means to handle optimization problems where there is a set of parameters that are uncertain. The effectiveness of the method is especially noticeable when these parameters are only known to lie inside some uncertainty region. Unfortunately, there are important computational considerations that have prevented the methodology from being fully adopted in fields of practice where the cost function that needs to be robustified is nonlinear with respect to such parameters. In this paper, we propose a new robust optimization formulation that circumvent the computational burden in problems where the cost decomposes as the sum of costs that each involve a single decision variable. This is done by exploiting the fact that in this formulation the worst-case cost function can be expressed as a convex combination between a nominal and an upper-bounding cost function. One can still control the conservatism of the robust solution by adjusting how many terms of this total cost can simultaneously reach their respective most pessimistic value. To demonstrate the potential of this "practicable robust counterpart" formulation, we present how it can be employed for the robust optimization of packet routing on a telecommunication network with congestion. In such problems, an important source of uncertainty stems from the queueing delay, which is usually approximated by a nonlinear convex function using the theory of M/M/1 queues. Computational results on a large number of problem instances of realistic size confirmthat it is possible to identify robust solutions that significantly outperform a deterministic approach in terms of both the amount of congestion and the risks of excessive congestion. Moreover, our proposed method also improves significantly the quality of solutions that are obtained using two other natural approximation schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2) the likelihood ratio (LR) method, and (3) the weak derivative method, to a setting where they did not previously apply. Examples in probability constraints, control charts, and financial derivatives demonstrate the broad applicability of the proposed framework. The new estimator preserves the singlerun efficiency of the classic IPA-LR estimators in applications, which is substantiated by numerical experiments. [ABSTRACT FROM AUTHOR]

As a byproduct of the oil refining process, fuel gas is the primary energy source of refineries. Considering self-generated and purchased fuel gas simultaneously in an optimization model will cut down energy cost and reduce carbon emissions in oil refineries. A mixed-integer linear program (MILP) has been built in our previous work. However, due to the fluctuation in the fuel gas generation and consumption, theoretical scheduling solutions may become infeasible or inaccurate. This article presents a robust engineering strategy for validating the model to variable conditions in four aspects: model precision, solving performance, optimization effect, and execution. The proposed strategy has been applied to a fuel gas system in one of the largest oil refineries (LRF) in China to ensure model feasibility, necessity, and effectiveness. The implementation results show that the proposed method reduces costs up to 5.63% through the single-period operational optimization and up to 7.76% in the multiperiod scheduling. [ABSTRACT FROM AUTHOR]

Summary: In optimization of multiple tuned mass dampers (MTMDs), certain restrictions or preconditions such as uniform distribution of stiffness, mass, or frequency spacing had been applied for simplification, but in turn, solution of individual stiffness and damping parameters are not the true optima. The main purpose of this paper is to obtain the true optima of individual stiffness and damping parameters of MTMD system. In the proposed method, parameters of TMD units are treated as free search optimization variables, and an efficient optimization algorithm, namely, artificial bee colony algorithm has been utilized in obtaining optimum parameters of MTMDs. Performance proposed method with respect to uncontrolled structure is verified through numerical analyses and compared with other reported methods. Comparisons show a superior performance of proposed approach. The basic properties of optimum design, the effectiveness, and robustness with respect to number of dampers are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, we consider the robust mean variance optimization problem where the probability distribution of assets’ returns is multivariate normal and the uncertain mean and covariance are controlled by a constraint involving Rényi divergence. We present the closed-form solutions for the robust mean variance optimization problem and find that the choice of order parameter which is related to the Rényi divergence measure will not impact optimal portfolio strategy under the cases that the mean vector and the covariance matrix are uncertain, respectively. Moreover, we obtain the closed-form solution for the robust mean variance optimization problem under the case that the mean vector and the covariance matrix are both uncertain. We illustrate the efficiency of our results with an example. [ABSTRACT FROM PUBLISHER]

Motivated by weakly convex optimization and quadratic optimization problems, we first show that there is no duality gap between a difference of convex (DC) program over DC constraints and its associated dual problem. We then provide certificates of global optimality for a class of nonconvex optimization problems. As an application, we derive characterizations of robust solutions for uncertain general nonconvex quadratic optimization problems over nonconvex quadratic constraints. [ABSTRACT FROM PUBLISHER]

Batch-end quality prediction is of paramount importance in industries producing high added-value products. However, all available methods hereto only use raw data at its native resolution. This paper proposes a new multi-resolution quality prediction (MRQP) methodology for batch-end quality, which exploits structured correlation in both the time and variables dimensions. The implementation of this methodology results in a more parsimonious and robust model structure, with a predictive performance bounded to be at least as good as the current standard approach. The implementation of MRQP is illustrated in three different case studies, where improvements over the standard single-resolution approach were found to be in the range of 10%–50%. From an interpretation standpoint, multi-resolution models are more robust with respect to the selection of too many predictors, facilitating the identification of key process variables, and providing information on the process time scales that influence final product quality, which can be further exploited for diagnosis, control, and optimization. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of probabilistic robust stabilization for uncertain systems subject to input saturation. A new probabilistic solution framework for robust control analysis and synthesis problems is addressed by a scenario optimization approach, in which the uncertainties are not assumed to be norm bounded. Furthermore, by expressing the saturated linear feedback law on a convex hull of a group of auxiliary linear feedback laws, we establish conditions under which the closed-loop system is probabilistic stable. Based on these conditions, the problem of designing the state feedback gains for achieving the largest size of the domain of attraction is formulated and solved as a constrained optimization problem with linear matrix inequality constraints. The results are then illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

Topology optimization using stress constraints and considering uncertainties is a serious challenge, since a reliability problem has to be solved for each stress constraint, for each element in the mesh. In this paper, an alternative way of solving this problem is used, where uncertainty quantification is performed through the first-order perturbation approach, with proper validation by Monte Carlo simulation. Uncertainties are considered in the loading magnitude and direction. The minimum volume problem subjected to local stress constraints is formulated as a robust problem, where the stress constraints are written as a weighted average between their expected value and standard deviation. The augmented Lagrangian method is used for handling the large set of local stress constraints, whereas a gradient-based algorithm is used for handling the bounding constraints. It is shown that even in the presence of small uncertainties in loading direction, different topologies are obtained when compared to a deterministic approach. The effect of correlation between uncertainties in loading magnitude and direction on optimal topologies is also studied, where the main observed result is loss of symmetry in optimal topologies. [ABSTRACT FROM AUTHOR]

A complex optimization criterion that takes into account the integral quadratic error and the stability margin of a robust digital control system for the rectification column used for the butylene-divinyl fraction is proposed. The influence of the weight coefficient, taking into account the ratio of the performance and stability indicators, on the dynamic characteristics of the system is investigated. High system performance and system stability indices over a sufficiently broad range of variation of the parameters of the rectification column are achieved through the use of the proposed optimization criterion and a stability estimation algorithm in the synthesis of a robust digital control system. [ABSTRACT FROM AUTHOR]

For a competitive design of a distillation system, heat integration is currently a necessary component in the chemical industry. In distillation systems with heat integration, the complex interaction among the column design variables and heat integration calls for a systematic optimization method. In this study, the development of a simultaneous optimization framework for distillation systems and heat integration is described. To achieve an accurate and reliable design, rigorous distillation column, heat exchanger and compressor models were used for the optimization. These models were reformulated using a pseudo-transient continuation (PTC) approach to improve their robustness. Because the framework is based on the equation-oriented environment, the automatic or symbolic differentiation is accessible for highly efficient gradient-based optimization algorithms. The results of two optimization cases of distillation systems with heat integration validated the proposed framework. In addition, the application of a bypass efficiency method while optimizing the number of stages is further discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-objective discrete robust optimization (MODRO) algorithm for design of engineering structures involving uncertainties. In the present MODRO procedure, grey relational analysis (GRA), coupled with principal component analysis (PCA), was used as a multicriteria decision making model for converting multiple conflicting objectives into one unified cost function. The optimization process was iterated using the successive Taguchi approach to avoid the limitation that the conventional Taguchi method fails to deal with a large number of design variables and design levels. The proposed method was first verified by a mathematical benchmark example and a ten-bar truss design problem; and then it was applied to a more sophisticated design case of full scale vehicle structure for crashworthiness criteria. The results showed that the algorithm is able to achieve an optimal design in a fairly efficient manner attributable to its integration with the multicriteria decision making model. Note that the optimal design can be directly used in practical applications without further design selection. In addition, it was found that the optimum is close to the corresponding Pareto frontier generated from the other approaches, such as the non-dominated sorting genetic algorithm II (NSGA-II), but can be more robust as a result of introduction of the Taguchi method. Due to its independence on metamodeling techniques, the proposed algorithm could be fairly promising for engineering design problems of high dimensionality. [ABSTRACT FROM AUTHOR]

Over the past few years, many crowd related accidents have happened at places of mass gatherings such as religious places, venues of sports, political and social events, and railway stations due to poor crowd management or poor system design. To mitigate the impact of such tragedies, developing adequate crowd management strategies is essential. A critical component of crowd management strategies in such emergencies is an evacuation plan. A proactive measure for evacuation strategies is to make a plan to utilize the available capacity by efficient network redesign. Redesigning the network helps in influencing the demand by enhancing or limiting the use of certain routes, without the use of force or explicit guiding mechanisms. A bi-level network based optimization model is proposed for network redesign. This approach aims at an optimal capacity change for an efficient crowd flow and evacuation during mass gatherings. To determine the optimal capacity change required to achieve the desired evacuation state, an Interior Point Algorithm (IPA) is applied. Further, to increase the computational efficiency, a heuristic is developed that finds a good starting point for IPA. Finally, the robustness of the redesigned network with respect to evacuation demand is examined along with alternate measures that further help in reducing the evacuation time. [ABSTRACT FROM AUTHOR]

The use of weights in multi-objective problems is one of the simplest ways to include multiple criteria in optimization models, in what is known as a weighted-sum approach. However, the solution to these models is highly dependent on the value of the weights, which is difficult to determine accurately. In this paper, we consider that the weights are defined as intervals of possible values rather than point estimates, and formulate a robust version of the traditional multi-objective optimization model. We explore, through a computational experiment, the effect that the uncertainty in the weights has on the optimal decisions and the levels obtained of the different objectives. Robust solutions favor decisions that produce similar levels of the different objectives and produce more of those objectives for which the weights are more certain. We apply this model to a lumber production problem where, in practice, more than a single performance indicator is pursued, but there is no clear preference relationship among them. [ABSTRACT FROM AUTHOR]