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In order to address the multi-target tracking problem of airborne passive bisstatic radar (APBR) in an unknown clutter environment, a robust adaptive labeled multi-Bernoulli (RA-LMB) filter is proposed. Firstly, a model for multi-target tracking problem is established on the basis of the LMB filter algorithm framework. Then, for the problems of unknown target newborn parameters, clutter parameters and target detection probability, the measurement-driven target newborn model and the online parameter estimation method based on the cardinally-balanced multi-target multi-Bernoulli estimator are proposed. Finally, considering the non-linearity of APBR measurements, the sequential Monte Carlo method is used to implement the proposed algorithm. The experimental results show that the proposed filter is able to estimate the multi- target trajectory using the APBR measurements, and the performance in the unknown clutter environment can be approximated to the generalized LMB filter with known clutter parameters, with better robustness. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We consider a class of controlled nonlinear plants, the dynamics of which are governed by a vector system of ordinary differential equations with a right-hand side that is partially known. The study's objective is to construct a robust tracking controller with certain constraints on the state variables, assuming that the state variables and their time derivatives can be observed. The Legendre–Fenchel transform and a chosen proxy function are utilized to develop this mathematical development using the mirror descent approach, which is frequently employed in convex optimization problems involving static objects. The Average Subgradient Method (an improved version of the Subgradient Descent Method), and the Integral Sliding Mode technique for continuous-time control systems are basically extended by the suggested unifying architecture. The primary findings include demonstrating that the "desired regime"—a non-stationary analog of the sliding surface – can be achieved from the very start of the process and getting an explicit upper bound on the cost function's decrement. [ABSTRACT FROM AUTHOR]

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.

In this paper, a novel robust tracking control strategy for nonlinear unmatched uncertain systems is formulated using the event-based adaptive dynamic programming (ADP) approach. First, an augmented system is constructed based on the nonlinear system and the reference trajectory. Then, by forming an auxiliary system and introducing a discounted cost function, the event-based robust tracking control problem is transformed into the event-based optimal control problem of the auxiliary system. The event-based Hamilton–Jacobi–Bellman (HJB) equation associated with the event-based optimal control problem is solved using a single critic neural network (NN) under the ADP framework. A novel weight tuning rule for the critic network is formulated to avoid the necessity of an initial admissible control at the beginning of the weights tuning process. The obtained event-based controller is updated only at the triggering instants decided by the designed triggering condition, which helped in a significant reduction of resources used in computation and communication. Meanwhile, it is demonstrated that the obtained event-based controller can guarantee the tracking error's uniform ultimate boundedness. Furthermore, using the Lyapunov method, it is guaranteed that the established novel event-triggering rule ensures uniform ultimate boundedness of all signals associated with the closed-loop auxiliary system. Finally, the applicability of the proposed control scheme is demonstrated by providing two simulation examples. [ABSTRACT FROM AUTHOR]

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.

In this paper, a data-based neural policy learning method is established to solve the robust tracking control problem of a class of continuous-time systems which have two kinds of uncertainties at the same time. First, the robust trajectory tracking is achieved by controlling the tracking error to zero. The specific implementation strategy is to construct an augmented system including the tracking error and then transform the robust tracking control problem into an optimal control problem by selecting a suitable cost function. Then, a neural network identifier is built to reconstruct the unknown dynamics and a policy iteration algorithm is adopted by using a critic neural network. In this way, the Hamilton–Jacobi–Bellman equation can be solved. Through this learning algorithm, the approximate optimal control policy is obtained and the solution of the robust tracking control problem can be derived. Finally, two simulation examples are proposed to verify the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

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/.

This paper proposes a constructive solution to the output regulation — output tracking problem for a general class of interconnected systems. The class of systems under consideration consists of a linear 2 × 2 hyperbolic Partial Differential Equations (PDE) system coupled at both ends with Ordinary Differential Equations (ODEs). The proximal ODE system, which represents actuator dynamics, is actuated. Colocated measurements are available. The distal ODE system represents the load dynamics. The control objective is to ensure, in the presence of a disturbance signal (regulation problem), that a virtual output exponentially converges to zero. By doing so, we can ensure that a state component of the distal ODE state robustly converges towards a known reference trajectory (output tracking problem) even in the presence of a disturbance with a known structure. The proposed approach combines the backstepping methodology and frequency analysis techniques. We first map the original system to a simpler target system using an invertible integral change of coordinates. From there, we design an adequate full-state feedback controller in the frequency domain. Following a similar approach, we propose a state observer that estimates the state and reconstructs the disturbance from the available measurement. Combining the full-state feedback controller with the state estimation results in a dynamic output-feedback control law. Finally, existing filtering techniques guarantee the closed-loop system robustness properties. [ABSTRACT FROM AUTHOR]

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 paper focuses on the problem of static output feedback preview tracking control for discrete-time systems with time-varying parameters subject to a previewable reference signal. We develop a design method of a robust controller with integral and preview actions achieving robust tracking performance. First, an augmented error system including future information on reference signal is constructed by introducing two new related auxiliary variables to the original system state and input. This leads to a tracking problem being transformed into a regulator problem. Then, a previewable reference signal is fully utilised through reformulation of the output equation for the augmented error system while considering a static output feedback. Meanwhile, the static output preview control gains are solved explicitly by the proposed conditions. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A robust multi-objective linear parameter-varying (LPV) control method based on pole assignment in specified region is proposed for longitudinal flight control of air-breathing hypersonic vehicle with wave-rider configuration. A longitudinal nonlinear mechanism model of air-breathing hypersonic vehicle is presented, which is used for obtaining its rigid LPV model. A design method of LPV state feedback control system based on pole assignment in specified region is proposed. The robust stability, disturbance rejection and tracking performance of the system are constrained to realize the multi-objective robust tracking control of LPV system by extended linear matrix inequality. The conservatism of this method is reduced by introducing the slack variables to decouple the Lyapunov function matrix and system matrix. The designed controller is applied to the nonlinear mechanism model of hypersonic vehicle for simulation proof. The simulated results show that the designed controller can be used to make the closed-loop feedback control system effectively track the change of command signals, and the system has good dynamic performance and strong anti-interference ability. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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.

Abstract A robust visual tracking system is expected to track the object accurately and rapidly in complex scenes with clutters, distracters and partial occlusion etc. These challenges remain the focuses of current research. Studies on human vision reveal that the human visual system is able to tackle these challenges efficiently by applying mechanisms including visual saliency and attention. Inspired by this research finding, this paper presents a salience-based tracking method for robust tracking. The major steps include salience computation, feature tracking and model updating. The salience of visual features is defined on their regions of interest. By examining visual saliency in regions of a visual object and its environment, we are able to estimate object salience and environment salience of extracted visual features for robust visual tracking. Experiments indicate that the proposed method can achieve promising performance in an environment with distracters and partial occlusion compared with the state of the art tracking methods. [ABSTRACT FROM AUTHOR]

The tracking problem is considered for a recurring class of systems, such as the Cartesian robots with real actuators, some transportation systems, and scanning devices used for medical and engineering applications. Some theorems are proved to design a PD type controller, with a possible compensation signal, in order to track sufficiently smooth trajectories with a prescribed maximum error. The developed design methodology is illustrated through two engineering examples. The first example concerns the safety distance control of an electric vehicles fleet. The second example deals with the identification of an antenna scanning system and the prototype design of a new controller that provides better performance than the current one. [ABSTRACT FROM AUTHOR]

To solve system parameter variation such as magnetic circuit saturation and excitation inductance of the doubly fed induction generator (DFIG) caused by grid voltage swells, a high‐voltage ride‐through (HVRT) robust control method for the DFIG based on the internal model control (IMC) controller is proposed. Through an analysis of the transient behavior of the rotor current caused by grid voltage swells, it was found that a rotor current inner loop using IMC could be established. Utilizing the H2‐norm control theory, and by considering the system's robust stability and robust tracking, the IMC controller parameter of the rotor current inner loop is set. The proposed method suppresses the rotor current and the electromagnetic torque when the parameter changes. Through the simulation of a 1.5‐MW DFIG, it is verified that the proposed method can enhance its HVRT capability, exhibiting good robustness against model errors and external disturbances. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

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]