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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]

This paper studies the problem of guaranteed cost control for spacecraft evacuation. The relative dynamic model is established based on Clohessy-Wiltshire (C-W) equations. The paper has taken parameter uncertainty, output tracking, disturbance attenuation, and fuel cost into consideration. The paper introduces a new Lyapunov approach, so the controller design problem can be transferred into a convex optimization problem subject to linear matrix inequality (LMI) constraints. By using the controller, the spacecraft evacuation can be completed in a safe extent. Meanwhile, the fuel cost also has an upper bound. Then the paper analyzes the approach of evacuation and discusses possible initial states of the spacecraft for the controller design. An illustrative example is applied to show the effectiveness of the proposed control design method, and different performances caused by different initial states of spacecraft (-V-bar, -R-bar, and +H-bar) are simulated. [ABSTRACT FROM AUTHOR]

Sliding mode control (SMC) is rapped for the chattering due to high gain control. However, high gain control causes the system robust. For developing a system with robustness of SMC, a servo motor motion controller combining the two-degree-of-freedom (2DOF) system and SMC is proposed. The discussed motion type is point-to-point control with the constraint of trapezoid velocity profile. SMC is designed to guide the motor motion to follow a predefined trail, and the inner 2DOF system is used to compensate the deterioration due to the adoption of load observer. The proposed hybrid system is realized on a PC-based motion controller, and the validness is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

Due to being poor in control quality of the combustion process of gas heating furnace, this paper explored a sort of strong robust control algorithm in order to improve the control quality of the combustion process of gas heating furnace. The paper analyzed the control puzzle in the complex combustion process of gas heating furnace, summarized the cybernetics characteristic of the complex combustion process, researched into control strategy of the uncertainty complex control process, discussed the control model of the complex process, presented a sort of intelligent integration between human-simulated intelligence and expert control technology, and constructed the control algorithm for the combustion process controlling of gas heating furnace. The simulation results showed that the control algorithm proposed in the paper is not only better in dynamic and steady quality of the combustion process, but also obvious in energy saving effect, feasible, and effective in control strategy. [ABSTRACT FROM AUTHOR]

Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the "store-carry-forward" strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI) algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks. [ABSTRACT FROM AUTHOR]

For the areas of low textured in image pairs, there is nearly no point that can be detected by traditional methods. The information in these areas will not be extracted by classical interest-point detectors. In this paper, a novel weakly textured point detection method is presented. The points with weakly textured characteristic are detected by the symmetry concept. The proposed approach considers the gray variability of the weakly textured local regions. The detection mechanism can be separated into three steps: region-similarity computation, candidate point searching, and refinement of weakly textured point set. The mechanism of radius scale selection and texture strength conception are used in the second step and the third step, respectively. The matching algorithm based on sparse representation (SRM) is used for matching the detected points in different images. The results obtained on image sets with different objects show high robustness of the method to background and intraclass variations as well as to different photometric and geometric transformations; the points detected by this method are also the complement of points detected by classical detectors from the literature. And we also verify the efficacy of SRM by comparing with classical algorithms under the occlusion and corruption situations for matching the weakly textured points. Experiments demonstrate the effectiveness of the proposed weakly textured point detection algorithm. [ABSTRACT FROM AUTHOR]

A two-degree-of-freedom control structure is proposed for a class of unstable processes with time delay based on modified Smith predictor control; the superior performance of disturbance rejection and good robust stability are gained for the system. The set-point tracking controller is designed using the direct synthesis method; the IMC-PID controller for disturbance rejection is designed based on the internal mode control design principle. The controller for set-point response and the controller for disturbance rejection can be adjusted and optimized independently. Meanwhile, the two controllers are designed in the form of PID, which is convenient for engineering application. Finally, simulation examples demonstrate the validity of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper proposed a robust fault-tolerant control algorithm for satellite stabilization based on active disturbance rejection approach with artificial bee colony algorithm. The actuating mechanism of attitude control system consists of three working reaction flywheels and one spare reaction flywheel. The speed measurement of reaction flywheel is adopted for fault detection. If any reaction flywheel fault is detected, the corresponding fault flywheel is isolated and the spare reaction flywheel is activated to counteract the fault effect and ensure that the satellite is working safely and reliably. The active disturbance rejection approach is employed to design the controller, which handles input information with tracking differentiator, estimates system uncertainties with extended state observer, and generates control variables by state feedback and compensation. The designed active disturbance rejection controller is robust to both internal dynamics and external disturbances. The bandwidth parameter of extended state observer is optimized by the artificial bee colony algorithm so as to improve the performance of attitude control system. A series of simulation experiment results demonstrate the performance superiorities of the proposed robust fault-tolerant control algorithm. [ABSTRACT FROM AUTHOR]

Noncooperative target spacecrafts are those assets in orbit that cannot convey any information about their states (position, attitude, and velocities) or facilitate rendezvous and docking/berthing (RVD/B) process. Designing a guidance, navigation, and control (GNC) module for the chaser in a RVD/B mission with noncooperative target should be inevitably solved for on-orbit servicing technologies. The proximity operations and the guidance for achieving rendezvous problems are addressed in this paper. The out-of- plane maneuvers of proximity operations are explored with distinct subphases, including a chaser far approach in the target's orbit to the first hold point and a closer approach to the final berthing location. Accordingly, guidance solutions are chosen for each subphase from the standard Hill based Closhessy-Willtshire (CW) solution, elliptical fly-around, and Glideslope algorithms. The control is based on a linear quadratic regulator approach (LQR). At the final berthing location, attitude tracker based on a proportional derivative (PD) form is tested to synchronize the chaser and target attitudes. The paper analyzes the performance of both controllers in terms of the tracking ability and the robustness. Finally, it prescribes any restrictions that may be imposed on the guidance during any subphase which can help to improve the controllers tracking ability. [ABSTRACT FROM AUTHOR]

With the uncertainty probability distribution, we establish the worst-case CVaR (WCCVaR) risk measure and discuss a robust portfolio selection problem with WCCVaR constraint. The explicit solution, instead of numerical solution, is found and two-fund separation is proved. The comparison of efficient frontier with mean-variance model is discussed and finally we give numerical comparison with VaR model and equally weighted strategy. The numerical findings indicate that the proposed WCCVaR model has relatively smaller risk and greater return and relatively higher accumulative wealth than VaR model and equally weighted strategy. [ABSTRACT FROM AUTHOR]

This paper is concerned with control synthesis of uncertain Roesser-type discrete-time two-dimensional (2D) systems. The mathematical model of the 2D system's parameter uncertainty, which may appear typically in many actual environment, is modeled as a convex bounded uncertain domain. By using the Lyapunov stability theory, stabilization conditions is proposed in with the purpose of ensuring the robust asymptotical stability of the underlying closed-loop uncertain Roesser-type discrete-time 2D systems. Furthermore, the obtained result of this paper is formulated in the form of linear matrix inequalities (LMIs), which can be easily solved via standard numerical software. Finally, a numerical example is also provided to demonstrate the effectiveness of the proposed result. [ABSTRACT FROM AUTHOR]

This paper investigates the problems of robust stochasticmean square exponential stabilization and robust H∞ for stochastic partial differential time delay systems. Sufficient conditions for the existence of state feedback controllers are proposed, which ensuremean square exponential stability of the resulting closed-loop system and reduce the effect of the disturbance input on the controlled output to a prescribed level of H∞ performance. Alinearmatrix inequality approach is employed to design the desired state feedback controllers. An illustrative example is provided to show the usefulness of the proposed technique. [ABSTRACT FROM AUTHOR]

A new optimization method based on artificial bee colony (ABC) algorithm is presented for solving parameter identification problems. The ABC algorithm as a swarm intelligent optimization algorithm is inspired by honey bee foraging. In this paper, for the first time, the ABC method is developed to determine the optimum parameters of Bouc-Wen hysteretic systems. The proposed method exhibits efficiency, robustness, and insensitivity to noise-corrupted data. The results of the ABC are compared with those other optimization algorithms from the literature to show the efficiency of this technique for solving parameter identification problems. [ABSTRACT FROM AUTHOR]

This paper considers the robust fault detection and isolation (FDI) problem for a class of nonlinear networked systems (NSs) with randomly occurring quantisations (ROQs). After vector augmentation, Lyapunov function is introduced to ensure the asymptotically mean-square stability of fault detection system. By transforming the quantisation effects into sector-bounded parameter uncertainties, sufficient conditions ensuring the existence of fault detection filter are proposed, which can reduce the difference between output residuals and fault signals as small as possible under H∞ framework. Finally, an example linearized from a vehicle system is introduced to show the efficiency of the proposed fault detection filter. [ABSTRACT FROM AUTHOR]

The robust nonlinear dynamic inversion (RNDI) control technique is proposed to keep the relative position of spacecrafts while formation flying. The proposed RNDI control method is based on nonlinear dynamic inversion (NDI). NDI is nonlinear control method that replaces the original dynamics into the user-selected desired dynamics. Because NDI removes nonlinearities in the model by inverting the original dynamics directly, it also eliminates the need of designing suitable controllers for each equilibrium point; that is, NDI works as self-scheduled controller. Removing the original model also provides advantages of ease to satisfy the specific requirements by simply handling desired dynamics. Therefore, NDI is simple and has many similarities to classical control. In real applications, however, it is difficult to achieve perfect cancellation of the original dynamics due to uncertainties that lead to performance degradation and even make the system unstable. This paper proposes robustness assurance method for NDI. The proposed RNDI is designed by combining NDI and sliding mode control (SMC). SMC is inherently robust using high speed switching inputs. This paper verifies similarities of NDI and SMC, firstly. And then RNDI control method is proposed. The performance of the proposed method is evaluated by simulations applied to spacecraft formation flying problem. [ABSTRACT FROM AUTHOR]

The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA). Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions. [ABSTRACT FROM AUTHOR]

In this study, design of a delay-dependent type moving horizon state-feedback control (MHHC) is considered for a class of linear discrete-time system subject to time-varying state delays, norm-bounded uncertainties, and disturbances with bounded energies. The closed-loop robust stability and robust performance problems are considered to overcome the instability and poor disturbance rejection performance due to the existence of parametric uncertainties and time-delay appeared in the system dynamics. Utilizing a discrete-time Lyapunov-Krasovskii functional, some delay-dependent linear matrix inequality (LMI) based conditions are provided. It is shown that if one can find a feasible solution set for these LMI conditions iteratively at each step of run-time, then we can construct a control law which guarantees the closed-loop asymptotic stability, maximum disturbance rejection performance, and closed-loop dissipativity in view of the actuator limitations. Two numerical examples with simulations on a nominal and uncertain discrete-time, time-delayed systems, are presented at the end, in order to demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

A robust filtering problem is formulated and investigated for a class of nonlinear systems with correlated noises, packet losses, and multiplicative noises. The packet losses are assumed to be independent Bernoulli random variables. The multiplicative noises are described as random variables with bounded variance. Different from the traditional robust filter based on the assumption that the process noises are uncorrelated with the measurement noises, the objective of the addressed robust filtering problem is to design a recursive filter such that, for packet losses and multiplicative noises, the state prediction and filtering covariance matrices have the optimized upper bounds in the case that there are correlated process and measurement noises. Two examples are used to illustrate the effectiveness of the proposed filter. [ABSTRACT FROM AUTHOR]

To benefit from the development of compressive sensing, we cast tracking as a sparse approximation problem in a particle filter framework based on multifeatures. In this framework, the target template is composed of multiple features extracted from visible and infrared frames; in addition, occlusion, interruption, and noises are addressed through a set of trivial templates. With this model, the sparsity is achieved via a compressive sensing approach without nonnegative constraints; then the residual between sparsity representation and the compressed sensing observation is used to measure the likelihood which weights particles. After that, the target template is adaptively updated according to the Bhattacharyya coefficients. Some experimental results demonstrate that the proposed tracker appears to have better robustness compared with four different algorithms. [ABSTRACT FROM AUTHOR]

The representation based classification method (RBCM) has shown huge potential for face recognition since it first emerged. Linear regression classification (LRC) method and collaborative representation classification (CRC) method are two well-known RBCMs. LRC and CRC exploit training samples of each class and all the training samples to represent the testing sample, respectively, and subsequently conduct classification on the basis of the representation residual. LRC method can be viewed as a "locality representation" method because it just uses the training samples of each class to represent the testing sample and it cannot embody the effectiveness of the "globality representation." On the contrary, it seems that CRC method cannot own the benefit of locality of the general RBCM. Thus we propose to integrate CRC and LRC to perform more robust representation based classification. The experimental results on benchmark face databases substantially demonstrate that the proposed method achieves high classification accuracy. [ABSTRACT FROM AUTHOR]

Textual information embedded inmultimedia can provide a vital tool for indexing and retrieval. A lot of work is done in the field of text localization and detection because of its very fundamental importance. One of the biggest challenges of text detection is to deal with variation in font sizes and image resolution. This problem gets elevated due to the under segmentation or over segmentation of the regions in an image. The paper addresses this problem by proposing a solution using novel fuzzy-based method. This paper advocates post processing segmentation method that can solve the problem of variation in text sizes and image resolution. The methodology is tested on ICDAR 2011 Robust Reading Challenge dataset which amply proves the strength of the recommended method. [ABSTRACT FROM AUTHOR]

For blended data, the robustness of extreme learning machine (ELM) is so weak because the coefficients (weights and biases) of hidden nodes are set randomly and the noisy data exert a negative effect. To solve this problem, a new framework called "RMSEELM" is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different ensemble groups concurrently and then employs selective ensemble approach to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble approach is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirmthe robustness of our new approach in two key aspects (mean square error and standard deviation). The space complexity of ourmethod is increased to some degree, but the result has shown that RMSE-ELM significantly improves robustness with a rapid learning speed compared to representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP, and E-GASEN). It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future. [ABSTRACT FROM AUTHOR]

A two-layer robust control scheme is proposed to get a better response ability for emergency maneuvers of helicopter. Note that the power used in ascending flight is the main coupling between helicopter and its turboshaft engines; therefore vertical flight control is separated from conventional helicopter control loops and combined with fuel flow and turbine bleeding to new control loops denoted as an inner layer, whereas the mission level flight control is as the out layer. A conclusion in global asymptotically tracking for devising this newscheme is firstly derived from a Generalized Gronwall-Bellman approach. Due to this integrated designing, not only is the helicopter better controlled, but also much better power rapid tracking is realized for engines. Simulations are conducted to validate the new scheme in emergent ascending and descending flights, and the results illustrate that the response time of the closed-loop system is dramatically reduced when compared to the traditional one. Moreover, the presented system also has better dynamic performance under inferences. [ABSTRACT FROM AUTHOR]

In this work an anaerobic digester is controlled using input-output linearization and Lyapunov-like function methods. It is assumed that model parameters are unknown, time-varying, and bounded, and upper or lower bounds are also unknown. To tackle the effect of input saturation, a state observer is designed. The tracking and observer errors are defined in terms of the noisy measured output instead of ideal output, given by the mathematical model. The design of the observer mechanism and the update laws is based on the Lyapunov-like function technique, whereas the design of the control law is based on the input-output linearization method. In this paper two important properties of the controlled system are proven. First, the observer error converges asymptotically to a residual set whose size is user-defined, and such convergence is not disrupted, neither by the input saturation nor by the parameter uncertainties. Second, when the control input is nonsaturated the tracking error converges to a residual set whose size is user-defined. The model parameter uncertainties are included to prove the convergence of errors. Finally, a numerical example to validate the developed control is presented. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a differential braking rollover mitigation strategy for wheeled vehicles. The strategy makes use of a polytopic (piecewise linear) description of the vehicle and includes translational and rotational dynamics, as well as suspension effects. The braking controller is robust and the system states are predicted to estimate the rollover risk up to a given time horizon. In contrast to existing works, the switched predictive nature of the control allows it to be applied only when risk of rollover is foreseen, interfering a minimum with driver's actions. The stability of the strategy is analyzed and its robustness is illustrated via numerical simulations using CarSim for a variety of vehicles. [ABSTRACT FROM AUTHOR]

Attitude stability analysis and robust control algorithms for spacecrafts orbiting irregular asteroids are investigated in the presence of model uncertainties and external disturbances. Rigid spacecraft nonlinear attitude models are considered and detailed attitude stability analysis of spacecraft subjected to the gravity gradient torque in an irregular central gravity field is included in retrograde orbits and direct orbits using linearized system model. The robust adaptive backstepping sliding mode control laws are designed to make the attitude of the spacecrafts stabilized and responded accurately to the expectation in the presence of disturbances and parametric uncertainties. Numerical simulations are included to illustrate the spacecraft performance obtained using the proposed control laws. [ABSTRACT FROM AUTHOR]

Considering the limited communications conditions such as delays, disturbances, and topologies uncertainties, the stability criteria for robust consensus of multiagent systems are proposed in this paper. Firstly, by using the idea of state decomposition and space transformation, the condition for guaranteeing consensus is converted into verifying the robust stability of the disagreement system. In order to deal with multiple time-varying delays and switching topologies, jointly quadratic common Lyapunov- Krasovskii (JQCLK) functional is built to analyze the robust stability. Then, the numerical criterion can be obtained through solving the corresponding feasible nonlinear matrix inequality (NLMI); at last, nonlinear minimization is used like solving cone complementarity problem. Therefore, the linearmatrix inequality (LMI) criterion is obtained, which can be solved by mathematical toolbox conveniently. In order to relax the conservativeness, free-weighting matrices (FWM) method is employed. Further, the conclusion is extended to the case of strongly connected topologies. Numerical examples and simulation results are given to demonstrate the effectiveness and the benefit on reducing conservativeness of the proposed criteria. [ABSTRACT FROM AUTHOR]

Based on the independently developed five-speed dry dual clutch transmission (DDCT), the paper proposes the torque coordinating control strategy between engine and two clutches, which obtains engine speed and clutch transferred torque in the shifting process, adequately reflecting the driver intention and improving the shifting quality. Five-degree-of-freedom (DOF) shifting dynamics model of DDCT with single intermediate shaft is firstly established according to its physical characteristics. Then the quantitative control objectives of the shifting process are presented. The fuzzy decision of shifting time and the model-based torque coordinating control strategy are proposed and also verified by simulating under different driving intentions in up-/downshifting processes with the DCT model established on the MATLAB/Simulink. Simulation results validate that the shifting control algorithm proposed in this paper can not only meet the shifting quality requirements, but also adapt to the various shifting intentions, having a strong robustness. [ABSTRACT FROM AUTHOR]

The sliding mode control has the merits with respect to the variation of the disturbance and robustness. In this paper, the sensorless sliding-mode observer with least mean squared error approach for permanent magnet synchronous motor (PMSM) to detect the rotor position by counter electromotive force and then compute motor speed is designed and implemented. In addition, the neural network control is also used to compensate the PI gain tuning to increase the speed accuracy without regarding the errors of the current measurement and motor noise. In this paper, a digital signal processor TMS320F2812 utilizes its high-speed ADC module to get current feedback information and thus to estimate the rotor position and takes advantage of the built-in modules to achieve SVPWM current control so that the senseless speed control will be accomplished. The correctness and effectiveness of the proposed control system will be verified from the experimental results. [ABSTRACT FROM AUTHOR]

Robustness of a network in the presence of node or link failures plays an important role in the design of the network. A key factor that quantifies this robustness is the algebraic connectivity of the network. In this paper, the authors address the problem of finding a network that maximizes the algebraic connectivity of the network while ensuring that the length of the shortest path joining any two nodes in the network is within a given bound. This paper presents k-opt and tabu search heuristics for finding feasible solutions for this network synthesis problem. Computational results are also presented to corroborate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

A new queue length information fused iterative learning control approach (QLIF-ILC) is presented for freeway traffic rampmetering to achieve a better performance by utilizing the error information of the on-ramp queue length. The QLIF-ILC consists of two parts, where the iterative feed forward part updates the control input signal by learning from the past control data in previous trials, and the current feedback part utilizes the tracking error of the current learning iteration to stabilize the controlled plant. These two parts are combined in a complementary manner to enhance the robustness of the proposed QLIF-ILC. A systematic approach is developed to analyze the convergence and robustness of the proposed learning scheme. The simulation results are further given to demonstrate the effectiveness of the proposed QLIF-ILC. [ABSTRACT FROM AUTHOR]

The paper mainly investigates the H∞ fuzzy control problem for a class of nonlinear discrete-time stochastic systems with Markovian jump and parametric uncertainties. The class of systems is modeled by a state space Takagi-Sugeno (T-S) fuzzy model that has linear nominal parts and norm-bounded parameter uncertainties in the state and output equations. An H∞ control design method is developed by using the Lyapunov function. The decoupling technique makes the Lyapunov matrices and the system matrices separated, which makes the control design feasible. Then, some strict linear matrix inequalities are derived on robust H∞ norm conditions in which both robust stability and H∞ performance are required to be achieved. Finally, a computer-simulated truck-trailer example is given to verify the feasibility and effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

This paper proposes a multiobjective portfolio selection problem with most probable random distribution derived from current market data and other random distributions of boomand recession under the risk-controlled parameters determined by an investor. The current market data and information include not only historical data but also interpretations of economists' oral and linguistic information, and hence, the boom and recession are often caused by these nonnumeric data. Therefore, investors need to consider several situations from most probable condition to boom and recession and to avoid the risk less than the target return in each situation. Furthermore, it is generally difficult to set random distributions of these cases exactly. Therefore, a robust-based approach for portfolio selection problems using the onlymean values and variances of securities is proposed as a multiobjective programming problem. In addition, an exact algorithm is developed to obtain an explicit optimal portfolio using a principle of compromise. [ABSTRACT FROM AUTHOR]

An adaptive robust fault tolerant control approach is proposed for a class of uncertain nonlinear systems with unknown signs of high-frequency gain and unmeasured states. In the recursive design, neural networks are employed to approximate the unknown nonlinear functions, K-filters are designed to estimate the unmeasured states, and a dynamical signal and Nussbaumgain functions are introduced to handle the unknown sign of the virtual control direction. By incorporating the switching function σ algorithm, the adaptive back stepping scheme developed in this paper does not require the real value of the actuator failure. It is mathematically proved that the proposed adaptive robust fault tolerant control approach can guarantee that all the signals of the closed-loop system are bounded, and the output converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by the simulation examples. [ABSTRACT FROM AUTHOR]

We study distributed detection and fusion in sensor networks with bathtub-shaped failure (BSF) rate of the sensorswhichmay or not send data to the Fusion Center (FC). The reliability of semiconductor devices is usually represented by the failure rate curve (called the "bathtub curve"), which can be divided into the three following regions: initial failure period, randomfailure period, and wearout failure period. Considering the possibility of the failed sensors which still work but in a bad situation, it is unreasonable to trust the data from these sensors. Based on the above situation, we bring in new characteristics to failed sensors. Each sensor quantizes its local observation into one bit of information which is sent to the FC for overall fusion because of power, communication, and bandwidth constraints. Under this sensor failure model, the Extension Log-likelihood Ratio Test (ELRT) rule is derived. Finally, the ROC curve for this model is presented. The simulation results show that the ELRT rule improves the robust performance of the system, compared with the traditional fusion rule without considering sensor failures. [ABSTRACT FROM AUTHOR]

As to strong nonlinearity of doubly fed induction generators (DFIG) and uncertainty of its model, a novel rotor current controller with nonlinearity and robustness is proposed to enhance fault ride-though (FRT) capacities of grid-connected DFIG. Firstly, the model error, external disturbances, and the uncertain factors were estimated by constructing extended state observer (ESO) so as to achieve linearization model, which is compensated dynamically from nonlinear model. And then rotor current controller of DFIG is designed by using terminal sliding mode variable structure control theory (TSMC). The controller has superior dynamic performance and strong robustness. The simulation results show that the proposed control approach is effective. [ABSTRACT FROM AUTHOR]

How to effectively detect object and accurately give out its visible parts is a major challenge for object detection. In this paper we propose an explicit occlusion model through integrating appearance and motion information. The model combines together two parts: part-level object detection with single frame and object occlusion estimation with continuous frames. It breaks through the performance bottleneck caused by lack of information and effectively improves object detection rate under severe occlusion. Through reevaluating the semantic parts, the detecting performance of partial object detectors is largely enhanced. The explicit model enables the partial detectors to have the capability of occlusion estimation. By discarding the geometric representation in rigid single-angle perspective and applying effective pattern of objective shape, our proposed approaches greatly improve the performance and robustness of similarity measurement. For validating the performance of proposed methods, we designed a comparative experiment on challenging pedestrian frame sequences database. The experimental results on challenging pedestrian frame sequence demonstrate that, compared to the traditional algorithms, the methods proposed in this paper have significantly improved the detection rate for severe occlusion. Furthermore, it also can achieve better localization of semantic parts and estimation of occluding. [ABSTRACT FROM AUTHOR]

This paper is concerned with the predictive control of water level in an experimental pilot plant, in the presence of input constraints and uncertain time delays. Robustness is achieved by casting the delay uncertainty into a polytopic form and using a predictive control formulation based on linear matrix inequalities. Integral action is introduced into the controller to ensure offset-free tracking of step changes in the set point. Moreover, an integrator resetting procedure based on the concept of regions of guaranteed cost is proposed to improve the resulting transient response. The results show that the introduction of robustness into the predictive control formulation is indeed of value to avoid closed-loop instability in the presence of time delays. In addition, the integrator resetting procedure was found to provide substantial performance improvements in terms of overshoot and settling time. [ABSTRACT FROM AUTHOR]

A robust adaptive control method with full-state feedback is proposed based on the fact that the elbow joint of a seven-function hydraulic manipulator with double-screw-pair transmission features the following control characteristics: a strongly nonlinear hydraulic system, parameter uncertainties susceptible to temperature and pressure changes of the external environment, and unknown outer disturbances. Combined with the design method of the back-stepping controller, the asymptotic stability of the control system in the presence of disturbances from uncertain systematic parameters and unknown external disturbances was demonstrated using Lyapunov stability theory. Based on the elbow joint of the seven-function master-slave hydraulic manipulator for the 4500m Deep-Sea Working System as the research subject, a comparative study was conducted using the control method presented in this paper for unknown external disturbances. Simulations and experiments of different unknown outer disturbances showed that (1) the proposed controller could robustly track the desired reference trajectory with satisfactory dynamic performance and steady accuracy and that (2) the modified parameter adaptive laws could also guarantee that the estimated parameters are bounded. [ABSTRACT FROM AUTHOR]

Personalized recommendation systems have been widely used as an effective way to deal with information overload. The common approach in the systems, item-based collaborative filtering (CF), has been identified to be vulnerable to "Shilling" attack. To improve the robustness of item-based CF, the authors propose a novel CF approach based on the mostly used relationships between users. In the paper, three most commonly used relationships between users are analyzed and applied to construct several user models at first. The DBSCAN clustering is then utilized to select the valid user model in accordance with how the models benefit detecting spam users. The selected model is used to detect spam user group. Finally, a detection-based CF method is proposed for the calculation of item-item similarities and rating prediction, by setting different weights for suspicious spam users and normal users. The experimental results demonstrate that the proposed approach provides a better robustness than the typical item-based kNN (k Nearest Neighbor) CF approach. [ABSTRACT FROM AUTHOR]