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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]

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]

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]

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]

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]

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]

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]

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.)

Purpose The purpose of this paper is to solve the tracking problem for free-floating space manipulators (FFSMs) in task space with parameter uncertainties and external disturbance.Design/methodology/approach In this paper, the novel cerebellar model articulation controller (CMAC) is designed with the feedback controller. More precisely, the parameter uncertainties in the FFSM are considered for achieving the robustness.Findings By using the dynamically equivalent model, the CMAC can be designed and trained with the desired performance, such that the prescribed trajectory can be followed accordingly. The simulation results are presented for illustrating the validity of the derived results.Originality/value Based on the designed CMAC, the tracking error would be approaching zero by choosing appropriate quantization level in CMAC and the corresponding learning rules can be tuned online. [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]

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]

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]

This article aims at a new algorithm for tracking moving objects in the long term. We have tried to overcome some potential difficulties, first by a comparative study of the measuring methods of the difference and the similarity between the template and the source image. In the second part, an improvement of the best method allows us to follow the target in a robust way. This method also allows us to effectively overcome the problems of geometric deformation, partial occlusion and recovery after the target leaves the field of vision. The originality of our algorithm is based on a new model, which does not depend on a probabilistic process and does not require a data based detection in advance. Experimental results on several difficult video sequences have proven performance advantages over many recent trackers. The developed algorithm can be employed in several applications such as video surveillance, active vision or industrial visual servoing. [ABSTRACT FROM AUTHOR]

This paper considers the problem of output tracking of a time-varying reference signal for a class of uncertain systems in the presence of actuator saturation. To achieve this capability, a new controller is proposed by robustifying the generalized composite nonlinear feedback control method with the integral sliding mode controller. Since the proposed controller may be saturated, a precise analysis is done to show its robust performance despite the presence of actuator saturation and model uncertainties. For this purpose, a theorem is given and proved that guarantees the robust output tracking via the proposed control law for three different cases of the saturation function and it is shown that even if the control signal is saturated, the proposed controller achieves output tracking of the time-varying reference signal. Also, in order to show the applicability of the proposed controller, it is applied on two practical systems, the XY-table and inertia wheel inverted pendulum. Computer simulations verify the theoretical results and also display the effective performance of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of H∞ preview tracking control with robust performance for uncertain discrete-time systems. In order to avoid applying the difference operator to the time-varying matrix, by taking advantage of the difference between the system state variables, input variables, and the corresponding auxiliary variables, instead of the usual difference between system states, an augmented error system including previewed information is constructed, which converts the tracking problem into a regulator problem. A sufficient condition based on the free-weighting matrices technique and the Lyapunov stability theory is derived for the robust asymptotic stability of uncertain systems. Moreover, a state feedback control law with preview action design method is obtained via the linear matrix inequality approach. Based on these, a state observer for preview control systems is formulated. Previewable reference signals are fully utilized through reformulation of the output equation while designing the state observer. The proposed construction method of augmented error system is applicable to uncertain discrete-time systems in which the uncertainties are general. Also an integrator is introduced to ensure the closed-loop system tracking performance with no static error. The numerical results also show the effectiveness of the preview control law for uncertain systems in the paper. [ABSTRACT FROM AUTHOR]

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]

Occlusion is one of the most challenging problems in visual object tracking. Recently, a lot of discriminative methods have been proposed to deal with this problem. For the discriminative methods, it is difficult to select the representative samples for the target template updating. In general, the holistic bounding boxes that contain tracked results are selected as the positive samples. However, when the objects are occluded, this simple strategy easily introduces the noises into the training data set and the target template and then leads the tracker to drift away from the target seriously. To address this problem, we propose a robust patch-based visual tracker with online representative sample selection. Different from previous works, we divide the object and the candidates into several patches uniformly and propose a score function to calculate the score of each patch independently. Then, the average score is adopted to determine the optimal candidate. Finally, we utilize the non-negative least square method to find the representative samples, which are used to update the target template. The experimental results on the object tracking benchmark 2013 and on the 13 challenging sequences show that the proposed method is robust to the occlusion and achieves promising results. [ABSTRACT FROM AUTHOR]

Magnetic levitation systems are able to provide frictionless, reliable, fast and economical operations in wide-range applications. The effectiveness and applicability of these systems require precise feedback control designs because the magnetic levitation is an unstable process and have highly nonlinear dynamics. In this article, a robust sliding mode–based cascade control approach is proposed for effectively tracking the reference position of a magnetic levitation system. The magnetic levitation plant is described with electrical and mechanical models, and the control problems of these parts are treated with cascade controllers. An integral sliding mode and an output feedback sliding mode controllers are designed for use in the cascade loops. The performance of the sliding mode controllers is compared with a proportional–integral–velocity plus proportional–integral control structure. It is shown that the proposed control structure is able to provide a highly satisfactory tracking performance and can eliminate the effects of the inductance-related uncertainties and operating point originated disturbances. The experimental results are provided to validate the efficacy and feasibility of the approach. [ABSTRACT FROM AUTHOR]

In this paper, the adaptive robust tracking and model following controller is designed for the uncertain nonlinear systems with disturbances, time-delays and input nonlinearities. A novel control law based on the linear matrix inequalities is proposed for the tracking control of the nonlinear systems with Lipschitz nonlinearities. The selection procedure of the nonlinear integral sliding surface and the existence of the proposed sliding mode are two significant subjects, which have been addressed. This controller guarantees that the tracking error decreases asymptotically to the origin in the presence of time-varying perturbations. By elimination of the reaching mode of the sliding surface, the robustness performance against perturbations is guaranteed right from the initial instant. Furthermore, the knowledge of the upper bounds of the uncertainties, disturbances and time-delays is not required to be determined. Simulation results are provided to illustrate the efficiency of the presented technique compared to the previous methods. [ABSTRACT FROM AUTHOR]

This paper proposes a robust adaptive position control scheme for automotive electronic throttle (ET) valve. Compared with the conventional throttle control systems, in this paper, a robust adaptive sliding mode (RASM) control scheme is developed in order to eliminate the effects of the parameter uncertainties and nonlinearities including friction, return-spring limp-home and gear backlash. It is shown that both the lumped uncertainty bound and the control gains are adaptively estimated by the update laws, such that not only the bound information of the lumped uncertainty and the control gains are no longer required, but also a robust tracking performance can be ensured in the presence of the parametric variations and disturbances. The comparative simulation and experimental studies are demonstrated to verify the excellent transient and steady-state tracking performance of the proposed RASM controller for ET systems. [ABSTRACT FROM AUTHOR]

We investigate the problem of robust adaptive tracking by output feedback for a class of uncertain nonlinear systems. Based on the high-gain scaling technique and a new adaptive law, a linear-like output feedback controller is constructed. Only one dynamic gain is designed, which makes the controller easier to implement. Furthermore, by modifying the update law, the adaptive controller is robust to bounded external disturbance and is able to guarantee the convergence of the output tracking error to an arbitrarily small residual set. A numerical example is used to illustrate the effectiveness of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We investigate the global robust tracking problem via output feedback for a class of cascade nonlinear systems with dynamic uncertainties and non-vanishing disturbances. It does not require a priori knowledge of the sign of the high-frequency gain. A recursive design scheme is presented using the ideas of pseudosign function, Nussbaum-type gain technique and the deadzone method. It is shown that under some conditions, the tracking error can be guaranteed asymptotic to the interval [−ε, ε]⊂ℝ with arbitrary prescribed ε>0 after a finite time, while keeping all signals of the resulting closed-loop systems bounded. The simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, a combination of integral sliding mode and composite nonlinear feedback method is proposed for fast and accurate robust tracking and model following of uncertain multiple-input multiple-output linear systems with multiple time-delays and external disturbances. The composite nonlinear feedback technique allows following the reference trajectory within a given accuracy and the invariance property of the integral sliding mode method rejects the disturbances and preserves the stability of the closed-loop system. By using the Lyapunov-Krasovskii functional, the conditions for asymptotic stabilization are obtained in the form of linear matrix inequalities. To improve the tracking performance for different reference amplitudes, a new nonlinear function is included in the control law and is optimally tuned using a modified random search algorithm. The selection of sliding surface and the existence of the sliding mode are two significant issues, which have been addressed. This scheme not only guarantees robustness against time-delays and uncertainties, but also avoids chattering phenomenon and reaching phase. Finally, the simulations are provided to verify the theoretical results. The results show that the proposed method leads to great improvement on the tracking error and the control effort compared to the available 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]

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. [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]

The noise bandwidth is an important parameter on the Global Positioning System (GPS) tracking loop; the larger the bandwidth the better the high dynamic performance, but the thermal noise suppression capability is reduced. In a GPS/Inertial Navigation System (INS) tightly integration system, the GPS receiver’s carrier phase lock loops (PLLs) are aided by the Doppler shift from the INS, which can reduce user most dynamics. So the noise bandwidth can be narrowed to decrease the thermal noise tracking errors. However, due to the inertial sensor errors, the external INS-derived Doppler shift information are not completely accurate, therefore a Doppler-aiding error-induced phase error source must exist. The part of tracking error is derived in this paper. Other error sources are still present, so the bandwidth of PLLs cannot be set arbitrarily. This paper applies one artificial intelligence technique, the fuzzy logic controller (FLC), to calculate the PLL noise bandwidth on-line. Simulation results prove that the proposed FLC-based software receiver does achieve a superior tracking ability over conventional tracking loops in complicated conditions. In addition, the proposed algorithm works in the real-time mode and can be used in many practical applications. [ABSTRACT FROM AUTHOR]

In this paper, the composite nonlinear feedback (CNF) method for robust tracking control of multivariable time-delayed systems with input saturation is proposed. By constructing an appropriate Lyapunov-Krasovskii functional and using the linear matrix inequality (LMI) technique, the asymptotic tracking criteria is derived in terms of LMI, which can be solved numerically using the LMI toolbox of MATLAB. Unlike the previous robust tracking controller design methods, which need restrictive assumptions, less conservative design conditions are obtained using this approach. The proposed CNF-based robust tracking controller improves the transient performance and steady state accuracy simultaneously. Simulation results are included to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the composite nonlinear feedback control method is considered for robust tracking and model following of uncertain linear systems. The control law guarantees that the tracking error decreases asymptotically to zero in the presence of time varying uncertain parameters and disturbances. For performance improvement of the dynamical system, the proposed robust tracking controller consists of linear and nonlinear feedback parts without any switching element. The linear feedback law is designed to allow the closed loop system have a small damping ratio and a quick response while the nonlinear feedback law increases the damping ratio of the system as the system output approaches the output of the reference model. A new collection of different nonlinear functions used in the control law are offered to improve the reference tracking performance of the system. The proposed robust tracking controller improves the transient performance and steady state accuracy simultaneously. Finally, the simulations are provided to verify the theoretical results. [ABSTRACT FROM AUTHOR]

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]

This paper addresses double-loop robust tracking controller design of the miniaturized linear motor drive precision stage with mass and damping ratio uncertainties. As an inner-loop, a disturbance observer (DOB) is employed to suppress exogenous low frequency disturbances such as friction and cutting force. To further eliminate the residual disturbance and to guarantee the robust tracking to the reference input, a µ-synthesis outer-loop controller is designed. For eliminating the steady state error, a technique is proposed to design the µ-synthesis outer-loop controller with an integrator. A guideline to select the bandwidth of the Q-filter in the DOB is provided. Simulations using a model of a prototype micro-milling machine indicate that the proposed outer-loop synthesis scheme is superior to the H suboptimal control in disturbance rejection performance and steady state tracking performance. Furthermore, it is shown experimentally that the proposed double-loop robust tracking controller improves the tracking performance of the stage by 29.6% over PID control with a DOB inner-loop. [ABSTRACT FROM AUTHOR]

The paper focuses on the implementation of a hybrid control technique applied to a pneumatically actuated micro robot which is modelled and simulated to operate in a constrained environment for an in-pipe application. A mathematical model that represents the dynamic characteristics of micro robot is first derived and later the dynamic response of the robot system subjected to different input excitations rigorously investigated. Three different types of control algorithms are designed and integrated into this system via a proportional-integral-derivative (PID) controller, sliding mode controller (SMC) and active force control (AFC) method. This hybrid scheme is to be known as AFC+SMC+PID and its primary objective is to ensure accurate and robust trajectory tracking control of the micro robot system is achieved. The performance of the control system under different types of disturbances is evaluated through a simulation study. The obtained results clearly demonstrate an effective trajectory tracking capability of the wormlike micro robot in spite of the negative effects of the external disturbances. [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]

This paper investigates the robust tracking control problem for a class of nonlinear networked control systems (NCSs) using the Takagi-Sugeno (T-S) fuzzy model approach. Based on a time-varying delay system transformed from the NCSs, an augmented Lyapunov function containing more useful information is constructed. A less conservative sufficient condition is established such that the closed-loop systems stability and time-domain integral quadratic constraints (IQCs) are satisfied while both time-varying network-induced delays and packet losses are taken into account. The fuzzy tracking controllers design scheme is derived in terms of linear matrix inequalities (LMIs) and parallel distributed compensation (PDC). Furthermore, robust stabilization criterion for nonlinear NCSs is given as an extension of the tracking control result. Finally, numerical simulations are provided to illustrate the effectiveness and merits of the proposed method. [ABSTRACT FROM AUTHOR]

A two degree-of-freedom controller structure is proposed in this paper for robust tracking of desired trajectories. The inputs required for these trajectories, estimated by an augmented state-space model approach, constitutes the feed-forward part that ensures output tracking for the nominal plant model, and a feedback part, via a multiple model approach, makes the overall system robust. This method is then applied to helicopter hover control. [ABSTRACT FROM AUTHOR]