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This article aims at designing a robust wide-area damping controller (WADC) to address the problems associated with communication failures. The proposed controller is a state feedback-based controller, which uses generators' states as the controller inputs. The structure of the proposed controller's feedback gain matrix is a combination of two feedback gain matrices to achieve adequate robust performance over different disturbances and communication losses. These two feedback gain matrices are; full-scale feedback gain matrix and decentralized scale feedback gain matrix. Both models are designed by considering all generators' states are available at the controller input side. However, both matrices' structures are different from each other. The conventional LQR state feedback control technique is used to design the full-scale matrix. On the other hand, the decentralized scale matrix is computed using a structurally constrained H 2 -norm optimization technique to acquire the required structure. The full-scale model gives robust performance under different faults. Whereas, the decentralized scale matrix will result robust performance against communication failures. Therefore, to achieve superior performance under all conditions, the proposed controller can be used. To show the efficacy of the proposed controller, it is compared with different existing controllers and the results are illustrated. Simulation studies are conducted on well-known IEEE 39-bus and 68-bus test systems to validate the robustness of the proposed controller considering different faults and communication failures. [ABSTRACT FROM AUTHOR]

A robust optimal control of discrete time Markov chains with finite terminal T and bounded costs or wealth using probability distortion is studied. The time inconsistency of these distortion operators and hence its lack of dynamic programming are discussed. Due to that, dynamic versions of these operators are introduced, and its availability for dynamic programming is demonstrated. Based on dynamic programming algorithm, existence of the optimal policy is justified and an application of the theory to portfolio optimization along with a numerical study is also presented. [ABSTRACT FROM AUTHOR]

Proportional reactive power sharing among the distributed generators (DGs) is still a challenge in an islanded microgrid. The disparity in output voltage of each DG is the main cause of the error in reactive power sharing in DGs. The output voltage difference of DGs is caused by unequal line impedances and connected local loads. In order to reduce the error in reactive power sharing among the DGs, a Robust Volt-VAr control scheme (RVVCS) with an adaptive nominal voltage is proposed in this study. Since the suggested strategy is entirely decentralized, it does not need knowledge of feeder impedance or network configurations. Also, it does not require any communication link or optimization technique. The technique proposed is tested on several network configurations, including various loads, radial networks, reconfigured networks, and mesh networks, and is found satisfactory in each case. In one of the many cases presented in this study, with n q = 3 e - 4 V/VAr, β = 0.2, β add = 0.002 and local as well as common load is connected, the suggested RVVCS minimises the error in reactive power output of D G 1 , D G 2 and D G 3 to (42.68%, - 8.92%, - 33.82%), respectively, compared to the error obtained in RDCS (72.21 %, - 15.61%, - 56.6%), SADCS (72.6%, - 16%, - 56.6%) and CDCS (243.74%, - 69.87%, - 173.87%). A similar reduction pattern in reactive power output is also observed in all the case studies performed in this research. The stability of the proposed strategy is investigated in terms of eigenvalue analysis. The practicability of the proposed technique is validated in time-domain simulation in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Background and problem statement: Model-free or learning-based control, in particular, reinforcement learning (RL), is expected to be applied for complex robotic tasks. Traditional RL requires that a policy to be optimized is state-dependent, that means, the policy is a kind of feedback (FB) controllers. Due to the necessity of correct state observation in such a FB controller, it is sensitive to sensing failures. To alleviate this drawback of the FB controllers, feedback error learning integrates one of them with a feedforward (FF) controller. RL can be improved by dealing with the FB/FF policies, but to the best of our knowledge, a methodology for learning them in a unified manner has not been developed. Contribution: In this paper, we propose a new optimization problem for optimizing both the FB/FF policies simultaneously. Inspired by control as inference, the proposed optimization problem considers minimization/maximization of divergences between trajectories, one is predicted by the composed policy and a stochastic dynamics model, and others are inferred as optimal/non-optimal ones. By approximating the stochastic dynamics model using variational method, we naturally derive a regularization between the FB/FF policies. In numerical simulations and a robot experiment, we verified that the proposed method can stably optimize the composed policy even with the different learning law from the traditional RL. In addition, we demonstrated that the FF policy is robust to the sensing failures and can hold the optimal motion. [ABSTRACT FROM AUTHOR]

This paper presents an optimal decentralized control system for an isolated, networked dc microgrid with multiple sources and composite loads. The key feature of the proposed controller is that it requires only locally measurable states for controlling the local generation while achieving global stability. The controller is designed to minimize a performance index accounting for voltage regulation and branch current regulation with improved dynamics and stability. Decentralized control guarantees high reliability and modularity of the microgrid enabling plug-and-play operations of new units with little modifications. The solution of optimal decentralized control is obtained through constrained parameter optimization technique. The designed controller ensures stability over extended operating conditions of load and droop changes. This leads to a unique robust control law for the dc microgrid that obviates the need for frequent change of controller settings for any change in system parameters. Modal analysis and time-domain simulation of the controlled system substantiate robustness and effectiveness of the proposed decentralized control system in improving the system dynamic performance and optimal set point tracking with a unique controller configuration. [ABSTRACT FROM AUTHOR]

Robust model predictive control approaches and other applications lead to nonlinear optimization problems defined on (scenario) trees. We present structure-preserving Quasi-Newton update formulas as well as structured inertia correction techniques that allow to solve these problems by interior-point methods with specialized KKT solvers for tree-structured optimization problems. The same type of KKT solvers could be used in active-set based SQP methods. The viability of our approach is demonstrated by two robust control problems. [ABSTRACT FROM AUTHOR]

The current work describes the optimum design of a tuned mass damper inerter (TMDI) to control a benchmark 10-story base-excited linear shear building under seismic excitations. Mass and inertance ratios are preselected, and optimum free vibration parameters of the TMDI (i.e., natural frequency and damping ratios) are calculated for single degree-of-freedom (SDOF) and multi degree-of-freedom (MDOF) models with different configurations of single and double inerter TMDIs at different locations using the colliding bodies optimization technique. Four different inherent damping values are considered for each analysis. Minimizing the H ∞ norm of the roof displacement transfer function is considered as the objective function for robust control of the building. Additionally, the optimum designed damper performance and its robustness is assessed in both the frequency and time domains. Results show that while being robust, the SDOF-based optimized TMDI approach should be used with caution and it is recommended to optimize and employ the TMDI using the MDOF model. Results indicate the superior performance of the proposed well-tuned damper with proper configuration in comparison to a same weight/mass classical TMD. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the problem of the stabilization for discrete-time Takagi–Sugeno fuzzy parametric uncertain systems. The stabilization conditions of these systems are investigated with two observers and two different Lyapunov functions: nonquadratic and delayed nonquadratic. The stabilization conditions are analyzed between k and k + t sample variations in the Lyapunov function. The obtained stabilization results represent an extension of previous works with one-sample variation in discrete time. All the results are obtained in the form of linear matrix inequalities which are solved by using various convex optimization algorithms. Two theorems are proposed, and comparison via simulation is given to demonstrate the robustness of the proposed approaches. Nevertheless, this paper shows that the second proposed observer gives the less conservative results (less restrictive). These reduced conservative results are demonstrated by a larger feasible area of stabilization (stabilization domain) and a fast convergence of estimation errors compared to the first. [ABSTRACT FROM AUTHOR]

Semi-supervised classification is a hot topic in pattern recognition and machine learning. However, in presence of heavy noise and outliers, the unlabeled training data could be very challenging or even misleading for the semi-supervised classifier. In this paper, we propose a novel structure regularized self-paced learning method for semi-supervised classification problems, which can efficiently learn partially labeled training data sequentially from the simple to the complex ones. The proposed formulation consists of three components: a cost function defined by a mixture of losses, a functional complexity regularizer, and a self-paced regularizer; and the corresponding optimization algorithm involves three iterative steps: classifier updating, sample importance calculating, and pseudo-labeling. In the proposed method, the cost function for classifier updating and sample importance calculating is defined as a combination of the label fitting loss and manifold smoothness loss. Then, the importance of the pseudo-labeled and unlabeled samples is adaptively calculated by the novel cost. Unlabeled samples with high importance values are pseudo-labeled with their current predictions. In this way, labels are efficiently propagated from the labeled samples to the unlabeled ones in the robust self-paced manner. Experimental results on several benchmark data sets are provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To achieve the state estimation of uncertain nonlinear systems, this paper proposes a novel optimal robust observer via the fuzzy bound information of the uncertainty. A robust observer scheme is first proposed to guarantee the uniform boundedness and uniform ultimate boundedness of the state estimation system regardless of the actual value of the uncertainty. To optimize the observer gain, a fuzzy set-theoretic-based optimal approach is then proposed by utilizing the fuzzy information of the uncertainty bound. The optimization problem is completely solved in analytic form. The resulting observer is able to guarantee the robustness of the state estimation system to the uncertainty, while minimizing an optimization index related to the state estimation performance and the observer cost. The novelty of this research is a creative optimal robust observer design by blending state estimation theory, fuzzy set theory, and optimization theory into an integrated framework. [ABSTRACT FROM AUTHOR]

We develop search algorithms based on local search, and a matheuristic that solves a set of mixed integer programming models to improve the robustness of a set of solutions for an academic timetabling problem. The matheuristic uses the solution pool feature of CPLEX while solving two related MIP models iteratively. The solutions form a network (Akkan et al. in Eur J Oper Res 249(2):560-576, 2016. doi:10.1016/j.ejor.2015.08.047), in which edges are defined by the Hamming distance between pairs of solutions. This network is used to calculate a robustness measure, where disruption of a solution is assumed to occur when the time slot to which a team had been assigned is no longer feasible for that team and the heuristic response to this disruption is choosing one of the neighbors of the disrupted solution. Considering the objective function of the timetabling problem and this robustness measure results in a bi-criteria optimization problem where the goal is to improve the Pareto front by enlarging the network. We compare the performance of the heuristics on a set of random instances and seven semesters' actual data. These results show that some of the proposed local search algorithms and the matheuristic find high quality approximate Pareto fronts. Besides being one of the few timetabling algorithms in the literature addressing robustness, a key contribution of this research is the demonstration of the effectiveness of the matheuristic approach. By using this matheuristic approach, for any discrete optimization model that can be solved optimally or near-optimally in an acceptable time, researchers can develop a robustness improvement algorithm. [ABSTRACT FROM AUTHOR]

A graph-based classification method is proposed for both semi-supervised learning in the case of Euclidean data and classification in the case of graph data. Our manifold learning technique is based on a convex optimization problem involving a convex quadratic regularization term and a concave quadratic loss function with a trade-off parameter carefully chosen so that the objective function remains convex. As shown empirically, the advantage of considering a concave loss function is that the learning problem becomes more robust in the presence of noisy labels. Furthermore, the loss function considered here is then more similar to a classification loss while several other methods treat graph-based classification problems as regression problems. [ABSTRACT FROM AUTHOR]

In this paper, a simple and robust approach for flame and fire image analysis is proposed. It is based on the local binary patterns, double thresholding and Levenberg-Marquardt optimization technique. The presented algorithm detects the sharp edges and removes the noise and irrelevant artifacts. The autoadaptive nature of the algorithm ensures the primary edges of the flame and fire are identified in the different conditions. Moreover, a graphical approach is presented which can be used to calculate the combustion furnace flame temperature. The various experimentations are carried out on synthetic as well as real flame and fire images which validate the efficacy and robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

The optimum design of a cam mechanism is a very interesting problem in the contact mechanics today, due to the alternative industrial applications as a mechanism of precision. In this paper, a new evolutionary algorithm called modified adaptive differential evolution (MADE) is introduced for multi-objective optimization of a cam mechanism with offset translating roller follower. The optimization procedure is investigated for three objectives among them minimum congestion, maximum efficiency, and maximum strength resistance of the cam. To enhance the design quality of the mechanism in the optimization process, more geometric parameters and more design constraints are included in the problem formulation. In order to validate the developed algorithm, three engineering design problems are solved. The simulation results for the tested problems indicate the effectiveness and the robustness of the proposed algorithm compared to the various existed optimization methods. Finally, the optimal results obtained for the case study example provide very useful decisions for a cam mechanism synthesis. [ABSTRACT FROM AUTHOR]

In this paper, robust efficient solutions of a vector optimization problem, whose image space is ordered by an arbitrary ordering cone, are defined. This is done from different points of view, including set based and norm based. The relationships between these solution concepts are established. Furthermore, it is shown that, for a general vector optimization problem, each norm-based robust efficient solution is a strictly efficient solution; each isolated efficient solution is a norm-based robust efficient solution; and, under appropriate assumptions, each norm-based robust efficient solution is a Henig properly efficient solution. Various necessary and sufficient conditions for characterizing norm-based robust solutions of a general vector optimization problem, in terms of the tangent and normal cones and the nonascent directions, are presented. An optimization problem for calculating a robustness radius is provided, and then, the largest robustness radius is determined. Moreover, some alterations of objective functions preserving weak/strict/Henig proper/robust efficiency are studied. [ABSTRACT FROM AUTHOR]

This paper focuses on a unified approach to characterizing different kinds of multiobjective robustness concepts. Based on linear and nonlinear scalarization results for several set order relations, together with the help of image space analysis, some suitable subsets of scalarization image space are introduced to make equivalent characterizations for upper set (lower set, set, certainly, respectively) less ordered robustness for uncertain multiobjective optimization problems. In particular, the nonlinear scalarization functional plays a significant role in computing various multiobjective robust solutions. Finally, the corresponding examples are included to show the effectiveness of the results derived in this paper. [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]

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]

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]

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]

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]

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]

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]

In this paper, we study risk-averse models for multicriteria optimization problems under uncertainty. We use a weighted sum-based scalarization and take a robust approach by considering a set of scalarization vectors to address the ambiguity and inconsistency in the relative weights of each criterion. We model the risk aversion of the decision makers via the concept of multivariate conditional value-at-risk ( CVaR). First, we introduce a model that optimizes the worst-case multivariate CVaR and show that its optimal solution lies on a particular type of stochastic efficient frontier. To solve this model, we develop a finitely convergent delayed cut generation algorithm for finite probability spaces. We also show that the proposed model can be reformulated as a compact linear program under certain assumptions. In addition, for the cut generation problem, which is in general a mixed-integer program, we give a stronger formulation than the existing ones for the equiprobable case. Next, we observe that similar polyhedral enhancements are also useful for a related class of multivariate CVaR-constrained optimization problems that has attracted attention recently. In our computational study, we use a budget allocation application to benchmark our proposed maximin type risk-averse model against its risk-neutral counterpart and a related multivariate CVaR-constrained model. Finally, we illustrate the effectiveness of the proposed solution methods for both classes of models. [ABSTRACT FROM AUTHOR]

UNIRANDI is a stochastic local search algorithm that performs line searches from starting points along good random directions. In this paper, we focus on a modified version of this method. The new algorithm, addition to the random directions, considers more promising directions in order to speed up the optimization process. The performance of the new method is tested empirically on standard test functions in terms of function evaluations, success rates, error values, and CPU time. It is also compared to the previous version as well as other local search methods. Numerical results show that the new method is promising in terms of robustness and efficiency. [ABSTRACT FROM AUTHOR]

In this paper we propose the use of damped techniques within Nonlinear Conjugate Gradient (NCG) methods. Damped techniques were introduced by Powell and recently reproposed by Al-Baali and till now, only applied in the framework of quasi-Newton methods. We extend their use to NCG methods in large scale unconstrained optimization, aiming at possibly improving the efficiency and the robustness of the latter methods, especially when solving difficult problems. We consider both unpreconditioned and Preconditioned NCG. In the latter case, we embed damped techniques within a class of preconditioners based on quasi-Newton updates. Our purpose is to possibly provide efficient preconditioners which approximate, in some sense, the inverse of the Hessian matrix, while still preserving information provided by the secant equation or some of its modifications. The results of an extensive numerical experience highlights that the proposed approach is quite promising. [ABSTRACT FROM AUTHOR]

The production life of oil reservoirs starts under significant uncertainty regarding the actual economical return of the recovery process due to the lack of oil field data. Consequently, investors and operators make management decisions based on a limited and uncertain description of the reservoir. In this work, we propose a new formulation for robust optimization of reservoir well controls. It is inspired by the multiple shooting (MS) method which permits a broad range of parallelization opportunities and output constraint handling. This formulation exploits coherent risk measures, a concept traditionally used in finance, to bound the risk on constraint violation. We propose a reduced sequential quadratic programming (rSQP) algorithm to solve the underlying optimization problem. This algorithm exploits the structure of the coherent risk measures, thus a large set of constraints are solved within sub-problems. Moreover, a variable elimination procedure allows solving the optimization problem in a reduced space and an iterative active-set method helps to handle a large set of inequality constraints. Finally, we demonstrate the application of constraints to bound the risk of water production peaks rather than worst-case satisfaction. [ABSTRACT FROM AUTHOR]

In this paper we present a novel feature-based RGB-D camera pose optimization algorithm for real-time 3D reconstruction systems. During camera pose estimation, current methods in online systems suffer from fast-scanned RGB-D data, or generate inaccurate relative transformations between consecutive frames. Our approach improves current methods by utilizing matched features across all frames and is robust for RGB-D data with large shifts in consecutive frames. We directly estimate camera pose for each frame by efficiently solving a quadratic minimization problem to maximize the consistency of 3D points in global space across frames corresponding to matched feature points. We have implemented our method within two state-of-the-art online 3D reconstruction platforms. Experimental results testify that our method is efficient and reliable in estimating camera poses for RGB-D data with large shifts. [ABSTRACT FROM AUTHOR]

The high productivity of a production process has a major impact on the reduction of the production cost and on a quick response to changing demands. Information about a failure-free machine operation time obtained in advance allows the users to plan preventive maintenance in order to keep the machine in a good operational condition. The introduction of maintenance work into a schedule reduces the frequency of unpredicted breaks caused by machine failures. It also results in higher productivity and in-time production. The foregoing of this constitutes the main idea of the predictive scheduling method proposed in the paper. Rescheduling of disrupted operations, with a minimal impact on the stability and robustness of a schedule, is the main idea of the reactive scheduling method proposed. The first objective of the paper is to present a hybrid multi-objective immune algorithm (H-MOIA) aided by heuristics: a minimal impact of disrupted operation on the schedule (MIDOS) for predictive scheduling and a minimal impact of rescheduled operation on the schedule (MIROS) for reactive scheduling. The second objective is to compare the H-MOIA with various methods for predictive and reactive scheduling. The H-MOIA + MIDOS is compared to two algorithms, identified in reference publications: (1) an algorithm based on priority rules: the least flexible job first (LFJ) and the longest processing time (LPT) (2) an Average Slack Method. The H-MOIA + MIROS is compared to: (1) an algorithm based on priority rules: the LFJ and LPT and (2) Shifted Gap-Reduction. This paper presents the research results and computer simulations. [ABSTRACT FROM AUTHOR]

Variability of load magnitude/direction is a most significant source of uncertainties in practical engineering. This paper investigates robust topology optimization of structures subjected to uncertain dynamic excitations. The unknown-but-bounded dynamic loads/accelerations are described with the non-probabilistic ellipsoid convex model. The aim of the optimization problem is to minimize the absolute dynamic compliance for the worst-case loading condition. For this purpose, a generalized compliance matrix is defined to construct the objective function. To find the optimal structural layout under uncertain dynamic excitations, we first formulate the robust topology optimization problem into a nested double-loop one. Here, the inner-loop aims to seek the worst-case combination of the excitations (which depends on the current design, and is usually to be found by a global optimization algorithm), and the outer-loop optimizes the structural topology under the found worst-case excitation. To tackle the inherent difficulties associated with such an originally nested formulation, we convert the inner-loop into an inhomogeneous eigenvalue problem using the optimality condition. Thus the double-loop problem is reformulated into an equivalent single-loop one. This formulation ensures that the strict-sense worst-case combination of the uncertain excitations for each intermediate design be located without resorting to a time-consuming global search algorithm. The sensitivity analysis of the worst-case objective function value is derived with the adjoint variable method, and then the optimization problem is solved by a gradient-based mathematical programming method. Numerical examples are presented to illustrate the effectiveness and efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]

Background: In the past few decades, a significant volume of work has been carried out on various aspects of the state estimation problem to estimate an optimum state vector of the power system. This problem has been focused on, in previous studies regarding the computational efficiency and numerical robustness in view to find point estimates for system state parameters. This current investigation, constructed confidence intervals for the unknown state parameters of the system. The research indicates that confidence intervals can yield addition useful information about the estimated parameters. Methods: The feasible interval estimates for the system state parameters have been modelled in this study by considering the random uncertainty in the processing measurements. The statistical assumptions of the measurement errors have been utilized to characterize the probabilistic behavior of the estimated parameters in terms of confidence intervals. The Gauss-Newton algorithm has been adopted for maximizing the likelihood function of the processing measurements and obtaining the confidence intervals. Results: The usage of the confidence intervals was demonstrated through Monte Carlo experiments on a real dataset of the 6-bus and IEEE 14-bus power systems for both small and large sample sizes. The confidence intervals were constructed for the test networks for the sample of measurements 18, 28, 44 and 68 based on the redundancy ratio R. The proposed interval estimates outperformed for the sample sizes of 28 in the 6 bus and 68 in the IEEE 14-bus systems, respectively. The poor performance for the constructed interval estimates have been reported even for the large sample sizes in the existence of contaminated measurements. Conclusions: The results of the study show that the method is effective and practically applicable in the state estimation of a power system. The constructed confidence intervals for the system state parameters adequately perform for the lager sample size. However, the existence of the gross errors in the processing measurements had severe effect on the performance of the proposed interval estimates. [ABSTRACT FROM AUTHOR]

Second-order cone programming (SOCP) formulations have received increasing attention as robust optimization schemes for Support Vector Machine (SVM) classification. These formulations study the worst-case setting for class-conditional densities, leading to potentially more effective classifiers in terms of performance compared to the standard SVM formulation. In this work we propose an SOCP extension for Twin SVM, a recently developed classification approach that constructs two nonparallel classifiers. The linear and kernel-based SOCP formulations for Twin SVM are derived, while the duality analysis provides interesting geometrical properties of the proposed method. Experiments on benchmark datasets demonstrate the virtues of our approach in terms of classification performance compared to alternative SVM methods. [ABSTRACT FROM AUTHOR]

This led to graduate studies at UC Berkeley and joining the Berkeley faculty in 1957. His over 50-year career at Berkeley has included everything from research and teaching to serving as the first ombudsman in the UC system. Is Professor of the Graduate School and Professor Emeritus of Engineering Science at the University of California, Berkeley. [Extracted from the article]

We consider optimal decision-making problems in an uncertain environment. In particular, we consider the case in which the distribution of the input is unknown, yet there is some historical data drawn from the distribution. In this paper, we propose a new type of distributionally robust optimization model called the likelihood robust optimization (LRO) model for this class of problems. In contrast to previous work on distributionally robust optimization that focuses on certain parameters (e.g., mean, variance, etc.) of the input distribution, we exploit the historical data and define the accessible distribution set to contain only those distributions that make the observed data achieve a certain level of likelihood. Then we formulate the targeting problem as one of optimizing the expected value of the objective function under the worst-case distribution in that set. Our model avoids the over-conservativeness of some prior robust approaches by ruling out unrealistic distributions while maintaining robustness of the solution for any statistically likely outcomes. We present statistical analyses of our model using Bayesian statistics and empirical likelihood theory. Specifically, we prove the asymptotic behavior of our distribution set and establish the relationship between our model and other distributionally robust models. To test the performance of our model, we apply it to the newsvendor problem and the portfolio selection problem. The test results show that the solutions of our model indeed have desirable performance. [ABSTRACT FROM AUTHOR]

In this paper, a contribution to the determination of reliable cutting parameters is presented, which is minimizing the expected machining cost and maximizing the expected production rate, with taking into account the uncertainties of uncontrollable factors. The concept of failure probability of stochastic production limitations is integrated into constrained and unconstrained formulations of multi-objective optimization problems. New probabilistic version of the nondominated sorting genetic algorithm P-NSGA-II, which incorporates the Monte Carlo simulations for accurate assessment of cumulative distribution functions, was developed and applied in two numerical examples based on similar and anterior work. In the first case, it is a question of the search space that is completely 'closed' by high natural variability related to the multi-pass roughing operation: in this case, the failure risk of technological limitations are considered as objectives to minimize with economic objectives. The second case is related to deformed search space due to the uncertainties specific to finishing operation; therefore, the economic objectives are minimized under imposed maximum probabilities of failure. In both situations, the efficiency and robustness of optimal solutions generated by the P-NSGA-II algorithm are analysed, discussed and compared with sequence of unconstrained minimization technique (SUMT) method. [ABSTRACT FROM AUTHOR]

We study the problems of robust stabilization and optimization of the equilibrium states of nonlinear mechanical systems. Sufficient conditions for the stabilization of a linear system with measured output feedback are formulated by means of the full-order state observers. The solution of the general problem of robust stabilization and the estimates of the quadratic performance criterion are presented for a family of nonlinear systems on examples of a one-link pendulum on a moving platform in the upper equilibrium position and a pendulum with flywheel control. The application of the obtained results is reduced to the solution of systems of linear matrix inequalities. [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]

In this paper, based on biological phenomena of different characters at different stages, we propose a nonlinear multi-stage enzyme-catalytic dynamic system with unknown time and system parameters. Such system starts at different initial conditions for formulating batch culture process of glycerol bio-dissimilation to 1,3-propanediol. Some properties of the nonlinear system are discussed. In view of the difficulty in accurately measuring the concentration of intracellular substances and the absence of equilibrium points for the nonlinear system, we quantitatively define biological robustness for the entire process of batch culture instead of one for the approximately stable state of continuous culture. Taking the biological robustness of the intracellular substances together with the relative error between the experimental data and the computational values of the extracellular substances as the cost function, we formulate an identification problem subject to the nonlinear system, continuous state inequality constraints and parameter constraints. Analytical solution to system is not naturally available, therefore, a huge number of numerical computations of the proposed system and the proposed biological robustness make solving the identification problem by a serial computer a very complicated task. To improve computational efficiency, we develop an effective parallelized optimization algorithm, based on the constraint transcription and smoothing approximation techniques, for seeking the optimal time and system parameters. Compared with previous work, we assert that the optimal time and system parameters together with the corresponding nonlinear multi-stage dynamical system can reasonably describe batch fermentation at different initial conditions. [ABSTRACT FROM AUTHOR]

The design of mechanical microstructures having auxetic behaviour is proposed in this paper using techniques of topology optimization for compliant mechanisms. A robust hybrid algorithm based on evolutionary algorithms and local search steps is used. The result may need verification in order to accommodate needs not taken into account in the topology optimization. Therefore, a numerical homogenization scheme is used in order to show that the final design still has the wished negative Poisson's property. [ABSTRACT FROM AUTHOR]

In this paper, the fault detection problem is investigated for a class of nonlinear networked systems with multiple fading measurements. A stochastic Rice fading model is employed to simultaneously describe the phenomena of random time delays and channel fadings via setting different values of the channel coefficients. Attention is focused on the design of a full-order fault detection filter such that the overall fault detection dynamics is exponentially mean-square stable and the disturbance rejection attenuation is constrained to a prescribed level by means of the $$H_{\infty }$$ performance index. Intensive stochastic analysis is carried out to obtain sufficient conditions for the existence of the desired fault detection filter, and the corresponding optimal filter parameters can be easily derived by solving a convex optimization problem via a semi-definite program method. Finally, a numerical example is exploited to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Consider networks in harsh environments, where nodes may be lost due to failure, attack, or infection-how is the topology affected by such events? Can we mimic and measure the effect? We propose a new generative model of network evolution in dynamic and harsh environments. Our model can reproduce the range of topologies observed across known robust and fragile biological networks, as well as several additional transport, communication, and social networks. We also develop a new optimization measure to evaluate robustness based on preserving high connectivity following random or adversarial bursty node loss. Using this measure, we evaluate the robustness of several real-world networks and propose a new distributed algorithm to construct secure networks operating within malicious environments. [ABSTRACT FROM AUTHOR]

A new closed advancing-layer method for generating high-aspect-ratio elements in the boundary-layer (BL) region is presented. This approach utilizes a recent connectivity optimization-based moving mesh strategy for deforming the volume mesh as the BL is inflated. It handles very efficiently BL front collision and produces a natural smooth anisotropic blending between colliding layers. Moreover, it provides a robust strategy to couple unstructured anisotropic mesh adaptation and high-aspect-ratio elements pseudo-structured BL meshes. The proposed method is directly compared to a well-established open advancing-layer method. Results for typical aerospace configurations are presented that provide a clear comparison between both methods as well as the effectiveness of the connectivity optimization-based moving mesh strategy. They show that the closed method yields similar results in terms of mesh quality and efficiency, and that the considered moving mesh strategy is an efficient and effective method for deforming the unstructured volume mesh. [ABSTRACT FROM AUTHOR]

A new approach to the control design for the constraint-following of fuzzy mechanical system is proposed in this paper. We consider the mechanical system containing uncertainty, which may be nonlinear and fast time varying. The uncertainty is assumed to be bounded, and the bound of the uncertainty lies within a prescribed fuzzy set. A class of robust controls is proposed, and the corresponding adaptive law is constructed to emulate a constant parameter related to the bound of the uncertainty. The control scheme is deterministic and is not if-then rule based. Furthermore, we formulate the gain design of the adaptive law as a constrained optimization problem, which minimizes both the average fuzzy performance and the control effort. It is proved that the global solution to this optimization problem exists and is unique. The closed-form solution and the closed-form minimum cost are presented. The resulting adaptive robust control is able to guarantee the uniform boundedness and uniform ultimate boundedness of the uncertain system regardless of the uncertainty, while minimizing the average fuzzy performance and control effort. [ABSTRACT FROM AUTHOR]

In this paper, we present a new relaxed nonmonotone trust region method with adaptive radius for solving unconstrained optimization problems. The proposed method combines a relaxed nonmonotone technique with a modified version of the adaptive trust region strategy proposed by Shi and Guo (J Comput Appl Math 213:509-520, ). Under some suitable and standard assumptions, we establish the global convergence property as well as the superlinear convergence rate for the new method. Numerical results on some test problems show the efficiency and effectiveness of the new proposed method in practice. [ABSTRACT FROM AUTHOR]

The stochastic location-allocation p-hub median problems are related to long-term decisions made in risky situations. Due to the importance of this type of problems in real-world applications, the authors were motivated to propose an approach to obtain more reliable policies in stochastic environments considering the decision makers' preferences. Therefore, a systematic approach to make robust decisions for the single location-allocation p-hub median problem based on mean-variance theory and two-stage stochastic programming was developed. The approach involves three main phases, namely location modeling, risk modeling, and decision making, each including several steps. In the first phase, the pertinent location-allocation model of the problem is developed in the form of a two-stage stochastic model based on its deterministic version. A risk measure, based on total cost function and mean-variance theory, is derived in the second phase. Furthermore, two heterogeneous terms of the risk measure have been normalized and an innovative procedure has been proposed to significantly improve the calculation efficiency. In the third phase, the Pareto solution is obtained, the frontier curve is depicted to determine the decision maker's risk aversion coefficient, and a robust policy is obtained through optimization based on decision makers' preferences. Finally, a case study of an automobile part distribution system with stochastic demand is described to further illustrate our risk management and analysis approach. [ABSTRACT FROM AUTHOR]

The broadcasting communication mode is widely used in Vehicular Ad hoc Networks. It is used for sending emergency messages, road-traffic information or to help routing protocols to determine routes. This communication mode is known to be hard to achieve efficiently since it depends on the network density. Indeed, broadcasting methods may cause network congestion if they are not well designed. This paper introduces a novel Autonomic Dissemination Method (ADM) which delivers messages in accordance with given message classes and network density levels. The proposed approach is based on two steps: an offline optimization process and an online adaptation to the network characteristics. ADM allows each node to dynamically adapt its broadcasting strategy not only with respect to the network density, but also according to the class of the message to send: emergency (high-priority), road-traffic (medium-priority) or either comfort message (low-priority). The ultimate goal of ADM is to make effective use of radio resources when there are many messages to send simultaneously. This approach increases the efficiency of the broadcast process in terms of message delivery ratio, latency and interferences reduction. The autonomic computing paradigm improves the robustness of protocols. [ABSTRACT FROM AUTHOR]

Traditionally, model calibration is formulated as a single objective problem, where fidelity to measurements is maximized by adjusting model parameters. In such a formulation however, the model with best fidelity merely represents an optimum compromise between various forms of errors and uncertainties and thus, multiple calibrated models can be found to demonstrate comparable fidelity producing non-unique solutions. To alleviate this problem, the authors formulate model calibration as a multi-objective problem with two distinct objectives: fidelity and robustness. Herein, robustness is defined as the maximum allowable uncertainty in calibrating model parameters with which the model continues to yield acceptable agreement with measurements. The proposed approach is demonstrated through the calibration of a finite element model of a steel moment resisting frame. [ABSTRACT FROM AUTHOR]

We propose a robust static control algorithm for linear objects under parametric and structural uncertainty and an external uncontrollable disturbance. The resulting algorithm ensures that the object output tracks the reference signal with the necessary precision. We give modeling results that illustrate that the algorithm operates correctly. [ABSTRACT FROM AUTHOR]

This paper describes the optimal flight of a discus. Two objective functions are considered. One is the flight distance, and the other is robustness. Robustness is defined as insensitivity to deviations from the local optimal release conditions. The aim of the optimization is to maximize both flight distance and robustness. There are ten control and design variables, which include variables concerned with the skill of the thrower and with the design of the equipment. It was found that there is a trade-off between flight distance and robustness. It was also found that lower drag is preferable in the first half of flight, and greater lift and drag are preferable in the latter half of flight for the world record throw of 77 m. In order to do this, the initial angle between the velocity vector and the discus planform should be negative at the launch, even if the initial lift acts vertically downward. The attitude of the discus should be inclined at launch. The discus rolls with time during the flight, so that the discus planform becomes almost in the horizontal plane in the latter half of the flight. Lower mass is good for achieving longer flight distance, but conversely, mass should be slightly greater for robustness. [ABSTRACT FROM AUTHOR]