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This paper deals with the problem of guidance law design for the single moving mass controlled reentry vehicle when impact angle constraints and maneuvering target are taken into consideration. More specifically, a modified rolling guidance law is proposed with the interactive virtual target and the landing point prediction strategy. First, considering the fact that the roll channel can be controlled directly, the relative motion between the single moving mass controlled reentry vehicle and the target is described by the error angle between the relative velocity and the line-of-sight. Second, a nonlinear error angle command is given to reduce the rotation rate. To satisfy impact angle constraints, an interactive virtual target is presented and the “S” formed velocity of the virtual target is given to abate the error angle tracking difficulty at the final stage of the reentry phase. Then, the landing point prediction strategy is employed and the motion variation trend is also taken into consideration. As the maneuvering target is replaced with the predicted landing point, the error angle tracking difficulty caused by the target velocity decreases, which is helpful to meet impact angle constraints and improve guidance accuracy at the same time. Finally, the finite-time rolling guidance law is proposed and proved via Lyapunov stability theorem. Compared with the existing method, lower-speed rotation, smaller missing distance and less impact angle errors are obtained, which can be demonstrated by numerical simulations.

This paper designs a novel finite-time extended state observer (FTESO) based super-twisting sliding mode (STSM) control scheme to promote the anti-disturbance property and tracking performance of permanent magnet synchronous motor (PMSM). Firstly, a FTESO combined with generalized super-twisting technique is constructed to estimate the unknown lumped disturbance and compensate the estimated value to the STSM speed controller. Compared with the conventional extended state observer (ESO), the proposed FTESO can make the estimation error converge to the origin in finite time, which provides a faster convergence speed and higher estimation accuracy. Moreover, in order to tackle the problem of performance deterioration caused by inertia mismatch, particularly for PMSM speed control, an inertia identification method is employed to calculate the true value of inertia online to tune the parameters of FTESO and STSM controller automatically. Comprehensive simulation and experimental results demonstrate that the proposed control method can improve the robustness of PMSM system in the presence of load torque and inertia variations.

Fault-tolerant attitude tracking control for rigid-flexible coupled spacecraft is studied in this paper. Taking actuator faults, measurement errors of attitude and angular velocity, unmeasured modal displacements, model uncertainties, and external disturbances into consideration, an adaptive fault-tolerant attitude tracking control is designed. Then based on the Sequential Lyapunov Method, the stability of the closed-loop system is analyzed showing that, if the attitude and angular velocity measurement errors are bounded, then successively less conservative bounds on the attitude tracking error and the angular velocity error can be obtained.

The power voltage (P-V) characteristics of photovoltaic (PV) systems exhibit multiple power peaks under partially shaded conditions. Several global maximum power point tracking (GMPPT) algorithms in the literature recognize the irradiance change, only after the convergence of operating point to global peak, or use additional hardware to call GMPPT subroutine at definite time intervals to detect any insolation change, and thus track the global peak. However, during fast changing partial shading conditions, these methods are less effective, as they do not detect any irradiance change during the tracking phase of any shading pattern. This paper proposes a novel modified current control approach that uses current as a parameter to detect the insolation change during the tracking phase and track the global peak under fast changing partial shading conditions without any additional hardware. The proposed technique improves the tracking efficiency by as much as 39%, thus proving to be effective under fast changing partial shading conditions. The superior tracking performance of the proposed algorithm over the existing techniques in terms of its tracking efficiency, dynamic tracking capability, tracking speed, and convergence to the global peak is demonstrated with extensive simulations using MATLAB/Simulink and experimental results.

Time-lapse microscopy images generated by biological experiments have been widely used for observing target activities, such as the motion trajectories and survival states. Based on these observations, biologists can conclude experimental results or present new hypotheses for several biological applications, i.e. virus research or drug design. Many methods or tools have been proposed in the past to observe cell and particle activities, which are formed as single cell tracking and single particle tracking problems, by using algorithms and deep learning technologies. In this article, a review for these works is presented in order to summarize the past methods and research topics at first, then points out the problems raised by these works, and finally proposes future research directions. The contributions of this article will help researchers to understand past development trends and further propose innovative technologies.

This article is mainly concerned with the fixed-time tracking control problem for multiple uncertain mechanical systems with actuation dead zones. As the dead zones and control gains are time varying and completely unknown, the control impact on mechanical system becomes completely unknown, making the achievement of fixed-time tracking control nontrivial. The existing studies on fixed-time tracking control cannot effectively estimate and compensate such dead zones, resulting in the upper bound of convergence time being uncertain and even system becoming unstable. To compensate the dead zones in a fixed time, we introduce a bound estimation method to estimate the impact of dead zones and design two fixed-time adaptive laws. Two cases on system uncertainties are considered. By introducing distributed control method, adaptive control method, neural networks and some skillful treatments, two novel distributed adaptive neural fixed-time tracking control schemes are proposed. It is confirmed that, with the proposed control schemes, all followers which are uncertain mechanical systems with completely unknown dead zones track the leader with sufficient precision in a predefined time.

This paper presents a summary of the work done on the development of a new algorithm for mobile robot navigation in unknown dynamic environment. The developed human-like algorithm uses a free-space attraction (Agoraphilic) concept for robot navigation. The algorithm presented in this paper is an advanced development of the Agoraphilic navigation algorithm. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for free-spaces towards the goal (solutions) to follow. The original Agoraphilic, while it was able to overcome the limitations of the traditional algorithms, it had its own limitations in navigating robots in environments cluttered with moving obstacles. The new Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE) was developed to overcome those limitations. ANADE consists of seven main modules reported in the paper. The objects tracking and objects prediction methodologies developed for the algorithm estimate future growing free-space passages towards the goal. The algorithm generates a time-varying single attractive force to pull the robot through the free-space towards the predicted (future) growing free-space passages leading to the goal. The new algorithm was tested, not only through simulation, but also through experimental work. Summary of the experimental results is presented and discussed in this paper.

This letter shows the first acquisition, Doppler tracking, and positioning results with Starlinks low Earth orbit (LEO satellite signals. A generalized likelihood ratio (GLR -based test is proposed to acquire Starlinks downlink signals. A Kalman filter (KF -based algorithm for tracking the Doppler frequency from the unknown Starlink signals is developed. Experimental results show Doppler tracking of six Starlink satellites, achieving a horizontal positioning error of 10 m.

To design simple and efficient inductive power transfer (IPT) systems, minimum number of conversion stages and effective maximum efficiency tracking (MET) are the primary design criteria. The MET control uses a fast mutual inductance estimation (MIE) algorithm to achieve fast tracking of the maximum efficiency point especially when the variation of the gap distance and misalignment of the magnetic coupler are significant. The feedback control of the MET-MIE IPT system should be designed without a wireless communication channel to improve stability and robustness. In this paper, a novel MET-MIE control strategy is proposed for achieving simplicity and high efficiency in IPT systems. Verifications are provided by simulations and experiments. It is shown that maximum efficiency can be tracked with less than 4% error in MIE under significant variation of the gap distance and misalignment of the magnetic coupler.

This paper addresses trajectory planning and control for a dual-arm free-floating space robot (FFSR) based on predefinedtime stability. Firstly, a class of general nonsingular terminal sliding mode control strategy is proposed to achieve global predefined-time stability by switching a control parameter in a form of function. Then, a high-precision trajectory planning method is proposed for a FFSR using the proposed predefined-time stability theorem, which can make the end-effector and the base reach the desired pose within a prespecified time, and reduce the adverse effect on planning accuracy resulted from the singularity avoidance algorithm and the obstacle avoidance one. Subsequently, a trajectory tracking control strategy is proposed for a FFSR employing the proposed general nonsingular predefined-time terminal sliding mode, and the tracking errors can converge within a predefined time to an arbitrarily small neighborhood of zero in the presence of persistent disturbances. The proposed trajectory planning method has the advantage of pose feedback, so can be combined with the trajectory tracking controller in the control process to further enhance the control performance for the FFSR. Simulation results validate the effectiveness of the proposed methodologies.

A novel adaptive method to achieve both path following and formation moving along desired orbits in the presence of a spatio-temporal flowfield is presented. The flowfield is a spatio-temporal general flow with unknown time-varying parameters. The so-called \emph{congelation of variables} method is used to estimate the time-varying flow parameters, which do not have any restrictions on the rate of their variation. The asymptotic properties of the resulting adaptive system are studied in detail. Simulation results demonstrate the effectiveness of the proposed method.

The wheel tracking test (WTT) has been extensively used for laboratory characterization of permanent deformation of hot mix asphalt (HMA). However, the fully-confined setup of the device is unable to capture the tertiary zone where shear deformation takes place. This makes the permanent deformation resistance characteristics of HMA difficult to analyze. Hence, a modified wheel tracker (MWT), with unconfined lateral sides along the wheel tracking direction was utilized in this study, which can capture the tertiary flow (referred to as the flow number (FN)). The dynamic creep test, which yields FN, is a common laboratory test which has the ability to characterize permanent deformation considering the shear behavior. The study investigated the relationship of the MWT and the dynamic creep test utilizing the permanent deformation and permanent microstrain data from the respective tests. A novel parameter, FN-Index was explored. The permanent deformation parameters derived from the MWT were well correlated with the dynamic creep test. The MWT showed promising repeatability for the FNs.

Mobile robots are often subject to multiplicative noise in the target tracking tasks, where the multiplicative measurement noise is correlated with additive measurement noise. In this paper, first, a correlation multiplicative measurement noise model is established. It is able to more accurately represent the measurement error caused by the distance sensor dependence state. Then, the estimated performance mismatch problem of Cubature Kalman Filter (CKF) under multiplicative noise is analyzed. An improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative noise. In practice, the model parameters are unknown or inaccurate, especially the correlation of noise is difficult to obtain, which can lead to a decrease in filtering accuracy or even divergence. To address this, an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is extended. Finally, the estimated performance is analyzed theoretically, and the simulation study is conducted to validate the effectiveness of the proposed algorithm.

Sequence image target tracking has two requirements of accuracy and speed. Aiming at the problem of slow speed of convolutional neural network target tracking algorithm, an improved algorithm for sequence image target tracking is designed by using a fusion of Faster MDNet and optical flow method. The target algorithm designed in this paper is improved on the basis of Multi-Domain Convolutional Neural Network (MDNet). The optical flow method is used to obtain the primary selection box first, which can effectively deal with the challenges brought by scale change and in-plane rotation, and has good tracking speed and performance. The objective algorithm of this paper is used to explore the Marangoni Effect in the melting process of solid iron tailings in molten blast furnace slag. Because the main component of iron tailings is silica, and the melting point of silica is the highest, so taking silica as the research object, an intelligent analysis of Marangoni Effect in high temperature melting process of silica particles is realized. The results show that: the target algorithm achieves the real-time standard (FPS > 24), the average Distance Precision (DP) is 97.3%, the average Success Rate (SR) is 98.02%, the average Center Location Error (CLE) is 6.2684, and the average Overlap Precision (OP) coverage is 63.86%. The target algorithm has certain advantages in five evaluation indexes: distance precision, success rate, center location error, overlap precision and frames per second.

Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation. It is a critical and challenging problem to evaluate the training samples collected from previous predictions and employ sample selection by their quality to train the model. To tackle the above problem, we propose a joint discriminative learning scheme with the progressive multistage optimization policy of sample selection for robust visual tracking. The proposed scheme presents a novel time-weighted and detection-guided self-paced learning strategy for easy-to-hard sample selection, which is capable of tolerating relatively large intraclass variations while maintaining interclass separability. Such a self-paced learning strategy is jointly optimized in conjunction with the discriminative tracking process, resulting in robust tracking results. Experiments on the benchmark datasets demonstrate the effectiveness of the proposed learning framework.

Obstacle avoidance is usually an additional task for a redundant manipulator when the end-effector tracking task is performed, which guarantees the safety of the redundant manipulator. By formulating and combining the end-effector tracking task and the obstacle avoidance task using zeroing neural network (ZNN), a concise continuous ZNN (CZNN) controller is first proposed. To develop discrete controllers for practical control, a second-order discrete formula and a third-order discrete formula are introduced. Therefore, by utilizing two discrete formulas to discretize the CZNN controller, two concise discrete ZNN (DZNN) controllers are proposed. Detailed theoretical analyses guarantee the effectiveness of task formulations, CZNN controller, and DZNN controllers. In addition, some comparisons with existing studies are presented in details. Furthermore, three groups of simulative experiments on the basis of UR5 manipulator and two groups of physical experiments on the basis of Kinova manipulator are conducted to illustrate the effectiveness, superiority, and practicability of two DZNN controllers.

This article focuses on the design of a novel adaptive fuzzy event-triggered tracking control approach for a category of high-order uncertain nonlinear systems with prescribed performance requirements, in which a high-order tan-type barrier Lyapunov function (BLF) is employed to handle and analyze the output tracking error, fuzzy systems are adopted to identify the totally unknown nonlinear functions, and only one gain function rather than parameter estimation functions is designed to cancel out all unknowns appearing in fuzzy systems. As a result, complicated calculations are avoided and a structured simple control is achieved. The proposed controller not only ensures that the tracking error is always within a predefined region but also reduces the communication burden from the controller to the actuator. Finally, comparison simulations are presented to verify the effectiveness of the proposed control schemes.

This paper proposes a sensor network information fusion based distributed state estimation algorithm for tracking non-cooperative maneuvering target. In order to lower the communication burden and energy consumption, an event-triggered mechanism is introduced. The distributed state estimation algorithm for maneuvering target tracking is designed by two stages, namely, local filtering stage and consensus fusion stage. The algorithm proposed in this paper can be used to obtain the high accurate state estimation of the target by introducing a multiple suboptimal fading factor even when the target makes big maneuvering. Moreover, a contribution factor is designed in the information fusion stage to improve the accuracy of maneuvering target state estimation and reduce the consensus iteration times. Besides, the stochastic boundedness of the event-triggered distributed estimation algorithm is proved by introducing a stochastic process. Finally, Monte Carlo numerical simulation example is designed to illustrate the effectiveness of the algorithm.

Resilience adjustment refers to the generation of a control strategy by evaluating the interaction between related factors. Tracking intervals of the high-speed train platoon change dynamically, which directly influences the operation safety and efficiency, and constrains the train operation trajectories. In China, the tracking interval is getting shorter. To ensure safety and improve efficiency, we research a dynamic interval resilience adjustment strategy based on the moving block system. Firstly, the optimal offline operation strategy is obtained by solving the multi-objective optimization model with the improved gravitational search algorithm (I-GSA). The resilience adjustment mechanism is developed to evaluate the tracking interval and choose the appropriate driving strategy to adjust operation states based on the resilience tracking interval model. Then, we study the relation between operation strategy and departure interval, and a seeker optimization algorithm (SOA) is used to obtain the optimal departure intervals and driving strategies. Simulations are conducted based on the sections between Chibi North station and Changsha South station in Wuhan-Guangzhou high-speed railway. The results indicate that the total operation time decreased by 191s and the operation safety can be ensured at any time.

This article addresses the adaptive neural tracking control problem for a class of uncertain stochastic nonlinear systems with nonstrict-feedback form and prespecified tracking accuracy. Some radial basis function neural networks (RBF NNs) are used to approximate the unknown continuous functions online, and the desired controller is designed via the adaptive dynamic surface control (DSC) method and the gain suppressing inequality technique. Different from the reported works on uncertain stochastic systems, by combining some non-negative switching functions and dynamic surface method with the nonlinear filter, the design difficulty is overcome, and the control performance is analyzed by employing stochastic Barbalat's lemma. Under the constructed controller, the tracking error converges to the accuracy defined a priori in probability. The simulation results are shown to verify the availability of the presented control scheme.

We present a novel real-time framework for non-rigid 3D reconstruction that is robust to noise, camera poses, and large deformation from a single depth camera. KinectFusion has achieved high-quality 3D object reconstructions in real-time by implicitly representing an object’s surface with a signed distance field (SDF) representation from a single depth camera. Many studies for incremental reconstruction have been presented since then, with the surface estimation improving over time. Previous works primarily focused on improving conventional SDF matching and deformation schemes. In contrast to these works, the proposed framework tackles the problem of temporal inconsistency caused by SDF approximation and fusion to manipulate SDFs and reconstruct a target more accurately over time. In our reconstruction pipeline, we introduce a refinement evolution method, where an erroneous SDF from a depth sensor is recovered more accurately in a few iterations by propagating erroneous SDF values from the surface. Reliable gradients of refined SDFs enable more accurate non-rigid tracking of a target object. Furthermore, we propose a level-set evolution for SDF fusion, enabling SDFs to be manipulated stably in the reconstruction pipeline over time. The proposed methods are fully parallelizable and can be executed in real-time. Qualitative and quantitative evaluations show that incorporating the refinement and fusion methods into the reconstruction pipeline improves 3D reconstruction accuracy and temporal reliability by avoiding cumulative errors over time. Evaluation results show that our pipeline results in more accurate reconstruction that is robust to noise and large motions, as well as outperforms previous state-of-the-art reconstruction methods.

This paper considers the leader-following consensus (consensus tracking) problem of multi-agent systems (MAS) with event-triggered communication. A novel hybrid eventtriggering mechanism is proposed to reduce the burden of network transmission, and it is ensured that Zeno behavior does not occur. Besides, it is required that the control protocol is only updated at agent’s own triggering instant. Compared with the existing works, the main contribution is that the minimum inter-event time (MIET) can be pre-specified, which is important for physical implementation, and the control update frequency is also reduced. An example is given to verify the correctness of the proposed hybrid event-triggered control strategy.

The ultra-high-definition 16K Virtual Reality (VR) video is coming to ages with more ”real” virtual experience and less cybersickness. However, the huge bitrate and decoding overhead would overwhelm today’s network and mobile hardware. The widely-known Field-of-View (FoV) adaptation streaming method still has severe bitrate wastes and decoding overhead as it delivers FoV areas with grid-like static tiles. Inspired by this, we present a novel ShiftTile-traCking (STC) streaming scheme, which crops and delivers tiles by tracking FoV movement. It is equivalent to deliver an FoV planar video instead of VR videos. This would save huge bit-rate and reduce decoding complexity. We mainly entail three contributions. 1) To reduce projection distortions, a novel FoV-centric sphere projection is proposed, which projects VR videos with the center of users’ FoVs. 2) To cover diverse FoV movement trajectories with a limited number of tiles, we propose an optimal tiling algorithm by trajectory clustering. 3) To be resilient to FoV prediction errors, we propose an accuracy-sensitive streaming algorithm, which scales FoV areas by the prediction accuracy. The evaluation shows that under the same network conditions, STC improves up to 1.3dB V-PSNR, reduces up to 13.2% buffering ratio, and achieves 60% faster decoding speed (61.5 frames per second) compared with state-of-the-art solutions.

In this paper, the problem of optimal time-varying attitude tracking control for rigid spacecraft with system constraints and unknown additive disturbances is considered. Through the design of a new non-linear tube-based robust model predictive control (TRMPC) algorithm, a dual-loop cascaded tracking control framework is established. The proposed TRMPC algorithm explicitly considers the effect of disturbances and applies tightened system constraints to predict the motion of the nominal system. The obtained optimal control action is then combined with a non-linear feedback law such that the actual system trajectories can always be steered within a tube region centred around the nominal solution. To facilitate the recursive feasibility of the optimization process and guarantee the input-to-state stability of the tracking control process, the terminal controller and the corresponding terminal invariant set are also constructed. The effectiveness of using the proposed dual-loop TRMPC control scheme to track reference attitude trajectories is validated by experimental studies. A number of comparative studies were carried out, and the obtained results reveal that the proposed design is able to achieve more promising constraint handling and attitude tracking performance than that of the other newly developed methods investigated in this research.

Series elastic actuator (SEA) is a promising compliance device due to its lower output mechanical impedance, and it is widely applied to ensure safe human-robot interaction. Although some efforts have been made to achieve accurate stiffness tracking, the time-delay issue in SEA control has still not been well investigated. However, the time delay can cause an inaccurate response and increase the risk of injury. To overcome this problem, this article proposes a fast-response admittance control method for SEAs. First, an admittance control scheme considering the external force estimation is developed for a hydraulic SEA. Then, a parallel adaptive time-series (ATS) (P-ATS) compensator is proposed and further adopted in the admittance control scheme to compensate for the time delay and tracking error. The P-ATS compensator is a modification of the ATS compensator, which is enhanced with a unique parallel mechanism. Such a mechanism can save more computational resources on locating better parameters the for P-ATS compensator, thus improving its performance. Moreover, the parameter setting is converted to an optimization task, which is solved by the whale swarm algorithm (WSA) to achieve higher accuracy. The newly located parameters are compared to the current parameters based on a proposed evaluation criterion, thus guaranteeing the quality of the updated parameters. All the above strategies are employed to improve the SEA admittance control performance. The results obtained from both simulation and real-world experiments validate that, compared to conventional methods, the proposed method achieves a better performance in SEA stiffness tracking with lower time delay and tracking error.

This paper exhibits a novel solution to the speed tracking problem of permanent magnet synchronous motors (PMSMs) subject to parameter and load uncertainties. The resultant cascade structured solution is designed to give the speed tracking capability to the closed-loop system, which depends on only the nominal PMSM parameter information. This study provides two novel contributions: a) a variable performance target system describing the desired perfect tracking nature with boosting feedback gains, and b) a robust control law forcing the target tracking system to be invariant through the utilization of active-damping pole-zero cancellation and disturbance observer techniques. The prototype 1-kW PMSM experimental set confirms the practical advantages of the proposed system compared to the recent auto-tuning controller.

A computational study was carried out on bubble dynamic behaviors and bubble size distributions in a pressurized lab-scale gas-solid fluidized bed of Geldart A particles. High-resolution 3-D numerical simulations were performed using the two-fluid model based on the kinetic theory of granular flow. A fine-grid, which is in the range of 3-4 particle diameters, was utilized in order to capture bubble structures explicitly without breaking down the continuum assumption for the solid phase. A novel bubble tracking scheme was developed in combination with a 3-D Detection and Tracking Algorithm (MS3DATA) and applied to detect the bubble statistics, such as bubble size, location in each time frame and relative position between two adjacent time frames, from numerical simulations. The spatial coordinates and corresponding void fraction data were sampled at 100Hz for data analyzing. The bubble coalescence/break-up frequencies and the daughter bubble size distribution were evaluated by using the new bubble tracking algorithm. The results showed that the bubble size distributed non-uniformly over cross-sections in the bed. The equilibrium bubble diameter due to bubble break-up and coalescence dynamics can be obtained, and the bubble rise velocity follows Davidson’s correlation closely. Good agreements were obtained between the computed results and that predicted by using the bubble break-up model proposed in our previous work. The computational bubble tracking method showed the potential of analyzing bubble motions and the coalescence and break-up characteristics based on time series data sets of void fraction maps obtained numerically and experimentally.

A moving target tracking control problem for marching tank based on adaptive robust feedback control scheme is addressed. A series of preparations is needed for tank gun before shooting a target, the purpose of this paper is to design a control system to fulfill two requirements in this process: the turret-barrel system of tank needs to be adjusted from off-target position to command position and point to the moving target stably when there are strong uncertainties (modeling error, uncertain disturbance with unknown boundaries and road excitation) in the system. Considering the characteristic of coupled interaction, the first thing we do in this paper is to build a coupled analysis model of turret - barrel system with uncertainty term in state-space form. Second, an adaptive robust feedback control scheme is proposed by adding adaptive law to overcome the uncertainty. Third, multi-body dynamics software is used to establish the mechanical mechanism of the tank, and DC-motor module is established in SIMULINK environment, thus the target information and tracking error of the control system is collected and transferred, the gear-ball screw is derived directly by the output torque of the DC-motor module. Finally, the control system and the 3D model are combined together by means of RecurDyn/SIMULINK co-simulation, the turret-barrel system of tank can approximately track the moving target in a certain range. With the adaptive robust feedback control, the target action is completely followed when the target location is constantly changing.

This paper proposes a new active power flow controller for DC-DC multi active bridge (MAB) converters with minimum current point tracking (MCPT) to minimize conductive losses. Unlike recent efforts in this area, the proposed controller does not require look-up table for implementation nor complex non-linear converter modelling and is not circuit parameter-dependent. The proposed control scheme is also generalized for MAB converters with any number of ports and is derived in per unit form to enable standard implementation for buck/boost/unity-gain operating modes. The proposed MCPT algorithm is based on an innovative adaptive perturb and observe (P&O) tracking of the minimum current point (MCP) on the MAB converter current characteristic plane. This MCP is tracked while active power regulation is simultaneously achieved based on a new phase shift decoupler to enable direct regulation of individual port active power. Detailed design procedure of the proposed controller is presented and extensive simulation is carried out to validate the effectiveness of the proposed MCPT closed loop controller. Experimental implementation is also provided to substantiate the simulation results.

We developed a minimum-cost circulation framework for solving the global data association problem, which plays a key role in the tracking-by-detection paradigm of multi-object tracking (MOT). The global data association problem was extensively studied under the minimum-cost flow framework, which is theoretically attractive as being flexible and globally solvable. However, the high computational burden has been a long-standing obstacle to its wide adoption in practice. While enjoying the same theoretical advantages and maintaining the same optimal solution as the minimum-cost flow framework, our new framework has a better theoretical complexity bound and leads to orders of practical efficiency improvement. This new framework is motivated by the observation that minimum-cost flow only partially models the data association problem and it must be accompanied by an additional and time-consuming searching scheme to determine the optimal object number. By employing a minimum-cost circulation framework, we eliminate the searching step and naturally integrate the number of objects into the optimization problem. By exploring the special property of the associated graph, that is, an overwhelming majority of the vertices are with unit capacity, we designed an implementation of the framework and proved it has the best theoretical computational complexity so far for the global data association problem. We evaluated our method with 40 experiments on five MOT benchmark datasets. Our method was always the most efficient in every single experiment and averagely 53 to 1,192 times faster than the three state-of-the-art methods. When our method served as a sub-module for global data association methods utilizing higher-order constraints, similar running time improvement was attained. We further illustrated through several case studies how the improved computational efficiency enables more sophisticated tracking models and yields better tracking accuracy. We made the source code publicly available on GitHub with both Python and MATLAB interfaces.

As a special case of the Volterra system, the second-order Volterra (SOV) filter is very efficient for nonlinear system identification. The improved correntorpy based on the generalized Gaussian density function has been proven robust against impulsive noise. In this brief, we propose several SOV filters based on a recursive maximum correntropy (RMC) algorithm for nonlinear system identification. We first introduce a basic RMC algorithm, which faces a trade-off between filtering accuracy and tracking capability due to the use of a fixed forgetting factor (FFF). Two RMCs with variable FF (VFF) are further proposed to enhance the tracking ability. Simulation results demonstrate that our proposed algorithms outperform existing ones in impulsive noise environments and/or in time-varying systems.

The capability to obtain detailed motility information of model organisms is fundamental to reveal their functional and social behavior characteristics. Zebrafish is a powerful vertebrate model organism. Despite recent success in the automatic quantification of adult zebrafish movement, it remains a laborious task for group zebrafish larval tracking due to their similar appearance, frequent occlusions, and highly discontinuous kinematics. This article presents DanioSense (DS), an automatic tracker for group larval zebrafish, to overcome these tracking challenges. The integration of a light convolutional neural network and a centerline extraction algorithm enables the tracker to localize individuals even in occlusion cases where objects' identities are prone to switch. With reliable detections, an adaptive Kalman filter is designed to optimally estimate locomotive parameters, which is also used for object reidentification accomplished by a two-stage data association protocol. Experimental results demonstrated a tracking accuracy of over 97%, median errors of 102 μm, and 8.8° for the position and orientation measurement, and a processing speed of over 30 frames/s with a normal computer configuration. DS provides detailed quantitative data for a large-scale larvae group in nearly real time, highly boosting the efficiency of characterizing individual phenotypes and analyzing social interactions.

This paper investigates the underwater node dynamic localization and prediction problems in a dynamic sensor network. Node localization in the internet of underwater things is the basis of target tracking and ocean monitoring. At present, most of the node location algorithms assume calm sea and fixed node location. However, the current velocity is uncertain in space and time. The nodes are drifted with the current motion. Therefore, most of the localization algorithms lose efficacy in the actual marine environment. In order to solve the above problems, a node dynamic prediction algorithm is proposed. First, the node mobility model is improved, which is more suitable for the actual marine environment. Second, a frequency based anchor node prediction algorithm is designed to improve anchor node location accuracy. Third, when the ordinary node receives the signals sent by anchor nodes of different depths, a deep information based weighted fusion method is designed for the ordinary node localization in order to mine more information in each direction. Finally, location and prediction simulation in sensor networks is carried out. The results show that the proposed node localization and prediction algorithm is more accurate than SMLP and HLMP algorithms and prove the enhanced effect of our method in dynamic marine.

Single nanoparticle tracking (SPT) is a unique and powerful tool to investigate the interaction between nanoparticles and cells, which is of considerable importance for nanotechnology applications ...

Distributed fast finite-time tracking consensus (FTTC) is investigated for first-order nonlinear multi-agent systems (MASs), where the leader has an external input unknown to all followers. Based on global finite-time stability and Lyapunov stability, two distributed tracking protocols are proposed to guarantee fast FTTC. First, a basic distributed non-smooth protocol for solving this problem is proposed and analyzed. A limitation of the designed protocol is that the control gains should depend on some global information like the spectrum of Laplacian matrix. To remove this limitation, a fully distributed protocol is further designed and analyzed. Theoretical studies imply that the states of the considered MASs will also achieve fast FTTC. Finally, two simulation examples are performed on Chua’s circuit system to verify the theoretical results.

In this paper, the finite-iteration tracking issue of multi-agent systems is investigated with the distributed optimization method. Since the infinite-iteration strategy is unrealistic, the finite-iteration approach is adopted to study the problem. Moreover, the tracking performance is undesired for the traditional iterative learning schemes. Then optimization technique is used to cover the drawback. The tracking error is regarded as the optimization objective, and the consensus tracking issue is considered as the distributed optimization problem. An appropriate learning control strategy is designed on the basis of the gradient of the tracking error. Further, the optimization issue can be solved by means of the finite-iteration approach, and the learning algorithm can improve the tracking performance. Theoretical results are proposed by using the norm estimation and some inequalities skills. At last, a numerical simulation is given to illustrate the effectiveness of the main results.

This work presents a novel methodology for systematically processing the time series that report the number of positive, recovered and deceased cases from a viral epidemic, such as Covid-19. The main objective is to unveil the evolution of the number of real infected people, and consequently to predict the peak of the epidemic and subsequent evolution. For this purpose, an original nonlinear model relating the raw data with the time-varying geometric ratio of infected people is elaborated, and a Kalman Filter is used to estimate the involved state variables. A hypothetical simulated case is used to show the adequacy and limitations of the proposed method. Then, several countries, including China, South Korea, Italy, Spain, UK and the USA, are tested to illustrate its behavior when real-life data are processed. The results obtained clearly show the beneficial effect of the social distancing measures adopted worldwide, confirming that the Covid-19 epidemic peak is left behind in those countries where the outbreak started earlier, and anticipating when the peak will take place in the remaining countries.

The robot-held laparoscope efficiently maintains the stability of the surgical screen in comparison with the laparoscope held by the assistant. The collaboration between the robot-held laparoscope with the surgeon is achieved without the artificial manipulation by tracking surgical instruments automatically. In this work, a visual tracking space vector method is proposed to autonomously control the field-of-view (FOV) by tracking multiple surgical instruments. In the proposed method, a visual tracking space vector is introduced to track the motion of multiple instruments, and a constraint vector is defined to ensure that the laparoscope is always within the safe working space. In addition, a modified YOLOv3 algorithm is proposed to robustly detect and classify the surgical instruments. The proposed method automatically adapts to the changes in the number of surgical instruments, as compared to other visual servoing methods for tracking surgical instruments. The proposed method does not require the depth information of the tracked surgical instruments. Experimental and simulation results verify that the proposed method accurately and efficiently controls the robot-held laparoscope to track the motion of multiple surgical instruments and ensure safe tracking movement.