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This paper proposes a novel robust tracking control scheme for discrete time linear uncertain Multiple-Input Multiple-Output (MIMO) systems subject to time-varying delay on the states. The considered system is affected by unknown but norm bounded uncertainties on parameters as well as matched disturbances on the states. The designed controller is based upon a proposed novel integral sliding surface and a new switching type of reaching law. Sufficient conditions based on Linear Matrix Inequalities (LMIs) and a suitable Lyapunov–Krasovskii Functional (LKF) are derived in order to guarantee the asymptotic stability of such system. The proposed controller ensures a good tracking performance despite the presence of the time varying delay and the matched/unmatched disturbances. Moreover and thanks to the proposed integral surface, the time reaching phase is eliminated and the chattering phenomenon is significantly reduced. The proposed controller is applied on an Autonomous Underwater Vehicle (AUV) to follow a prescribed desired trajectory. The simulation results illustrate the effectiveness of such controller. • The problem of the design of a robust discrete time integral sliding mode controllers for the tracking of an uncertain MIMO systems subject to time-varying delay on the states, and under the presence of matched and unmatched uncertainties has never been investigated before anyway. • Robustness against uncertainties is tackled in the present paper by considering the case of matched disturbances and unmatched Unknown But Norm Bounded (UBB) uncertainties. • In the current study we propose a novel discrete switching type reaching law, which is an extension of the one presented in Bartoszewicz and Latosinski (2014) to the MIMO case in the presence of delays, matched disturbances and unmatched uncertainties as well. • Based on a Lyapunov–Krasovskii Functional, the present work provides a sufficient condition guaranteeing the asymptotic stability of the closed-loop systems. With regard to that condition a new Integral Sliding Surface (ISS) has been proposed, which by canceling the reaching phase, it improves the performances of the designed controllers. • Discrete sliding mode controllers have been designed to ensure the occurrence of the sliding mode in finite time. One of these controllers lessens the chattering phenomenon. • The effectiveness of the proposed control algorithms are tested on an Autonomous Underwater Vehicle (AUV). The simulation results are promising. [ABSTRACT FROM AUTHOR]

Geodetic coordinate information and attitude information of the observation platform are necessary for multi-UAV position alignment and target tracking. In a complex sea environment, the navigation equipment of a UAV is susceptible to interference. High-precision geodetic coordinate information and attitude information are difficult to obtain. Aiming to solve the above problems, a low-precision geodetic coordinate real-time systematic spatial registration algorithm based on multi-UAV observation and an improved robust fusion tracking algorithm of multi-UAV to sea targets considering attitude error are proposed. The spatial registration algorithm obtains the observation information of the same target based on the mutual observation information. Then, geodetic coordinate systematic error is accurately estimated by establishing the systematic error estimation measurement equation. The improved robust fusion tracking algorithm considers the influence of UAV attitude error in the observation. The simulation experiment and practical experiment show that the algorithm can not only estimate systematic error accurately but also improve tracking accuracy. [ABSTRACT FROM AUTHOR]

This paper addresses a problem of optimal error quantification in the framework of robust control theory in the 1 setup. The upper bounds of biased external disturbance and the gains of coprime factor perturbations in a discrete-time linear time invariant SISO plant are assumed to be unknown. The computation of optimal data-consistent upper bounds under a known bias of external disturbance has been simplified to linear programming. This allows for the computation of optimal estimates in real-time and their application to achieve optimal robust steady-state tracking even when facing an unknown bias in the external disturbance. The presented results have been illustrated through computer simulations. [ABSTRACT FROM AUTHOR]

This paper presents a new nonlinear control scheme for a pulse-width modulated dc-dc Zeta converter operating in buck and boost modes. The averaged model of the dc-dc power converter is derived, based on which a robust control law is developed using a simplified sliding-mode control technique. The existence and stability conditions are introduced to select proper controller gains that ensure fast output voltage convergence towards reference voltage. A detailed design procedure is provided to realize the control scheme using low-cost discrete components. The proposed control method handles large disturbances, accommodates the non-minimum phase property, and maintains regulated output voltage during step-up and step-down operation modes. The control system also maintains constant switching frequency, improves the transient response, and eliminates the steady-state error at the output voltage. A MATLAB/SIMULINK model is developed to simulate the closed-loop dc-dc Zeta converter in continuous conduction mode and investigate the tracking and regulation performance. The simulation results confirm the robustness and stability of the nonlinear controlled power converter under abrupt line and load variations. [ABSTRACT FROM AUTHOR]

Geodetic coordinate information and attitude information of the observation platform are necessary for multi-UAV position alignment and target tracking. In a complex sea environment, the navigation equipment of a UAV is susceptible to interference. High-precision geodetic coordinate information and attitude information are difficult to obtain. Aiming to solve the above problems, a low-precision geodetic coordinate real-time systematic spatial registration algorithm based on multi-UAV observation and an improved robust fusion tracking algorithm of multi-UAV to sea targets considering attitude error are proposed. The spatial registration algorithm obtains the observation information of the same target based on the mutual observation information. Then, geodetic coordinate systematic error is accurately estimated by establishing the systematic error estimation measurement equation. The improved robust fusion tracking algorithm considers the influence of UAV attitude error in the observation. The simulation experiment and practical experiment show that the algorithm can not only estimate systematic error accurately but also improve tracking accuracy.

This paper addresses a problem of optimal error quantification in the framework of robust control theory in the 𝓁1 setup. The upper bounds of biased external disturbance and the gains of coprime factor perturbations in a discrete-time linear time invariant SISO plant are assumed to be unknown. The computation of optimal data-consistent upper bounds under a known bias of external disturbance has been simplified to linear programming. This allows for the computation of optimal estimates in real-time and their application to achieve optimal robust steady-state tracking even when facing an unknown bias in the external disturbance. The presented results have been illustrated through computer simulations.

The AI-aided variational Bayesian extended Kalman filter (AI-VBEKF)-based robust direction-of-arrival (DOA) technique is proposed to make reliable estimations of the bearing angle of an uncooperative underwater target with uncertain environment noise. Considering that the large error of the guess of the initial mean square error matrix (MSEM) will lead to inaccurate DOA tracking results, an attention-based deep convolutional neural network is first proposed to make reliable estimations of the initial MSEM. Then, by utilizing the AI-VBEKF estimating scheme, the uncertain measurement noise caused by the unknown underwater environment along with the bearing angle of the target can be estimated simultaneously to provide reliable results at every DOA tracking step. The proposed technique is demonstrated and verified by both of the simulations and the real sea trial data from the South China Sea in July 2021, and both the robustness and accuracy are proven superior to the traditional DOA-estimating methods. [ABSTRACT FROM AUTHOR]

This study proposes a composite nonlinear feedback approach for the robust tracking control problem of uncertain nonlinear systems with input saturation, Lipschitz nonlinear functions, multivariable time-delays, and disturbances. The composite nonlinear feedback technique includes two components: a linear feedback portion constructed in such a way that it changes the damping ratio so as to speed up the system’s response. A nonlinear feedback controller is designed to further increment the damping ratio in a way that it ensures the tracking whilst reducing the overshoot created by the linear portion. By creating a suitable Lyapunov functional and by using the linear matrix inequality (LMI) approach, the LMI conditions are determined to guarantee system stability and obtain the required design parameters. The performance of the proposed approach is assessed using a simulation study of a two-dimensional system along with a Chua’s circuit system. The advantages of the proposed approach are its less-restrictive assumptions, improved transient performance, and steady-state precision.

This study proposes an observer-based Composite Nonlinear Feedback (CNF) controller for the robust tracking of uncertain singular systems with input saturation, nonlinear function, time-delay, and disturbances. The suggested control law is designed based states reconstructed using a singular observer so as to increase steady-state accuracy and improve robustness. The CNF controller is developed based on Generalized Riccati Equations (GRE) and Lyapunov–Krasovskii functional. Additionally, the proposed theorem verifies the stability conditions of the system in the presence of uncertainties and disturbances. Among the advantages of this method, are its fewer restrictive assumptions, transient and high-speed performance improvement and steady-state precision. The uniform boundedness of the tracking error in the presence of the input saturation and external disturbancesis also a prominent feature of this method. The performance of the proposed approach is assessed using a simulation study.

This paper presents a new nonlinear control scheme for a pulse-width modulated dc-dc Zeta converter operating in buck and boost modes. The averaged model of the dc-dc power converter is derived, based on which a robust control law is developed using a simplified sliding-mode control technique. The existence and stability conditions are introduced to select proper controller gains that ensure fast output voltage convergence towards reference voltage. A detailed design procedure is provided to realize the control scheme using low-cost discrete components. The proposed control method handles large disturbances, accommodates the non-minimum phase property, and maintains regulated output voltage during step-up and step-down operation modes. The control system also maintains constant switching frequency, improves the transient response, and eliminates the steady-state error at the output voltage. A MATLAB/SIMULINK model is developed to simulate the closed-loop dc-dc Zeta converter in continuous conduction mode and investigate the tracking and regulation performance. The simulation results confirm the robustness and stability of the nonlinear controlled power converter under abrupt line and load variations.

Precise and robust feature extraction plays a key role in high-performance tracking to analyse the videos from drones, surveillance and automatic driving, etc. However, most existing Siamese network-based trackers mainly focus on constructing complicated network models and refinement strategies, while using comparatively simple and heuristic conventional or deformable convolutions to extract features from the sampling positions that may be far away from a target region. Consequently, the coarsely extracted features may introduce background noise and degrade the tracking performance. To address this issue, we present a propose-and-refine tracker (PRTracker) that combines anchor-free style proposals at the coarse level, and alignment convolution-driven refinement at the fine level. Specifically, at the coarse level, we design an anchor-free model to effectively generate proposals that provide more reliable interested regions for further verifying. At the fine level, an alignment convolution-based refinement strategy is adopted to improve the convolutional sampling positions of the proposals, thus making the classification and regression of them more accurate. Through using alignment convolution, the convolution sampling positions of the proposals can be efficiently and effectively re-localized, thus improving the accuracy of the extracted features. Finally, a simple yet robust target mask is designed to make full use of the initial state of a target to further improve the tracking performance. The proposed PRTracker achieves a competitive performance against six tracking benchmarks (i.e., UAV123, VOT2018, VOT2019, OTB100, NfS and LaSOT) at 75 FPS.

This paper develops a systematic method to design robust tracking controllers for multi-input multi-output (MIMO) uncertain discrete-time systems with bounded parametric uncertainties, in particular of rational multi-affine type, and generic discrete reference signals with bounded first or second discrete derivatives, also in presence of generic disturbances with bounded first or second discrete derivatives. Theoretical tools and systematic methodologies are provided to effectively design robust innovative controllers for the considered systems. Applicability and efficiency of the proposed methods are validated in two examples via simulation and experimental tests.

The AI-aided variational Bayesian extended Kalman filter (AI-VBEKF)-based robust direction-of-arrival (DOA) technique is proposed to make reliable estimations of the bearing angle of an uncooperative underwater target with uncertain environment noise. Considering that the large error of the guess of the initial mean square error matrix (MSEM) will lead to inaccurate DOA tracking results, an attention-based deep convolutional neural network is first proposed to make reliable estimations of the initial MSEM. Then, by utilizing the AI-VBEKF estimating scheme, the uncertain measurement noise caused by the unknown underwater environment along with the bearing angle of the target can be estimated simultaneously to provide reliable results at every DOA tracking step. The proposed technique is demonstrated and verified by both of the simulations and the real sea trial data from the South China Sea in July 2021, and both the robustness and accuracy are proven superior to the traditional DOA-estimating methods.

A ferromagnetic vehicle, such as a submarine, magnetized by the Earth’s magnetic field produces a magnetic anomaly field, and the tracking of moving targets can be realized through real-time analysis of magnetic data. At present, there are few tracking methods based on magnetic field vectors and their gradient tensor. In this paper, the magnetic field vector and its gradient tensor are used to calculate equivalent magnetic force. It shows the direction of the vector between the detector and the tracking targets for controlling the direction of motion of the detector and achieving the purpose of tracking. Compared with existing positioning methods, the proposed method is relatively less affected by instrument resolution and noise and maintains robustness when the velocity vectors of multiple magnetic targets change randomly.

Summary: Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals move in 3D. Here, we introduce Anipose, an open-source toolkit for robust markerless 3D pose estimation. Anipose is built on the 2D tracking method DeepLabCut, so users can expand their existing experimental setups to obtain accurate 3D tracking. It consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial regularization, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on a calibration board as well as mice, flies, and humans. By analyzing 3D leg kinematics tracked with Anipose, we identify a key role for joint rotation in motor control of fly walking. To help users get started with 3D tracking, we provide tutorials and documentation at http://anipose.org/.

A robust multi-Bernoulli filter based on the amplitude information of the target in sea clutter was proposed,in which the amplitude information of the target and sea clutter was exploited to improve the measurement likelihood.Therefore,the proposed approach could distinguish targets from clutter better when they were spatially close,which further enhanced the ability to track targets in marine radar detection applications.The simulation results of Swerling type Ⅰfluctuating targets tracking in K distributed sea clutter show that,the proposed algorithm is superior in performance to the robust multi-Bernoulli filter in terms of state estimation,cardinality estimation,as well as clutter rate estimation.

In this study, an optimal adaptive control approach is established to solve the robust output tracking problem of a class of continuous time uncertain linear systems based on the policy iteration (PI) in actor-critic algorithm. First, by augmenting the integral variables of the tracking error into state variables, the robust tracking problem is transformed into a robust control problem of an augmented uncertain linear system. It is proven that the robust control law of the augmented system enables the output of the considered system to track a polynomial time signal asymptotically. Second, an optimal control method in the corresponding auxiliary nominal system is established, and based on the Bellman optimality principle, PI algorithms are proposed to solve online tracking controllers for the matched and the mismatched uncertain systems. Finally, for testing the availability of the proposed approach and theoretical results, two numerical experiments are provided. [ABSTRACT FROM AUTHOR]

A tracking problem is considered for a very recurring class of systems, such as Cartesian robots with real actuators, conveyor belts, and certain scanning devices used for medical and engineering applications, as near-field antenna characterization. Theorems are proven for the design of a PID controller with a possible compensation signal to track sufficiently regular trajectories with a prescribed maximum error. The developed design methodology is used to identify the current antenna scanning system without a controller and to design and construct a new controller that provides better performance than the current one. Moreover, this paper proposes an optimized raster scan acquisition scheme that reduces the number of field samples and the scanning path length compared with the more conventional approaches. By using the new controller and the proposed optimized sampling strategy, which provides a sparse distribution of the samples, the performance of an antenna can be evaluated in a considerably shorter time than that necessary using the pre-existing controller and standard scanning, as experimentally assessed in this paper.

Ionospheric scintillation is the physical phenomena affecting radio waves coming from the space through the ionosphere. Such disturbance is caused by ionospheric electron‐density irregularities and is a major threat in Global Navigation Satellite Systems (GNSS). From a signal‐processing perspective, scintillation is one of the most challenging propagation scenarios, particularly affecting high‐precision GNSS receivers and safety critical applications where accuracy, availability, continuity, and integrity are mandatory. Under scintillation, GNSS signals are affected by amplitude and phase variations, which mainly compromise the synchronization stage of the receiver. To counteract these effects, one must resort to advanced signal‐processing techniques such as adaptive/robust methods, machine learning, or parameter estimation. This contribution reviews the signal‐processing landscape in GNSS receivers, with emphasis on different detection, monitoring, and mitigation problems. New results using real data are provided to support the discussion. To conclude, future perspectives of interest to the GNSS community are discussed. [ABSTRACT FROM AUTHOR]

This brief studies robust output regulation in discrete-time singular systems with actuator saturation and matched uncertainties. The singularity of dynamical equations makes the design procedure more complex due to the appearance of both algebraic and difference equations in such systems. In addition to this, the actuator saturation constraint along with uncertain terms complicates the design problem. The idea for robust output regulation is to add an additional control term to the composite nonlinear feedback (CNF) controller which guarantees the output regulation of the uncertain closed-loop singular system with an ultimately bounded tracking error. In this regard, a theorem is given which guarantees the output regulation even if the saturation takes place. The accuracy of the theoretical results is tested through simulations for a practical example. [ABSTRACT FROM AUTHOR]

Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electro-optical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera motions by pan-tilt motor control or dynamic distortions in field environments. Conventional context aware (CA) and deep learning based trackers have been studied to tackle these problems, but they have the drawbacks of not fully overcoming the problems and dealing with their computational burden. In this paper, a global motion aware method is proposed to address the fast camera motion issue. The proposed method consists of two modules: (i) a motion detection module, which is based on the change in image entropy value, and (ii) a background tracking module, used to track a set of features in consecutive images to find correspondences between them and estimate global camera movement. A series of experiments is conducted on thermal infrared images, and the results show that the proposed method can significantly improve the robustness of all trackers with a minimal computational overhead. We show that the proposed method can be easily integrated into any visual tracking framework and can be applied to improve the performance of EOTS applications. [ABSTRACT FROM AUTHOR]

Tracking based on correlation filters has demonstrated outstanding performance in recent visual object tracking studies and competitions. However, the performance is limited since the boundary effects are introduced by the intrinsic circular structure. In this study, a tracker, called adversarial correlation filter tracker (ACFT), is proposed to solve the above problem through Generative Adversarial Networks (GANs) that is specifically strong at producing realistic‐looking data from noise circumstances. Especially, a mask is generated by the GANs to assist the conventional correlation filter for the spatial regularisation. By overcoming the feature independence of current regularisation in another tracker, the GANs' mask can be effectively used to identify the robust features for the target variations representation in the temporal domain. Also in the spatial domain, the background features can be substantially suppressed to obtain the optimisation filter for more reliable matching and updating. In verification, the authors evaluate the proposed tracker on the standard tracking benchmarks, and the experimental results show that their tracker outperforms favourably against other state‐of‐the‐art trackers in the measurements of accuracy and robustness. [ABSTRACT FROM AUTHOR]

This manuscript presents a robust tracking (servomechanism) controller for linear time-invariant (LTI) islanded (autonomous, isolated) microgrid voltage control. The studied microgrid (MG) consists of many distributed energy resources (DERs) units, each using a voltage-sourced converter (VSC) for the interface. The optimal tracker design uses the ellipsoidal approximation to the invariant sets. The MG system is decomposed into different subsystems (DERs). Each subsystem is affected by the rest of the system that is considered as a disturbance to be rejected by the controller. The proposed tracker (state feedback integral control) rejects bounded external disturbances by minimizing the invariant ellipsoids of the MG dynamics. A condition to design decentralized controllers is derived in the form of linear matrix inequalities. The proposed controller is characterized by rapid transient response, and zero error in the steady state. A robustness analysis of the control strategy (against load changes, load unbalances, etc.) is carried out. A MATLAB/SimPowerSystems (R2017b, MathWorks, Natick, MA, USA) simulation of the case study confirm the robustness of the proposed controller.

A robust adaptive integral terminal sliding mode (AITSM) control scheme with an uncertainty observer (UO) is developed for automobile electronic throttle (AET) systems. Distinguished from the ordinary sliding mode (SM)-based AET control systems, by using a novel integral terminal sliding surface (SS) to force the closed-loop system to start on the SS at the very beginning, an excellent error convergence and tracking accuracy as well as robustness of the closed-loop system against uncertainties and nonlinearities including transmission friction, return spring limp-home, and gear backlash can be assured. Also, in order to achieve a stably excellent performance for different tracking commands, the sliding parameters are updated by the designed adaptive laws in Lyapunov sense. Moreover, for reducing the control chattering caused by the large switching gain in conventional SM control, an SM-based UO is introduced to provide with the online estimation for the system lumped uncertainty and conduct the feed-forward compensation in the control design. In addition, model-free robust exact differentiators (MRED) are presented to obtain the accurate angular velocity and acceleration information from the unique angle sensors with measurement noises. Not only the comparative experimental results from a real-time digital signal processor (DSP)-based AET plant but also the quantitive root mean square (RMS) values and the maximum (MAX) values of the sampled tracking errors are demonstrated to validate the superior performance of the proposed control. In particular, in the disturbance rejection test by combining trapezium and sinusoidal reference commands, the proposed control exhibits the smallest RMS error values of 0.6832 deg (37% and 27% of the ones of the nonsingular terminal sliding mode and the proportional-integral-derivative controllers), and the smallest MAX values of 2.9352 deg (67% and 35% of the ones of the comparative controllers, respectively). [ABSTRACT FROM AUTHOR]

Ionospheric scintillation is the physical phenomena affecting radio waves propagating from the space through the ionosphere to earth. The signal distortion induced by scintillation can pose a major threat to some GNSS application. Scintillation is one of the more challenging propagation scenarios, particularly affecting high-precision GNSS receivers which require high quality carrier phase measurements; and safety critical applications which have strict accuracy, availability and integrity requirements. Under ionospheric scintillation conditions, GNSS signals are affected by fast amplitude and phase variations, which can compromise the receiver synchronization. To take into account the underlying correlation among different frequency bands, we propose a new multivariate autoregressive model (MAR) for the multi-frequency ionospheric scintillation process. Multi-frequency GNSS observations and the scintillation MAR are modeled in state-space, allowing independent tracking of both line-of-sight phase variations and complex gain scintillation components. The resulting joint synchronization and scintillation mitigation problem is solved using a robust nonlinear Kalman filter, validated using real multi-frequency scintillation data with encouraging results.

Sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recognition and explore its potential in the analysis of forward-looking infrared (FLIR) video to support nighttime machine vision systems. A key technical contribution of this work is to unify existing sparse coding-based approaches toward tracking and recognition under the same framework, so that they can benefit from each other in a closed-loop. On the one hand, tracking the same object through temporal frames allows us to achieve improved recognition performance through dynamical updating of template/dictionary and combining multiple recognition results; on the other hand, the recognition of individual objects facilitates the tracking of multiple objects (i.e., walking pedestrians), especially in the presence of occlusion within a crowded environment. We report experimental results on both the CASIAPedestrian Database and our own collected FLIR video database to demonstrate the effectiveness of the proposed joint tracking-and-recognition approach.

The micromirror based on micro-electro-mechanical systems (MEMS) technology is widely employed in different areas, such as scanning, imaging and optical switching. This paper studies the MEMS electromagnetic micromirror for scanning or imaging application. In these application scenarios, the micromirror is required to track the command sinusoidal signal, which can be converted to an output regulation problem theoretically. In this paper, based on the internal model principle, the output regulation problem is solved by designing a robust controller that is able to force the micromirror to track the command signal accurately. The proposed controller relies little on the accuracy of the model. Further, the proposed controller is implemented, and its effectiveness is examined by experiments. The experimental results demonstrate that the performance of the proposed controller is satisfying. [ABSTRACT FROM AUTHOR]

The paper presents the robust finite-time tracking of Stewart platform using super-twisting sliding mode observer based forward kinematics solution. The forward kinematics problem—finding the states from the leg length measurements—in Stewart platform gives nonunique solution for any given leg lengths, and due to this it poses difficulties in the control design. The super-twisting observer is designed from the available leg length measurements which is the output of the system to obtain the position and orientation of movable platform and their respective derivatives. The finite-time convergence of this observer and the stability of the closed loop system are presented in detail. It is shown that using this leg length measurements, the states of the observer converge to actual state in finite-time and hence, it provides a solution to the forward kinematics problem. Using these estimated states, a robust finite-time tracking control is designed for the Stewart platform by considering all the uncertainties and parameter variations. However, the proposed method for forward kinematics solution can also be incorporated with any other control strategies. Simulation results are given to demonstrate the performance of the proposed observer for the Stewart platform. [ABSTRACT FROM PUBLISHER]

The problem of maximum power point tracking (MPPT) in photovoltaic (PV) systems, despite the model uncertainties and the variations in environmental circumstances, is addressed. Introducing a mathematical description, an adaptive sliding mode control (ASMC) algorithm is first developed. Unlike many previous investigations, the output voltage is not required to be sensed and the upper bound of system uncertainties and the variations of irradiance and temperature are not required to be known. Estimating the output voltage by an update law, an adaptive-based H ∞ tracking algorithm is then developed for the case the perturbations are energy-bounded. The stability analysis is presented for the proposed tracking control schemes, based on the Lyapunov stability theorem. From a comparison viewpoint, some numerical and experimental studies are also presented and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we develop a novel object tracking method based on sparse representation. First, we propose a relaxed sparse representation model, based on which the tracking problem is casted as an inverse sparse representation process. In this process, the target template is able to be sparsely approximated by all candidate samples. Second, we present an objective function that combines the sparse representation process of different fragments, the relaxed representation scheme and a weight reference prior. Based on some propositions, the proposed objective function can be solved by using an iteration algorithm. In addition, we design a tracking framework based on the proposed representation model and a simple online update manner. Finally, numerous experiments are conducted on some challenging sequences to compare our tracking method with some state-of-the-art ones. Both qualitative and quantitative results demonstrate that the proposed tracking method performs better than other competing algorithms. [ABSTRACT FROM AUTHOR]

Multiple experimental three-degrees-of-freedom (three-DOF) helicopters that are equipped with active disturbance systems constitute an attractive platform to validate robust control strategies. In this paper, a distributed synchronization controller is developed for such a platform, where each helicopter is subjected to unknown model uncertainties and external disturbances, and the desired trajectories are generated online, communicated through a network and not accessible by all helicopters. The controller is composed of a continuous tracker and a continuous uncertainty and disturbance estimator (UDE). The tracker makes the nominal closed-loop system globally asymptotically stable, and the UDE output is used to reject total uncertainties. The conditions that ensure zero-error tracking for each helicopter are identified; for the case with nonzero error, explicit relationship inequalities between the involved design parameters and the ultimate bound of error are revealed. Experimental results of four cases demonstrate improved tracking and synchronization accuracy of using the UDE with small parameters. [ABSTRACT FROM AUTHOR]

This paper considers some control design problems in a power system driven by battery/supercapacitor hybrid energy storage devices. The currents in the battery and the supercapacitor are actively controlled by two bidirectional buck–boost converters. Two control objectives are addressed in this paper: one is to achieve robust tracking of two reference variables, the battery current and the load voltage, the other is to achieve smooth transition of these variables during load switch. Based on the state-space averaged model we newly developed, the control design problems are converted into numerically efficient optimization problems with linear matrix inequality (LMI) constraints. An experimental system is constructed to validate the control design methods. [ABSTRACT FROM AUTHOR]

The disturbance‐observer‐based control (DOBC) is a well‐known robust control scheme and recently its non‐linear version has been developed. Observing that two components (so‐called Q‐filters) in the conventional DOBC structure have the same transfer function, the authors propose a reduced‐order DOBC for uncertain non‐linear systems. It is shown that the proposed controller also guarantees the same robust transient response as those developed before. Numerical simulations using a magnetic levitation system are included to illustrate the detailed design procedure and validate the performance of the proposed controller. [ABSTRACT FROM AUTHOR]

A coefficient ratio‐based sliding surface algorithm and an integral sliding mode control approach are proposed for multivariable dynamical systems. The sliding surface design problem is reduced to the specification of the desired time constant of closed‐loop systems. The proposed scheme is able to accomplish a non‐overshoot transient response and a short settling time for multivariable systems. The resulting sliding surfaces are robust and optimal in the existence of parameter perturbations. An integral sliding mode control approach is also developed for robust tracking by using the coefficient ratio‐based robust sliding surface designs. The developed methods are implemented on a flexible robotic manipulator and a strike aircraft system, and the numerical simulation results are provided in order to show the validity and feasibility of the methods. [ABSTRACT FROM AUTHOR]

Video object tracking is widely used in many real-world applications, and it has been extensively studied for over two decades. However, tracking robustness is still an issue in most existing methods, due to the difficulties with adaptation to environmental or target changes. In order to improve adaptability, this paper formulates the tracking process as a ranking problem, and the PageRank algorithm, which is a well-known webpage ranking algorithm used by Google, is applied. Labeled and unlabeled samples in tracking application are analogous to query webpages and the webpages to be ranked, respectively. Therefore, determining the target is equivalent to finding the unlabeled sample that is the most associated with existing labeled set. We modify the conventional PageRank algorithm in three aspects for tracking application, including graph construction, PageRank vector acquisition and target filtering. Our simulations with the use of various challenging public-domain video sequences reveal that the proposed PageRank tracker outperforms mean-shift tracker, co-tracker, semiboosting and beyond semiboosting trackers in terms of accuracy, robustness and stability. [ABSTRACT FROM PUBLISHER]

A ferromagnetic vehicle, such as a submarine, magnetized by the Earth's magnetic field produces a magnetic anomaly field, and the tracking of moving targets can be realized through real-time analysis of magnetic data. At present, there are few tracking methods based on magnetic field vectors and their gradient tensor. In this paper, the magnetic field vector and its gradient tensor are used to calculate equivalent magnetic force. It shows the direction of the vector between the detector and the tracking targets for controlling the direction of motion of the detector and achieving the purpose of tracking. Compared with existing positioning methods, the proposed method is relatively less affected by instrument resolution and noise and maintains robustness when the velocity vectors of multiple magnetic targets change randomly.

Robust tracking is a challenging problem, due to intrinsic appearance variability of objects caused by in-plane or out-plane rotation and extrinsic factors change such as illumination, occlusion, background clutter and local blur. In this paper, we present a novel tri-tracking framework combining different views (different models using independent features) for robust object tracking. This new tracking framework exploits a hybrid discriminative generative model based on online semi-supervised learning. We only need the first frame for parameters initialization, and then the tracking process is automatic in the remaining frames, with updating the model online to capture the changes of both object appearance and background. There are three main contributions in our tri-tracking approach. First, we propose a tracking framework for combining generative model and discriminative model, together with different cues that complement each other. Second, by introducing a third tracker, we provide a solution to the problem that it is difficult to combine two classification results in co-training framework when they are opposite. Third, we propose a principle way for combing different views, which based on their Discriminative power. We conduct experiments on some challenging videos, the results from which demonstrate that the proposed tri-tracking framework is robust. [ABSTRACT FROM AUTHOR]

An algorithm for mobile terminal (MT) tracking based on time-of-arrival measurements in non-line-of-sight (NLOS) environments is proposed. It is based on NLOS detection together with a modified probabilistic data association approach where different subgroups of range measurements are constructed. Each of the subgroups provide a position estimate of the MT with it's corresponding covariance matrix that are both used in a hypothesis test for NLOS detection. The accepted position estimates are weighted with different probabilities in a Kalman filter framework. Simulation results show a significant increase in positioning accuracy in NLOS environments with respect to both, the extended Kalman filter (EKF) and a NLOS mitigation algorithm from the literature. In LOS environments similar performance to the EKF is achieved. The proposed method does not assume any statistical knowledge of the NLOS errors and only assumes the sensor noise variance to be known. [ABSTRACT FROM PUBLISHER]

Abstract: To overcome the drawbacks of the high-gain feedback term, a controller with an adaptive compensation term is developed for robust tracking of uncertain dynamic delay systems. It is proved that the introduction of the adaptive compensation term will not affect the stability of the original closed-loop system. In this way, it will be practically flexible to decide whether to include the adaptive compensation term depending on the tracking performance requirements and controller gain constraints. The effectiveness of the proposed controller is demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

Tracking people or objects across multiple cameras and maintaining a track within a camera is a challenging task in applications such as video surveillance, traffic monitoring, event detection and recognition. Some of the major challenges while tracking a target are illumination/scale changes and partial occlusion. In this paper, we propose a novel tracking framework using particle filter to efficiently track an object within a camera and a blob-based target association scheme for tracking across cameras. The proposed particle filter tracking algorithm uses a fragment-based approach to model the target and track it by fusing color and gradient features. The proposed solution incorporates coarser level spatial information by fragmenting each particle and is shown to be useful for tracking under partial occlusion. A fast yet robust model update is employed to overcome illumination changes. Experimental results show (i) the robustness of the fragment-based tracking approach with respect to illumination/scale change and partial occlusion and (ii) tracking persons across two cameras. [ABSTRACT FROM AUTHOR]

The manipulation of deformable objects is an important problem in robotics and arises in many applications including biomanipulation, microassembly, and robotic surgery. For some applications, the robotic manipulator itself may be deformable. Vision-based deformable object tracking can provide feedback for these applications. Computer vision is a logical sensing choice for tracking deformable objects because the large amount of data that is collected by a vision system allows many points within the deformable object to be tracked simultaneously. This article introduces a template based deformable object tracking algorithm, based on the boundary element method, that is able to track a wide range of deformable objects. The robustness of this algorithm to occlusions and to spurious edges in the source image is also demonstrated. A robust error measure is used to handle the problem of occlusion and an improved edge detector based on the Canny edge operator is used to suppress spurious edges. This article concludes by quantifying the performance increase provided by the robust error measure and the robust edge detector. The performance of the algorithm is also demonstrated through the tracking of a sequence of cardiac MRI images. [ABSTRACT FROM AUTHOR]

To realize decentralized robust tracking control for robots with uncertain parameters, a new design method is proposed. A robust tracking controller designed by this method consists of two parts: a feedforward controller and a feedback robust controller. A feedforward control is first applied and error dynamics are introduced. For each joint error subsystem a robust controller is designed in two steps: first, a nominal controller is designed for the nominal plant to achieve desired tracking performance, then a robust compensator is added to restrain the influence of the perturbation, that is the difference of the real plant from the nominal plant. The controller designed by the proposed method is a linear time-invariant one. It is shown that robust stability and robust tracking property can be achieved by applying the controller with a sufficiently wide frequency bandwidth. An important feature of the method is that the controller parameters can be tuned on-line easily. [ABSTRACT FROM AUTHOR]

Tracking an object in a sequence of images can fail due to partial occlusion or clutter. Robustness to occlusion can be increased by tracking the object as a set of "parts" such that not all of these are occluded at the same time. However, successful implementation of this idea hinges upon finding a suitable set of parts. In this paper we propose a novel segmentation, specifically designed to improve robustness against occlusion in the context of tracking. The main result shows that tracking the parts resulting from this segmentation outperforms both tracking parts obtained through traditional segmentations, and tracking the entire target. Additional results include a statistical analysis of the correlation between features of a part and tracking error, and identifying a cost function that exhibits a high degree of correlation with the tracking error. [ABSTRACT FROM AUTHOR]