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Assessment of the components of a solution helps provide useful information for an optimization problem. This paper presents a new population-based problem-reduction evolutionary algorithm (PREA) based on the solution components assessment. An individual solution is regarded as being constructed by basic elements, and the concept of acceptability is introduced to evaluate them. The PREA consists of a searching phase and an evaluation phase. The acceptability of basic elements is calculated in the evaluation phase and passed to the searching phase. In the searching phase, for each individual solution, the original optimization problem is reduced to a new smaller-size problem. With the evolution of the algorithm, the number of common basic elements in the population increases until all individual solutions are exactly the same which is supposed to be the near-optimal solution of the optimization problem. The new algorithm is applied to a large variety of capacitated vehicle routing problems (CVRP) with customers up to nearly 500. Experimental results show that the proposed algorithm has the advantages of fast convergence and robustness in solution quality over the comparative algorithms. [ABSTRACT FROM AUTHOR]

Recently, synchronization movement control of multiple vessels has been studied broadly. In most of the studies, the communication network among vessels is considered to be fixed and the time delay is often ignored. However, the communication network among vessels maybe vary because of switching of different tasks, and the time delay is necessary to be considered when the communication network is unreliable. In this paper, the synchronization movement of multiple vessels with switching connected communication topologies is studied, and an adaptive synchronization control algorithm that is based on backstepping sliding mode control is proposed. The control algorithm is achieved by defining cross coupling error which is combination of the trajectory tracking error and velocity tracking error. And an adaptive control term is used to estimate the external disturbances, so that the unknown external disturbances can be compensated. Furthermore, the robustness of the control law to time-varying time delay is also discussed. At last, some simulations are carried out to validate the effectiveness of the proposed synchronization control algorithm. [ABSTRACT FROM AUTHOR]

The nonfragile H∞ filtering problem for a kind of Takagi-Sugeno (T-S) fuzzy stochastic system which has a time-varying delay and parameter uncertainties has been studied in this paper. Sufficient conditions for stochastic input-to-state stability (SISS) of the fuzzy stochastic systems are obtained. Attention is focused on the design of a nonfragile H∞ filter such that the filtering error system can tolerate some level of the gain variations in the filter and the H∞ performance level also could be satisfied. By using the SISS result, the approach to design the nonfragile filter is proposed in terms of linear matrix inequalities. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Application of chemical flooding in petroleum reservoirs turns into hot topic of the recent researches. Development strategies of the aforementioned technique are more robust and precise when we consider both economical points of view (net present value, NPV) and technical points of view (recovery factor, RF). In current study many attempts have been made to propose predictive model for estimation of efficiency of chemical flooding in oil reservoirs. To gain this end, a couple of swarm intelligence and artificial neural network (ANN) is employed. Also, lucrative and high precise chemical flooding data banks reported in previous attentions are utilized to test and validate proposed intelligent model. According to the mean square error (MSE), correlation coefficient, and average absolute relative deviation, the suggested swarm approach has acceptable reliability, integrity and robustness. Thus, the proposed intelligent model can be considered as an alternative model to predict the efficiency of chemical flooding in oil reservoir when the required experimental data are not available or accessible. [ABSTRACT FROM AUTHOR]

This paper presents an original adaptive sliding mode control strategy for a class of nonlinear systems on the basis of uncertainty and disturbance estimator. The nonlinear systems can be with parametric uncertainties as well as unmatched uncertainties and external disturbances. The novel adaptive sliding mode control has several advantages over traditional sliding mode control method. Firstly, discontinuous sign function does not exist in the proposed adaptive sliding mode controller, and it is not replaced by saturation function or similar approximation functions as well. Therefore, chattering is avoided in essence, and the chattering avoidance is not at the cost of reducing the robustness of the closed-loop systems. Secondly, the uncertainties do not need to satisfy matching condition and the bounds of uncertainties are not required to be unknown. Thirdly, it is proved that the closed-loop systems have robustness to parameter uncertainties as well as unmatched model uncertainties and external disturbances. The robust stability is analyzed from a second-order linear time invariant system to a nonlinear systemgradually. Simulation on a pendulum system with motor dynamics verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Grain transportation plays an important role in many relief and emergency supply chains. In this paper, we take the grain emergency vehicle scheduling model between multiware houses as the research object. Under the emergency environment, the aim of the problem is to maximize the utilization of vehicles and minimize the delay time. The randomness of the key parameters in grain emergency vehicle scheduling, such as time and demand, is determined through statistical analysis and the model is solved through robust optimization method. Besides, we apply the numerical examples for experimental analysis. We compare the robust optimization model with classic model to illustrate the differences and similarities between them. The results show that the uncertainty of both time and demand has great influence on the efficiency of grain emergency vehicle scheduling problem. [ABSTRACT FROM AUTHOR]

This paper investigates the robust adaptive exponential synchronization in mean square of stochastic perturbed chaotic delayed neural networks with nonidentical parametric uncertainties. A robust adaptive feedback controller is proposed based on Gronwally's inequality, drive-response concept, and adaptive feedback control technique with the update laws of nonidentical parametric uncertainties as well as linear matrix inequality (LMI) approach. The sufficient conditions for robust adaptive exponential synchronization inmean square of uncoupled uncertain stochastic chaotic delayed neural networks are derived in terms of linear matrix inequalities (LMIs). The effect of nonidentical uncertain parameter uncertainties is suppressed by the designed robust adaptive feedback controller rapidly. A numerical example is provided to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a method to design the robust parametric control for autonomous rendezvous of spacecrafts with the inertial information with uncertainty. We consider model uncertainty of traditional C-W equation to formulate the dynamic model of the relative motion. Based on eigenstructure assignment and model reference theory, a concise control law for spacecraft rendezvous is proposed which could be fixed through solving an optimization problem. The cost function considers the stabilization of the system and other performances. Simulation results illustrate the robustness and effectiveness of the proposed control. [ABSTRACT FROM AUTHOR]

Considering the nonlinearities, uncertainties of large erecting system, and the circumstance disturbances in erecting process, a novel sliding mode control strategy is proposed in this research. The proposed control strategy establishes the sliding mode without reaching phase using an integral sliding surface. Thus, robustness against uncertainties increases from the very beginning of the process. Furthermore, adaptive laws are used for the controller to estimate the unknown but bounded system uncertainties. Therefore, the upper bounds of the system uncertainties are not required to be known in advance. Then, the time-varying term is applied to ensure the global robustness. Moreover, the boundary layer method is used to attenuate the high frequency chattering. The experiment results demonstrated that the proposed strategy could effectively restrain parametric uncertainties and external disturbances and improve the tracking accuracy in the erecting process. In addition, the control performance of the proposed control strategy is better than that of the PID control and the conventional sliding mode control. [ABSTRACT FROM AUTHOR]

To simulate the freedom and uncertain individual behavior of krill herd, this paper introduces the opposition based learning (OBL) strategy and free search operator into krill herd optimization algorithm (KH) and proposes a novel opposition-based free search krill herd optimization algorithm (FSKH). In FSKH, each krill individual can search according to its own perception and scope of activities. The free search strategy highly encourages the individuals to escape from being trapped in local optimal solution. So the diversity and exploration ability of krill population are improved. And FSKH can achieve a better balance between local search and global search. The experiment results of fourteen benchmark functions indicate that the proposed algorithm can be effective and feasible in both low-dimensional and high-dimensional cases. And the convergence speed and precision of FSKH are higher. Compared to PSO, DE, KH, HS, FS, and BA algorithms, the proposed algorithm shows a better optimization performance and robustness. [ABSTRACT FROM AUTHOR]

An off-line robust constrained model predictive control (MPC) algorithm for linear time-varying (LTV) systems is developed. A novel feature is the fact that both model uncertainty and bounded additive disturbance are explicitly taken into account in the offline formulation of MPC. In order to reduce the on-line computational burdens, a sequence of explicit control laws corresponding to a sequence of positively invariant sets is computed off-line. At each sampling time, the smallest positively invariant set containing the measured state is determined and the corresponding control law is implemented in the process. The proposed MPC algorithm can guarantee robust stability while ensuring the satisfaction of input and output constraints. The effectiveness of the proposed MPC algorithm is illustrated by two examples. [ABSTRACT FROM AUTHOR]

A control law for retarded time-delay systems is considered, concerning infinite closed-loop spectrum assignment. An algebraic method for spectrum assignment is presented with a unique optimization algorithm for minimization of spectral abscissa and effective shaping of the chains of infinitely many closed-loop poles. Uncertainty of plant delays of a certain structure is considered in a sense of a robust simultaneous stabilization. Robust performance is achieved using mixed sensitivity design, which is incorporated into the addressed control law. [ABSTRACT FROM AUTHOR]

Robot simultaneous localization and mapping (SLAM) problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF) based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Since a celebrate linear minimum mean square (MMS) Kalman filter in integration GPS/INS system cannot guarantee the robustness performance, a H ∞ filtering with respect to polytopic uncertainty is designed. The purpose of this paper is to give an illustration of this application and a contrast with traditional Kalman filter. A game theory H ∞ filter is first reviewed; next we utilize linearmatrix inequalities (LMI) approach to design the robust H ∞ filter. For the special INS/GPSmodel, unstablemodel case is considered. We give an explanation for Kalman filter divergence under uncertain dynamic system and simultaneously investigate the relationship between H ∞ filter and Kalman filter. A loosely coupled INS/GPS simulation system is given here to verify this application. Result shows that the robust H ∞ filter has a better performance when system suffers uncertainty; also it is more robust compared to the conventional Kalman filter. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model for robust production planning. The model helps fashion apparel suppliers in making decisions concerning allocation of production orders to different production plants characterized by different lead times and production costs, and in proper time scheduling and sequencing of these production orders. The model aims at optimizing these decisions concerning objectives of minimal production costs and minimal tardiness. It considers several factors such as the stochastic nature of customer demand, differences in production and transport costs and transport times between production plants in different regions. Finally, the model is applied to a case study. The results of numerical computations are presented. The implications of the model results on different fashion related product types and delivery strategies, as well as the model's limitations and potentials for expansion, are discussed. Results indicate that the production planning model using conditional value at risk (CVaR) as the risk measure performs robustly and provides flexibility in decision analysis between different scenarios. [ABSTRACT FROM AUTHOR]

The homography between image pairs is normally estimated by minimizing a suitable cost function given 2D keypoint correspondences. The correspondences are typically established using descriptor distance of keypoints. However, the correspondences are often incorrect due to ambiguous descriptors which can introduce errors into following homography computing step. There have been numerous attempts to filter out these erroneous correspondences, but it is unlikely to always achieve perfect matching. To deal with this problem, we propose a nonlinear least squares optimization approach to compute homography such that false matches have no or little effect on computed homography. Unlike normal homography computation algorithms, our method formulates not only the keypoints' geometric relationship but also their descriptor similarity into cost function. Moreover, the cost function is parametrized in such a way that incorrect correspondences can be simultaneously identified while the homography is computed. Experiments show that the proposed approach can perform well even with the presence of a large number of outliers. [ABSTRACT FROM AUTHOR]

This paper investigates the stabilization problem for a class of nonlinear systems, whose control coefficient is uncertain and varies continuously in value and sign. The study emphasizes the development of a robust control that consists of a modified Nussbaum function to tackle the uncertain varying control coefficient. By such a method, the finite-time escape phenomenon has been prevented when the control coefficient is crossing zero and varying its sign. The proposed control guarantees the asymptotic stabilization of the system and boundedness of all closed-loop signals. The control performance is illustrated by a numerical simulation. [ABSTRACT FROM AUTHOR]

To rely on joint active disturbance rejection control (ADRC) and repetitive control (RC), in this paper, a compound control law for active power filter (APF) current control system is proposed. According to the theory of ADRC, the uncertainties in the model and from the circumstance outside are considered as the unknown disturbance to the system. The extended state observer can evaluate the unknown disturbance. Next, RC is introduced into current loop to improve the steady characteristics. The ADRC is used to get a good dynamic performance, and RC is used to get a good static performance. A good simulation result is got through choosing and changing the parameters, and the feasibility, adaptability, and robustness of the control are testified by this result. [ABSTRACT FROM AUTHOR]

Presently pneumatic muscles (PMs) are used in various applications due to their simple construction, lightweight, and high force-to-weight ratio. However, pneumatic muscles are facing various problems due to their nonlinear characteristics and various uncertainties in real applications. To cope with the uncertainties and strong nonlinearity of a PM model, a nonlinear disturbance observer (NDO) is designed to estimate the lumped disturbance. Based on the disturbance observer, the tracking control of PM is studied. Stability analysis based on Lyapunov method with respect to our proposed control law is discussed. The simulation results show the validity, effectiveness, and enhancing robustness of the proposed methods. [ABSTRACT FROM AUTHOR]

A nonlocal damage-plasticity coupled area-weighted model was proposed to investigate the fracture process of the ductile fracture materials with softening behavior, such as asphalt concrete materials. The model can overcome the mesh sensitivity problem in the analysis. A subroutine of ABAQUS software was developed to apply this nonlocal damage-plasticity coupled area-weighted model into finite element analysis. Then, the subroutine was adopted in finite element models to simulate several loading conditions. The results indicate that the nonlocal damage-plasticity coupled area-weighted model and the subroutine describe the softening behavior of asphalt concrete materials better than traditional softening models. And the robustness and stability of the proposed model were also justified. The proposed model provides a concrete and accurate alternate to predict the damage condition of asphalt concrete. [ABSTRACT FROM AUTHOR]

Aiming at the phenomenon of a large number of flight delays in the terminal area makes a reasonable scheduling for the approach and departure flights, which will minimize flight delay losses and improve runway utilization. This paper considered factors such as operating conditions and safety interval of multi runways; the maximum throughput and minimum flight delay losses as well as robustness were taken as objective functions; the model of optimization scheduling of approach and departure flights was established. Finally, the genetic algorithm was introduced to solve the model. The results showed that, in the program whose advance is not counted as a loss, its runway throughput is improved by 18.4%, the delay losses are reduced by 85.8%, and the robustness is increased by 20% compared with the results of FCFS (first come first served) algorithm, while, compared with the program whose advance is counted as a loss, the runway throughput is improved by 15.16%, flight delay losses are decreased by 75.64%, and the robustness is also increased by 20%. The algorithm can improve the efficiency and reduce delay losses effectively and reduce the workload of controllers, thereby improving economic results. [ABSTRACT FROM AUTHOR]

Advances in complex network research have recently stimulated increasing interests in understanding the relationship between the topology and dynamics of complex networks. In the paper, we study the synchronizability of a class of local-world dynamical networks. Then, we have proposed a local-world synchronization-optimal growth topology model. Compared with the local-world evolving network model, it exhibits a stronger synchronizability. We also investigate the robustness of the synchronizability with respect to random failures and the fragility of the synchronizability with specific removal of nodes. [ABSTRACT FROM AUTHOR]

The problem of robust decentralized adaptive neural stabilization control is investigated for a class of nonaffine nonlinear interconnected large-scale systems with unknown dead zones. In the controller design procedure, radical basis function (RBF) neural networks are applied to approximate packaged unknown nonlinearities and then an adaptive neural decentralized controller is systematically derived without requiring any information on the boundedness of dead zone parameters (slopes and break points). It is proven that the developed control scheme can ensure that all the signals in the closed-loop system are semi globally uniformly ultimately bounded in the sense of mean square. Simulation study is provided to further demonstrate the effectiveness of the developed control scheme. [ABSTRACT FROM AUTHOR]

Airport gate assignment is core task for airport ground operations. Due to the fact that the departure and arrival time of flights may be influenced by many random factors, the airport gate assignment scheme may encounter gate conflict and many other problems. This paper aims at finding a robust solution for airport gate assignment problem. A mixed integer model is proposed to formulate the problem, and colony algorithm is designed to solve this model. Simulation result shows that, in consideration of robustness, the ability of antidisturbance for airport gate assignment scheme has much improved. [ABSTRACT FROM AUTHOR]

The problem of delay-dependent robust stabilization for continuously singular time-varying delay systems with norm-bounded uncertainties is investigated in this paper. First, based on some mathematical transform, the uncertain singular system is described in a form which involves the time-delay integral items. Then, in terms of the delay-range-dependent Lyapunov functional and the LMI technique, the improved delay-dependent LMIs-based conditions are established for the uncertain singular systems with time-varying delay to be regular, causal, and stable. Furthermore, by solving these LMIs, an explicit expression for the desired state feedback control law can be obtained; thus, the regularity, causality, and stability of the closed-loop system are guaranteed. In the end, numerical examples are given to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

A robust control allocation scheme is developed for rigid spacecraft attitude stabilization in the presence of actuator partial loss fault, actuator failure, and actuator misalignment. First, a neural network fault detection scheme is proposed, Second, an adaptive attitude tracking strategy is employed which can realize fault tolerance control under the actuator partial loss and actuator failure within λmin = 0.5. The attitude tracking and faults detection are always here during the procedure. Once the fault occurred which could not guaranteed the attitude stable for 30 s, the robust control allocation strategy is generated automatically. The robust control allocation compensates the control effectiveness uncertainty which caused the actuator misalignment. The unknown disturbances, uncertain inertia matrix, and even actuator error with limited actuators are all considered in the controller design process. All are achieved with inexpensive online computations. Numerical results are also presented that not only highlight the closed-loop performance benefits of the control law derived here but also illustrate its great robustness. [ABSTRACT FROM AUTHOR]

Face verification in the presence of age progression is an important problem that has not been widely addressed. In this paper, we propose to use the active appearance model (AAM) and gradient orientation pyramid (GOP) feature representation for this problem. First, we use the AMA on the dataset and generate the AMA images; we then get the representation of gradient orientation on a hierarchical model, which is the appearance of GOP. When combined with a support vector machine (SM), experimental results show that our approach has excellent performance on two public domain face aging datasets: FGNET and MORPH. Second, we compare the performance of the proposed methods with a number of related face verification methods; the results show that the new approach is more robust and performs better. [ABSTRACT FROM AUTHOR]

The design of a variable structure sliding-mode controller (SMC) for a variable speed wind turbine with double-fed induction generator, based on the fuzzy logic, is described in this paper. The purpose of this controller is to maximize the energy capture by operating the turbine at the optimal rotational speed as well as fast and stable generator response. The dynamics of both the turbine and the generator are modeled to exhibit their mechanical/electrical characteristics. Two global sliding-mode controllers, which eliminate the reaching phase of SMC and the sliding-mode motion starts from the beginning, are designed to guarantee the robust tracking of both the optimal blade-rotor speed and the reference generator torque/flux in the whole process, despite the parametric uncertainty and external disturbance. To reduce the adverse chattering effect of the conventional SMC, the adaptive fuzzy inference strategy is adopted to deduce the adjustable switch gain, instead of the fixed gains. Simulation results show that the proposed controller achieves global asymptotic tracking, satisfied torque/flux responses, and has better performance and higher utilization ratio of wind energy than the conventional feedback-linearization method. [ABSTRACT FROM AUTHOR]

There are increasing concerns about missing traffic data in recent years. In this paper, a robust missing traffic flow data imputation approach based on matrix completion is proposed. In the proposed method, the similarity of traffic flow from day to day is exploited to impute missing data by the low-rank hypothesis of constructed traffic flow matrix. And the physical limitation of road capacity and nonnegativity is also considered through the optimization process, which avoids the possibility of producing negative and overcapacity values. Moreover, the proposed algorithm can impute missing data and recover outlier in a unify framework. The experiment results show that the proposed method is more accurate, stable, and reasonable. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) has been successfully applied to solve many practical engineering problems. However, more efficient strategies are needed to coordinate global and local searches in the solution space when the studied problem is extremely nonlinear and highly dimensional. This work proposes a novel adaptive elite-based PSO approach. The adaptive elite strategies involve the following two tasks: (1) appending the mean search to the original approach and (2) pruning/cloning particles. The mean search, leading to stable convergence, helps the iterative process coordinate between the global and local searches. The mean of the particles and standard deviation of the distances between pairs of particles are utilized to prune distant particles. The best particle is cloned and it replaces the pruned distant particles in the elite strategy. To evaluate the performance and generality of the proposed method, four benchmark functions were tested by traditional PSO, chaotic PSO, differential evolution, and genetic algorithm. Finally, a realistic loss minimization problem in an electric power system is studied to show the robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Aiming at the problems of modeling errors, parameter variations, and load moment disturbances in DC motor control system, one global sliding mode disturbance observer (GSMDO) is proposed based on the global sliding mode (GSM) control theory. The output of GSMDO is used as the disturbance compensation in control system, which can improve the robust performance of DC motor control system. Based on the designed GSMDO in inner loop, one compound controller, composed of a feedback controller and a feedforward controller, is proposed in order to realize the position tracking of DC motor system. The gains of feedback controller are obtained by means of linear quadratic regulator (LQR) optimal control theory. Simulation results present that the proposed control scheme possesses better tracking properties and stronger robustness against modeling errors, parameter variations, and friction moment disturbances. Moreover, its structure is simple; therefore it is easy to be implemented in engineering. [ABSTRACT FROM AUTHOR]

In the context of travel demand uncertainty, this paper investigates how to determine the robust road speed limits for improving mobility and lowering vehicular emissions in bimodal transportation systems that involve private cars and subway. More specifically, the total demand vector is supposed to vary within a given set. Our target is to find the optimal road speed limits against the worst feasible demand scenario so as to minimize the sum social cost of system travel time and vehicular emissions. In order to estimate traffic emissions more reliably, motor vehicle emission simulator (MOVES) is utilized to simulate the emission factor function with respect to average speed. On these bases, we formulate the robust speed limits design problem as a "min-max" nonlinear model with complementarity constraints and solve it iteratively by a cutting-plane scheme that contains two sub-MPCCs. A numerical example is illustrated at the end. [ABSTRACT FROM AUTHOR]

This paper deals with the aerodynamic modeling of a small ducted fan UAV and the problem of attitude stabilization when the parameter of the vehicle is varied. The main aerodynamic model of the hovering flight UAV is first presented. Then, an attitude control is designed from a linearization of the dynamic model around the hovering flight, which is based on the H∞ ∞ output feedback control theory with D-stability. Simulation results show that such method has good robustness to the attitude system. They can meet the requirements of attitude control and verify further the feasibility of such a control strategy. [ABSTRACT FROM AUTHOR]

The paper proposes a new switched control design method for some classes of uncertain nonlinear plants described by Takagi-Sugeno fuzzy models. This method uses a quadratic Lyapunov function to design the feedback controller gains based on linear matrix inequalities (LMIs). The controller gain is chosen by a switching law that returns the smallest value of the time derivative of the Lyapunov function. The proposed methodology eliminates the need to find the membership function expressions to implement the control laws. The control designs of a ball-and-beam system and of a magnetic levitator illustrate the procedure. [ABSTRACT FROM AUTHOR]

This paper proposes a robust switching control strategy for a transmission system with unknown backlash. Firstly, the adverse effects of backlash nonlinearity in the transmission system are analyzed. Then the backlash model and different operating modes of the transmission system with backlash are investigated. For each operating mode, an individual controller is designed to make the system robust against the unknown backlash and various frequencies of the input signal. Moreover, a supervisory controller is proposed to estimate the current mode of the transmission system and coordinate the controller switching between different modes. Simulations are done to verify that our switching control strategy can efficiently reduce the oscillation caused by backlash and is quite robust against the variation of the frequency of the input signal. [ABSTRACT FROM AUTHOR]

The robust H∞ filtering problem for a class of network-based systems with random sensor delay is investigated. The sensor delay is supposed to be a stochastic variable satisfying Bernoulli binary distribution. Using the Lyapunov function and Wirtinger's inequality approach, the sufficient conditions are derived to ensure that the filtering error systems are exponentially stable with a prescribed H∞ disturbance attenuation level and the filter design method is proposed in terms of linear matrix inequalities. The effectiveness of the proposed method is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

We present a novel linear observer with an extension dealing with polytopic uncertainties in a vehicle dynamic system to identify the side-slip angle. The performance optimization issue is addressed by the minimization of H∞ norm of the system considering the estimation error as an output and the steer angle as an input. Contrary to the standard robust optimal design approaches, we use a convex inner approximation technique to reduce the order of the observer and this enables us to derive suboptimal, fixed-order, and efficiently practicable estimators. Moreover, the numerical examples performed on two-track nonlinear model of the system are provided to illustrate the impacts of design parameters on the optimization results and the efficiency of the technique. [ABSTRACT FROM AUTHOR]

The conjugate gradient (CG) method has played a special role in solving large-scale nonlinear optimization problems due to the simplicity of their very low memory requirements. In this paper, we propose a new conjugacy condition which is similar to Dai-Liao (2001). Based on this condition, the related nonlinear conjugate gradient method is given. With some mild conditions, the given method is globally convergent under the strong Wolfe-Powell line search for general functions. The numerical experiments show that the proposed method is very robust and efficient. [ABSTRACT FROM AUTHOR]

A robust online fault prediction method which combines sliding autoregressive moving average (ARMA)modeling with online least squares support vector regression (LS-SVR) compensation is presented for unknown nonlinear system. At first, we design an online LS-SVR algorithm for nonlinear time series prediction. Based on this, a combined time series prediction method is developed for nonlinear system prediction. The sliding ARMA model is used to approximate the nonlinear time series; meanwhile, the online LSSVR is added to compensate for the nonlinear modeling error with external disturbance. As a result, the one-step-ahead prediction of the nonlinear time series is achieved and it can be extended to n-step-ahead prediction. The result of the n-step-ahead prediction is then used to judge the fault based on an abnormity estimation algorithm only using normal data of system. Accordingly, the online fault prediction is implemented with less amount of calculation. Finally, the proposed method is applied to fault prediction of model-unknown fighter F-16. The experimental results show that the method can predict the fault of nonlinear system not only accurately but also quickly. [ABSTRACT FROM AUTHOR]

Cameras with telephoto lens are usually used to recover details of an object that is either small or located far away from the cameras. However, the calibration of this kind of cameras is not as accurate as the one of cameras with short focal lengths that are commonly used in many vision applications. This paper has two contributions. First, we present a first-order error analysis that shows the relation between focal length and estimation uncertainties of camera parameters. To our knowledge, this error analysis with respect to focal length has not been studied in the area of camera calibration. Second, we propose a robust algorithm to calibrate the camera with a long focal length without using additional devices. By adding a regularization term, our algorithm makes the estimation of the image of the absolute conic well posed. As a consequence, the covariance of camera parameters can be reduced greatly. We further used simulations and real data to verify our proposed algorithm and obtained very stable results. [ABSTRACT FROM AUTHOR]

A relatively fast pursuit algorithm in face recognition is proposed, compared to existing pursuit algorithms. More stopping rules have been put forward to solve the problem of slow response of OMP, which can fully develop the superiority of pursuit algorithm-- avoiding to process useless information in the training dictionary. For the test samples that are affected by partial occlusion, corruption, and facial disguise, recognition rates of most algorithms fall rapidly. The robust version of this algorithm can identify these samples automatically and process them accordingly. The recognition rates on ORL database, Yale database, and FERET database are 95.5%, 93.87%, and 92.29%, respectively. The recognition performance under various levels of occlusion and corruption is also experimentally proved to be significantly enhanced. [ABSTRACT FROM AUTHOR]

This paper investigates the distributed consensus-based robust adaptive formation control for nonholonomic mobile robots with partially known dynamics. Firstly, multi robot formation control problem has been converted into a state consensus problem. Secondly, the practical control strategies, which incorporate the distributed kinematic controllers and the robust adaptive torque controllers, are designed for solving the formation control problem. Thirdly, the specified reference trajectory for the geometric centroid of the formation is assumed as the trajectory of a virtual leader, whose information is available to only a subset of the followers. Finally, numerical results are provided to illustrate the effectiveness of the proposed control approaches. [ABSTRACT FROM AUTHOR]

This paper considers the problem of recovering low-rank matrices which are heavily corrupted by outliers or large errors. To improve the robustness of existing recovery methods, the problem is solved by formulating it as a generalized non-smooth non-convex minimization functional via exploiting the Schatten p-norm (0 < p ⩽ 1) and Lq(0 < q ⩽ 1) seminorm. Two numerical algorithms are provided based on the augmented Lagrange multiplier (ALM) and accelerated proximal gradient (APG) methods as well as efficient root-finder strategies. Experimental results demonstrate that the proposed generalized approach is more inclusive and effective compared with state-of-the-art methods, either convex or nonconvex. [ABSTRACT FROM AUTHOR]

Smoothing noisy data is commonly encountered in engineering domain, and currently robust penalized regression spline models are perceived to be the most promising methods for coping with this issue, due to their flexibilities in capturing the nonlinear trends in the data and effectively alleviating the disturbance from the outliers. Against such a background, this paper conducts a thoroughly comparative analysis of two popular robust smoothing techniques, the M-type estimator and S-estimation for penalized regression splines, both of which are reelaborated starting from their origins, with their derivation process reformulated and the corresponding algorithms reorganized under a unified framework. Performances of these two estimators are thoroughly evaluated from the aspects of fitting accuracy, robustness, and execution time upon the MATLAB platform. Elaborately comparative experiments demonstrate that robust penalized spline smoothing methods possess the capability of resistance to the noise effect compared with the nonrobust penalized LS spline regression method. Furthermore, the M-estimator exerts stable performance only for the observations with moderate perturbation error, whereas the S-estimator behaves fairly well even for heavily contaminated observations, but consuming more execution time. These findings can be served as guidance to the selection of appropriate approach for smoothing the noisy data. [ABSTRACT FROM AUTHOR]

One of the main objectives of watermarking is to achieve a better tradeoff between robustness and high visual quality of a host image. In recent years, there has been a significant development in gray-level image watermarking using fractal-based method. This paper presents a human visual system (HVS) based fractal watermarking method for color images. In the proposed method, a color pixel is considered as a 3-D vector in RGB space. And a general form of 3 × 3 matrix is utilized as the scaling operator. Meanwhile, the luminance offset vector is substituted by the range block mean vector. Then an orthogonalization fractal color coding method is achieved to obtain very high image quality. We also show that the orthogonalization fractal color decoding is a mean vector-invariant iteration. So, the range block mean vector is a good place for hiding watermark. Furthermore, for consistency with the characteristics of the HVS, we carry out the embedding process in the CIE La* b* space and incorporate a just noticeable difference (JND) profile to ensure the watermark invisibility. Experimental results show that the proposed method has good robustness against various typical attacks, at the same time, with an imperceptible change in image quality. [ABSTRACT FROM AUTHOR]

We propose a robust probability classifier model to address classification problems with data uncertainty. A class-conditional probability distributional set is constructed based on the modified χ2-distance. Based on a "linear combination assumption" for the posterior class-conditional probabilities, we consider a classification criterion using the weighted sum of the posterior probabilities. An optimal robust minimax classifier is defined as the one with the minimal worst-case absolute error loss function value over all possible distributions belonging to the constructed distributional set. Based on the conic duality theorem, we show that the resulted optimization problem can be reformulated into a second order cone programming problem which can be efficiently solved by interior algorithms. The robustness of the proposed model can avoid the "overlearning" phenomenon on training sets and thus keep a comparable accuracy on test sets. Numerical experiments validate the effectiveness of the proposed model and further show that it also provides promising results on multiple classification problems. [ABSTRACT FROM AUTHOR]

The constant false alarm rate (CFAR) is a detection algorithm that is generally used in radar or sonar systems, but its performance depends greatly on the environment. This means that the detection performance cannot be satisfied with only a single CFAR detector. This paper evaluates mathematically a proposed environmental adaptive (EA) CFAR detector. The proposed CFAR detector selects an optimal CFAR detector depending on the environment. Computer simulations validate the mathematical analysis and robustness of the detector in homogeneous and nonhomogeneous backgrounds. [ABSTRACT FROM AUTHOR]

As more and more renewable energy sources (RES) integrated into the conventional distribution system, how to make the current electric grid more reliable and efficient is becoming an important topic the world must face. In order to achieve these goals, grid-connected hybrid energy systems (HES) which contain battery energy storage systems (BESS) and many other advanced technologies have been developed and applied. Many benefits of BESS, such as high density of energy and power, have fast response in energy time-shift, frequency regulation, and so on. This paper focuses on the fluctuation alleviation and power quality improvement of grid-connected HES with high penetration level of RES. A multistage dispatch strategy of BESS for HES is proposed in this paper to mitigate the randomness and intermittence of the power flowed in HES because of high penetration level of RES integration. Four other conventional strategies are also discussed for evaluating the performance of the method proposed in this paper. Detailed cases and corresponding discussions are implemented, and the results show that the method proposed in this paper is more effective and robust than the other conventional strategies. [ABSTRACT FROM AUTHOR]

Robust nonlinear control with compensation operator is presented for a Peltier actuated system, where the compensation operator is designed by using a predictive model on heat radiation. For the Peltier system, the heat radiation is related to the fourth power of temperature. So, the heat radiation is affected evidently by the temperature when it is high and temperature difference between the system and environment is large. A new nonlinear model with the heat radiation is set up for the system according to some thermal conduction laws. To ensure robust stability of the nonlinear system, operator based robust right coprime factorization design is considered. Also, a compensation operator based on a predictive model is proposed to cancel effect of the heat radiation, where the predictive model is set up by using radial basis kernel function based SVM (support vector machine) method. Finally, simulation results are given to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Multiple imaging modalities based face recognition has become a hot research topic. A great number of multispectral face recognition algorithms/systems have been designed in the last decade. How to extract features of different spectrum has still been an important issue for face recognition. To address this problem, we propose a robust tensor preserving projection (RTPP) algorithm which represents a multispectral image as a third-order tensor. RTPP constructs sparse neighborhoods and then computes weights of the tensor. RTPP iteratively obtains one spectral space transformation matrix through preserving the sparse neighborhoods. Due to sparse representation, RTPP can not only keep the underlying spatial structure of multispectral images but also enhance robustness. The experiments on both Equinox and DHUFO face databases show that the performance of the proposed method is better than those of related algorithms. [ABSTRACT FROM AUTHOR]