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The article presents the design of an adaptive controller, simulation, code generation, and porting of the algorithm into target hardware, movement of the 7.5 m antenna, testing, verification, and validation of theoretical and experimental results of the antenna system. The conventional servo control methods based on PID controllers are being used for tracking X-band missions. As the need for higher data rates is increasing, missions are switching to the Ka band, as a result the satellite ground stations will have to track the satellite in the Ka band where the antenna band beam width is narrowed by a factor of 3 when compared to the X-band antenna beamwidth. These challenges had driven the designer to design and implement a Model Reference Adaptive Controller over PID to improve and achieve the tracking accuracy of less than 25m° from the present 30 m°. Thus, consistent and constant performance is achieved throughout the trajectory tracking of the mission using PID + MRAC (Model Reference Adaptive Control). MRAC uses a reference model that specifies the desired response of the antenna system and adjusts the controller parameters such that the plant follows the reference to achieve the desired performance. The entire drive chain modules and their parameters are considered in the design of the plant, namely MRAC controller, PID controller, Power Drive electronic (Rectifier and Inverter), Servo Motor, mechanical torque coupling, Gear reducer, Slew Ring Bearing (SRB), and absolute encoder modules of each axis for position feedback from the antenna. Model Order Reduction of higher-order actual Plant model to the standard second-order system is optimized. The modelled and analysed outcomes of the MRAC scheme using the Modified MIT are demonstrated. The developed model is tested against step, ramp, parabolic, satellite trajectory, velocity and acceleration inputs in MATLAB/Simulink and validated with practical/ experimental test results with antenna load at the satellite ground Station. The achieved results indicate that the PID + MRAC controller has significant and consistent performance improvements when compared to the PID controller alone. [ABSTRACT FROM AUTHOR]

To enhance the safety and adaptability of human-machine interaction, a novel variable stiffness robot joint prototype was proposed. This prototype employed permanent magnets and hollow electromagnetic coils to form the variable stiffness mechanism, enabling rapid adjustment of joint stiffness by manipulating the system’ s magnetic flux. The stiffness model of the variable stiffness joint was successfully established based on molecular current method and virtual displacement method. Subsequently, controllers for coordinating joint position and stiffness, as well as a fuzzy adaptive controller, were developed. Experimental results indicate that increasing joint stiffness leads to improved accuracy in joint position and trajectory tracking, albeit with a decreasing trend in precision as motion frequency rises. The fuzzy adaptive controller outperforms traditional controllers in controlling the step response and trajectory tracking performance of the variable stiffness joint, exhibiting superior tracking accuracy and faster response speed. [ABSTRACT FROM AUTHOR]

In order to realize effective laser communication on underwater mobile platforms, the active tracking and alignment technology of underwater laser communication (UWLC) is studied. Firstly, the servo control principle of the UWLC system is analyzed. Secondly, aiming at underwater disturbance, an adaptive fuzzy PID controller is designed to realize parameter self-tuning, thereby improving the system's anti-disturbance ability. The designed algorithm was simulated, and the simulation results show that the adaptive fuzzy PID algorithm has better anti-disturbance ability and tracking performance than the traditional PID. Finally, an experimental platform was built for dynamic tracking experiments, and the results show that the dynamic tracking accuracy of the designed control algorithm was improved by 30.29% compared with the traditional control algorithm, which provides a certain reference for the development of laser communication on underwater moving platforms. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control 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.)

To solve the problem that in traditional switching strategies for lateral tracking control algorithms in intelligent vehicles, the model matching strategy was always in active state with consuming computational resources and reducing motion smoothness, a control mode switching strategy based on least squares support vector machine (LSSVM) was designed. By the strategy, the control modes were dynamically switched based on error assessment under varying operational conditions. The control effectiveness was evaluated from three aspects of safety, comfort and tracking accuracy. The optimal control amount was executed in real time based on a multi-objective optimization mathematical model. The simulation results show that by the switching strategy, the superior lateral tracking performance in intelligent vehicles can be achieved under varying conditions with path tracking deviation range of ±0.1 m, yaw rate fluctuation range of ±2 (°)/s and jerk value range of ±10 m/s³. Compared to traditional single control algorithms, the proposed strategy exhibits improved tracking accuracy, stability and comfort. [ABSTRACT FROM AUTHOR]

In order to realize effective laser communication on underwater mobile platforms, the active tracking and alignment technology of underwater laser communication (UWLC) is studied. Firstly, the servo control principle of the UWLC system is analyzed. Secondly, aiming at underwater disturbance, an adaptive fuzzy PID controller is designed to realize parameter self-tuning, thereby improving the system’s anti-disturbance ability. The designed algorithm was simulated, and the simulation results show that the adaptive fuzzy PID algorithm has better anti-disturbance ability and tracking performance than the traditional PID. Finally, an experimental platform was built for dynamic tracking experiments, and the results show that the dynamic tracking accuracy of the designed control algorithm was improved by 30.29% compared with the traditional control algorithm, which provides a certain reference for the development of laser communication on underwater moving platforms.

By the evolution of context-aware application, indoor location positioning gains much more attention worldwide. Radio Frequency Identification (RFID) is one such wireless positioning technology adopted widely. Moreover, enhancing the tracking accuracy and thus reducing the tracking error remains a challenging task in RFID based indoor environment. So as to overcome this aspect, a localization and tracking method depending on virtual reference tag (VRT) is employed in the indoor environment. This research work includes four modules like (i) data collection and Received Signal Strength Indication (RSSI) estimation;(ii) Optimal allocation of VRT using Spider Monkey Optimization (SMO) at which the allocation of VRT is made by considering number of tags, environmental factors (humidity and temperature), and SNR. Based on this data, the RFID reader allocates VRT for each grid so as to increase the tracking accuracy; (iii) Artificial Neural Network (ANN) module-based localization at which the localization of unknown tags is made; and (iv) tracking of moving target tag is carried based on Aquila Swarm Optimization-Long short-term memory (ASO-LSTM) approach. The optimization at this stage is employed to choose optimal position which enhances the accuracy of tracking. Thus, finally the location estimation is carried by this approach. The simulation is carried in NS3.26 network simulator and the performance are estimated in terms of SNR, tracking error, mean error, tracking accuracy, mean time, and standard deviation. The performance value is then compared with traditional models to show the enhancement of proposed mode over the other conventional techniques. [ABSTRACT FROM AUTHOR]

Heliostats are integral components of tower solar thermal power generation systems, optimizing heliostat efficiency directly impacts overall power generation effectiveness. This research focuses on evaluating and enhancing heliostat optical quality and tracking accuracy, critical factors influencing their concentration efficiency. The study presents a comprehensive approach based on spot evaluation and correction techniques to assess and boost heliostat performance. Static Optical Quality Assessment, A novel methodology is introduced to appraise heliostat optical quality. It involves capturing heliostat spot shapes through image processing, followed by fitting and comparison with theoretical simulations. This technique provides valuable insights into heliostat mirror quality. Dynamic Tracking Accuracy Evaluation scheme is devised to evaluate dynamic tracking accuracy, by analyzing centroid positions of spots captured at regular intervals, horizontal and vertical tracking deviation angles are computed. These angles gauge heliostat dynamic tracking accuracy. To refine dynamic tracking accuracy, a heliostat tracking error correction scheme is proposed. A dynamic geometric tracking error model is formulated, enabling the derivation of a precise tracking angle calculation formula. A least squares mathematical model is established to solve for unknown tracking errors, facilitating accurate error angle calculation and subsequent correction. The presented static spot quality and dynamic tracking accuracy evaluation methods offer simplicity, precision, and efficiency. These techniques hold practical significance for tower solar thermal power generation systems. The devised tracking error correction scheme demonstrates practical effectiveness, validated through experimental simulations and real-world measurements. Implementation of this scheme substantially enhances concentrating power generation efficiency within tower solar thermal power generation systems. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor size adjustability and low joint tracking accuracy of lower limb exoskeleton rehabilitation robot (LLERR), a variable size lower limb exoskeleton rehabilitation robot (VSLLERR) was designed by UG NX software based on human body size data. Then, the kinematics model of VSLLERR was established by DH method, and the motion space of VSLLERR was analyzed. In addition, the dynamics model of VSLLERR was established by Lagrangian energy method, and the general nonlinear friction model was designed to modify and improve the accuracy of dynamics model. Then, the PID and reaching law (RL) controllers of VSLLERR were designed by SIMULINK. Furthermore, the joint tracking accuracy of the two controllers and the influence of RL controller parameters on tracking accuracy were studied by simulation experiment. The results indicate that the joint angle and joint angular velocity tracking accuracy of RL controller are higher than that of PID controller. In addition, appropriate parameters (c1, c2, k, ε, ϕ) can improve the tracking accuracy of VSLLERR. [ABSTRACT FROM AUTHOR]

In order to improve the tracking accuracy of PMLSMs, a fractional-order feedback control method was proposed based on closed-loop identification model. By precisely adjusting the error amplitude characteristics of the feedback control system in a specific fractional-order form, the following errors of PMLSMs might be effectively suppressed. A closed-loop identification model was derived for the PMLSM and the parameter selection method of the fractional-order lead-lag compensator was determined based on analysis of the error amplitude characteristics. With the properly defined fractional-order parameters, the fractional-order feedback control system might accurately describe the frequency-domain characteristics of the PMLSM, and the identification accuracy and control performance of the PMLSM might thus be improved. The proposed method was experimentally validated on a PMLSM gantry motion platform through identification methods, tracking experiments with different motion planning, and circular trajectory motion experiments. The experiments on system closed-loop identification and motion tracking with different plannings were performed. The experimental results show that the closed-loop identification method may effectively suppress the influences of external interferences on the identification signals, and the closed-loop identification model is accurate for the control system, the proposed fractional-order feedback control method may greatly reduce the tracking errors of the PMLSMs, and compared to the PID control method, the proposed control method may reduce the root mean square error (RMSE) tracking errors by at least 82.47% under different motion scenarios. The experimental results demonstrate the effectiveness of the proposed method in improving the tracking accuracy of PMLSMs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui 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.)

Relational boredom is an important cognitive-emotional experience that is understudied in the relationship maintenance literature. In three dyadic studies, we investigated accuracy and bias in partners' perceptions of each other's relational boredom, and how accurate and biased boredom perceptions were associated with relationship quality. Results revealed that, overall, partners tended to overestimate—but accurately track—each other's relational boredom across the features that comprise relational boredom and across time. Additionally, when people accurately perceived their partner experiencing high levels of boredom, they reported lower relationship quality; in all other cases, one's own relationship quality was preserved. Furthermore, when people accurately perceived their partner experiencing high levels of boredom, their partner also reported lower relationship quality, while the partner's relationship quality was consistently preserved when the perceiver was accurate at low levels of boredom or overestimated. These findings have important implications for how couples navigate boredom and maintain long-term relationships. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient tracking accuracy caused by object occlusion in the process of multi-object tracking, this paper proposes a multi-order semantic fusion pedestrian multi-object tracking network. Firstly, the feature pyramid attention module is used in the backbone network to enlarge the receptive field and obtain more abundant feature information to improve the detection accuracy of different scale objects. Secondly, a size-aware module is integrated into the pedestrian re-identification branch network to fuse semantic features from different resolutions and extract more basic pedestrian features, thereby improving the tracking accuracy. Finally, the detection head is reconstructed and the small object detection layer is fused to make the proposed network adapt to objects of different sizes. Experiments on the MOT16 and MOT17 datasets show that the multi-object tracking accuracy of the proposed network reaches 75.4% (MOT16) and 74.3% (MOT17), which effectively deals with the problem of low tracking accuracy caused by occlusion in the field of autonomous driving, and achieves good tracking results. The network proposed in this paper improves the tracking accuracy of pedestrians and provides a basis for further practical applications. [ABSTRACT FROM AUTHOR]

Cascaded iterative learning control (CILC) is explored for a magnetically levitated (maglev) planar motor to achieve excellent tracking motion performance in this paper. The CILC control method is based on traditional iterative learning control (ILC) with deeper iterations. CILC solves the difficulty of ILC in constructing perfect learning filter and low-pass filter to obtain excellent accuracy. Specifically, in CILC, the traditional ILC strategy is implemented several times by the operation of feedforward signal registering and clearing in a cascaded structure, which makes the motion error reach an accuracy level superior to traditional ILC even though the filters are imperfect. The fundamental principle, convergence and stability of CILC strategy are explicitly presented and analyzed. Through the structure of CILC, the repetitive component of the convergence error can be completely eliminated in theory, while the non-repetitive component is accumulated but the sum is bounded. Simulation investigation and comparative experimental investigation on maglev planar motor are both conducted. The results consistently show that the CILC strategy is not only superior to PID and model-based feedforward control, but also obviously outperforms traditional ILC. The CILC investigations on maglev planar motor also provide a clue that CILC has appreciable application prospect for precision/ultra-precision systems requiring extreme motion accuracy. • Cascaded Iterative Learning Control (CILC) strategy is explored for maglev planar motor to achieve excellent motion control accuracy. • CILC is proven to be stable and can eliminate the motion error caused by repetitive disturbance perfectly in theory regardless of the imperfection of learner or Q-filter, while the traditional ILC method cannot if the learner or Q-filer is not ideal. • Sufficient simulations and experiments are conducted on a developed maglev planar motor system, and the results consistently show that the achieved control performance of CILC is superior to traditional ILC control method. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature 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.)

The second-order ILC algorithm is studied for a class of linear discrete-time switched systems with model uncertainties, external noises and the time-delay for tracking reference trajectory by taking advantage of super-vector representation. This study is based on the assumption that the systems operate under finite intervals. A sufficient condition for convergence of the algorithms is deduced when the model uncertainties and external noises are absent. Then the convergence is analysed, when the model uncertainties are present but the external noises are absent and the robustness against the bounded external noises is discussed. The analysis manifests that the second-order ILC algorithm is feasible and effective when it is imposed on the linear switched systems specified by the arbitrarily present switching rules when they are imposed on the system. We have presented a simulation example to illustrate effectiveness of the proposed second-order ILC algorithm in the study of robustness of the considered SISO linear discrete time-invariant time delay switched system. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient tracking accuracy caused by object occlusion in the process of multi-object tracking, this paper proposes a multi-order semantic fusion pedestrian multi-object tracking network. Firstly, the feature pyramid attention module is used in the backbone network to enlarge the receptive field and obtain more abundant feature information to improve the detection accuracy of different scale objects. Secondly, a size-aware module is integrated into the pedestrian re-identification branch network to fuse semantic features from different resolutions and extract more basic pedestrian features, thereby improving the tracking accuracy. Finally, the detection head is reconstructed and the small object detection layer is fused to make the proposed network adapt to objects of different sizes. Experiments on the MOT16 and MOT17 datasets show that the multi-object tracking accuracy of the proposed network reaches 75.4% (MOT16) and 74.3% (MOT17), which effectively deals with the problem of low tracking accuracy caused by occlusion in the field of autonomous driving, and achieves good tracking results. The network proposed in this paper improves the tracking accuracy of pedestrians and provides a basis for further practical applications.

Traditional position-speed-current loop nested structure cannot satisfy the requirements in industrial applications with high trajectory tracking accuracy and fast dynamic response like vehicle-used laser radars and wafer stages. Thus, a position and speed parallel control structure is proposed to achieve high tracking accuracy in this paper. First, the mechanism analysis and parameters tuning method of the classical PI-Lead structure and two traditional three-closed-loop structures, P-PI-PI and PI-P-PI are given. In the proposed position and speed parallel control structure based on improved active damping, the speed controller with proportional control is connected in parallel with the position controller of the PI-Lead structure to improve the stability of the PI-Lead structure and maintain its accuracy. Finally, comparative experiments between the proposed parallel structure and traditional three-closed-loop structure are carried out on commercial-grade microminiature drivers under permanent magnet synchronous motors, the experimental results indicate that the parallel structure can improve the dynamic response performance effectively and enhance the stability of the system.

Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this paper an improved Q-learning-based sensor-scheduling algorithm for multi-target tracking (MTT-SS). First, we devise an entropy weight method (EWM)-based strategy to evaluate the priority of targets being tracked according to target properties and network status. Moreover, we develop a Q-learning-based task allocation mechanism to obtain a balanced resource scheduling result in multi-target-tracking scenarios. Simulation results demonstrate that our proposed algorithm can obtain a significant enhancement in terms of tracking accuracy and energy efficiency compared with the existing sensor-scheduling algorithms. [ABSTRACT FROM AUTHOR]

As an effective application form for large-scale and efficient use of distributed power, microgrid not only realizes the flexible control of distributed power but also provides reliable power supply for the load. However, the power quality on the load side will be severely deteriorated when the microgrid is disturbed. To cope with this issue, an improve linear active disturbance rejection control (LADRC) technique is proposed in this article. In this technology, the idea of series correction is introduced into the disturbance observation channel of linear extended state observer. This strategy can achieve more accurate tracking of disturbance items through the correction link, and handle the unknown modeling error and external disturbances of the system as an overall disturbance through a single structure. Then, according to the linear feedback law to achieve good control of the output-side converter. Moreover, the traceability, convergence, and stability of ILADRC are analyzed. More importantly, guidance on parameters configuration is given on the basis of performance analysis. Finally, the validity of the proposed improved LADRC technique is verified on a 40-kW experiment platform. [ABSTRACT FROM AUTHOR]

As one of the most promising technologies for next-generation mobile platforms, Augmented Reality (AR) has the potential to radically change the way users interact with real environments enriched with various digital information. To achieve this potential, it is of fundamental importance to track and maintain accurate registration between real and computer-generated objects. Thus, it is crucially important to assess tracking capabilities. In this paper, we present a benchmark evaluation of the tracking performances of some of the most popular AR handheld devices, which can be regarded as a representative set of devices for sale in the global market. In particular, eight different next-gen devices including smartphones and tablets were considered. Experiments were conducted in a laboratory by adopting an external tracking system. The experimental methodology consisted of three main stages: calibration, data acquisition, and data evaluation. The results of the experimentation showed that the selected devices, in combination with the AR SDKs, have different tracking performances depending on the covered trajectory. [ABSTRACT FROM AUTHOR]

Current and recent relationship evaluations offer important information about the strength and stability of relationships. The diagnostic value of relationship evaluations should motivate people to accurately recall recent changes in relationship quality (tracking accuracy) but may also increase sensitivity to times of low relationship quality (directional bias). Such a vigilant pattern of tracking accuracy and negatively biased memories of relationship quality should be amplified for those high in attachment anxiety. The current study used repeated assessments to compare memories of relationship quality across several weeks with actual evaluations of relationship quality gathered in prior weeks (787 weekly reports, N = 113, 94% women). People high in attachment anxiety more accurately tracked the ups and downs of past relationship quality, but also remembered times of low relationship quality to be lower than actually reported. People high in attachment anxiety also experienced greater depressed moods across weeks when they remembered their relationship quality more negatively. These results have important implications for understanding inconsistencies in prior research and the links between attachment anxiety and depressed mood. [ABSTRACT FROM AUTHOR]

Path tracking control is a key technology in the hierarchical unmanned driving system. Its function is to control the vehicle so that it drives along the reference path given by the path planning system. The information such as the position and posture of the vehicle required for path tracking control is provided by the perception and positioning system. In recent years, the development of path tracking control has been very rapid, and researchers have published considerable research. As there are some common technical problems and solutions in path tracking control under the same or similar scenarios, recent research results are reviewed from the perspective of both low-speed and high-speed path tracking control. In the research of low-speed path tracking control, researchers pay more attention to the influence of system constraints on the accuracy of path tracking such as front-wheel angle speed. At present, methods to reduce the influence of system constraints include: (1) taking the system constraints into consideration when planning a reference path; (2) using preview control to make the controller respond early; and (3) using model predictive control methods, such as linear model predictive control (LMPC) or non-linear model predictive control (NMPC), as path tracking control methods. NMPC can reduce the impact of system constraints and does not need manual setting of preview distance. It has strong resistance to disturbance factors such as positioning errors. Since low-speed path tracking control has low real-time requirements, it can be considered that NMPC can meet most needs of low-speed path tracking control. High-speed path tracking control, in addition to being affected by system constraints, is also challenged by insufficient driving stability caused by higher vehicle speeds. Therefore, LMPC, which can take the dynamics-level complex system constraints into account, has a lower computational cost. It is often used as the path tracking control method. However, due to high-speed path tracking control, there is a coupling relationship between path tracking accuracy and vehicle driving stability. The use of dynamics-level LMPC or other dynamics-level control methods cannot completely solve the problem caused by this coupling relationship. The current common solution is to add an extra speed adjustment module or weight distribution module to path tracking control. Additionally, in high-speed path tracking control, the influence of environmental parameters, such as ground adhesion coefficient, is also greater. Hence, the estimation of environmental parameters, such as ground adhesion coefficient, has also become an important research direction in the field of high-speed path tracking control.

The radar target tracking filtering algorithm is an important part of radar signal processing, which plays an important role in air defense early warning, battlefield surveillance, missile guidance and other fields. This paper presents a target tracking algorithm based on a new steepest descent method. Firstly, a motion model based on the improved polynomial fitting model is established, and then a new steepest descent method is used to solve the optimal parameters of the motion model. The real-time optimal motion model is used to predict and track the moving target, and the regularization idea is used to remove the influence of noise. The simulation results show that the algorithm has higher accuracy, less computation and better real-time performance m the case of maneuvering and non-maneuvering targets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power 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.)

Servo systems driven by a permanent magnet linear synchronous motor (PMLSM) are often affected by uncertain disturbances, such as magnetic resistance, friction, and external disturbances, which increase tracking errors and reduce motion accuracy. Traditional control methods may have difficulty to achieve satisfactory control performance in terms of tracking accuracy and disturbance rejection. In this paper, we propose an internal model control PID method based on a model linear extended state observer, which is termed as IMC-PID-MLESO method. With this method, the nominal model parameters of the PMLSM servo system are obtained via system identification. The model linear extended state observer (MLESO), with known parameter information, is designed to improve the estimation accuracy for the system states and total unknown uncertainties. As the uncertainties are compensated in the feedback control law, the PMLSM servo system model is transformed into a known nominal model. Based on the nominal model, the IMC-PID feedback controller is designed to ensure satisfactory performance on disturbance rejection and tracking error reduction. Simulation and experimental results show that the IMC-PID-MLESO method can effectively improve the position tracking accuracy of the PMLSM servo system and rapidly suppress the system disturbance.

This study addresses the tracking–learning–detection (TLD) algorithm for long‐term single‐target tracking of moving vehicle from video streams. The problems leading to tracking failures in existing TLD methods are discovered, and an improved TLD (ITLD) tracking algorithm is proposed which is more robust to object occlusion and illumination variation. A square root cubature Kalman filter (SRCKF) is employed in the tracker of TLD to predict the position of the object when occlusion occurs. Besides, this study introduces fast retina keypoint (FREAK) feature into the tracker to alleviate the instability caused by illumination variation or scale variation. The overlap comparison and the normalised cross‐correlation coefficient (NCC) are introduced to the integrator of the TLD to obtain reliable bounding boxes with improved tracking precision. Experiments are conducted to compare the performance of the state‐of‐the‐art trackers and the proposed method, using the object tracking benchmark that includes 50 video sequences (OTB‐50) and TLD datasets. The experimental results show that the proposed ITLD outperforms on both tracking accuracy and robustness. The proposed method can track a moving vehicle even when it is temporally totally occluded.

We used the truth and bias model to examine changes in tracking accuracy and under/overestimation (directional bias) on therapists' judgments about clients' satisfaction. We examined 3 factors of clinical experience that could moderate accuracy: (a) overall level of acquaintanceship with a client, operationalized as treatment length (i.e., less or more time seeing a client), (b) time point in therapy with a specific client, operationalized as session number (i.e., earlier or later in treatment with a client), and (c) order (1st client seen, 2nd client seen . . . last client seen across two years of training in a psychology clinic) in which clients were seen. We conducted a three-level hierarchical linear modeling using data on 6054 sessions, nested in 284 adult clients, nested in 41 doctoral student therapists providing open-ended psychodynamic individual psychotherapy. We found that therapists were able to accurately track client-rated session evaluations with less underestimation (i.e., lower tendency to estimate that clients were less satisfied than they actually were) as they gained experience (both treatment length and client order). Furthermore, therapists exhibited greater tracking accuracy gains over the span of shorter treatments and when working with clients earlier in their clinical training. In longer treatments and with clients seen later in training, tracking accuracy was stable and consistent. Implications for research and practice are discussed. [ABSTRACT FROM AUTHOR]

In order to simultaneously provide excellent tracking performance as well as good generalization for precision motion control of magnetically levitated (maglev) planar motors, this article proposes a feedforward compensation strategy with precise reference modification prediction using gated recurrent units (GRU). First, a GRU network is constructed, which is capable of precisely predicting the reference modification after training with data collected from a maglev planar motor. Essentially, the GRU network can be viewed as a data-based model which captures the dynamic characteristics of appropriate reference modification and gives a prediction even before implementing the real trajectory. Consequently, an excellent feedforward compensation part can be formed, which is important in the whole control structure. Comparative experimental investigation validates the effectiveness of the GRU-based modification prediction ability and demonstrates the excellent tracking performances of the proposed control strategy. The proposed strategy not only performs as well as iterative learning control, but also does not need any time-consuming iterations, which is valuable for industrial applications. [ABSTRACT FROM AUTHOR]

Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this paper an improved Q-learning-based sensor-scheduling algorithm for multi-target tracking (MTT-SS). First, we devise an entropy weight method (EWM)-based strategy to evaluate the priority of targets being tracked according to target properties and network status. Moreover, we develop a Q-learning-based task allocation mechanism to obtain a balanced resource scheduling result in multi-target-tracking scenarios. Simulation results demonstrate that our proposed algorithm can obtain a significant enhancement in terms of tracking accuracy and energy efficiency compared with the existing sensor-scheduling algorithms.

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.)

Here, the authors propose a novel tracking algorithm that can automatically modify the initial configuration of a target to improve the tracking accuracy in subsequent frames. To achieve this goal, the authors’ method analyses the likelihood landscape (LL) for the image patch described by the initial configuration. A good configuration has a unimodal distribution with a steep shape in the LL. Using the LL analysis, the authors’ method improves the initial configuration, resulting in more accurate tracking results. The authors improve the conventional LL analysis based on two ideas. First, the authors’ method analyses the LL in the RGB space rather than the grey space. Second, the method introduces an additional criterion for a good configuration: a high likelihood value at the mode. The authors further enhance their method through post‐processing of the visual tracking results at each frame, where the estimated bounding boxes are modified by the LL analysis. The experimental results demonstrate that the authors’ advanced LL analysis helps improve the tracking accuracy of several baseline trackers on a visual tracking benchmark data set. In addition, the authors’ simple post‐processing technique significantly enhances the visual tracking performance in terms of precision and success rate.

In this paper, in order to improve the tracking precision and convergence speed of singular systems with measurement noise in the limited interval, an iterative learning control algorithm with learning gain self-regulation is proposed under the condition of incompletely known model information of the singular systems. The proposed self-regulation learning gain is constructed by using the left multiplication of a diagonal matrix by constant learning gain. The diagonal matrix has consisted of nonlinear functions with self-regulation characteristic according to error amplitude. And, the contraction mapping method is used to prove that the proposed algorithm can make the tracking error converge to a bound as the iterations increase in the limited time interval. The bound is only related to both system parameter and external disturbance. When the external disturbance is completely eliminated and iterations converge to infinite, system output can track precisely the desired trajectory. At the same time, the sufficient condition of the algorithm is derived. Furthermore, the simulation results show the effectiveness of the proposed algorithm.

As one of the most promising technologies for next-generation mobile platforms, Augmented Reality (AR) has the potential to radically change the way users interact with real environments enriched with various digital information. To achieve this potential, it is of fundamental importance to track and maintain accurate registration between real and computer-generated objects. Thus, it is crucially important to assess tracking capabilities. In this paper, we present a benchmark evaluation of the tracking performances of some of the most popular AR handheld devices, which can be regarded as a representative set of devices for sale in the global market. In particular, eight different next-gen devices including smartphones and tablets were considered. Experiments were conducted in a laboratory by adopting an external tracking system. The experimental methodology consisted of three main stages: calibration, data acquisition, and data evaluation. The results of the experimentation showed that the selected devices, in combination with the AR SDKs, have different tracking performances depending on the covered trajectory.

This paper proposed a monopulse method using multiple frequencies to overcome multipath interference in the tracking of low-altitude sea targets. The method determines target elevation candidates by comparing the difference between the measured complex monop- ulse ratios and those calculated considering multipath interference at three adjacent frequencies. The final target elevation is determined among the candidates by using a cost function set based on the characteristics of the results considering multipath interference. Because the proposed method requires solely the measured target distance and monopulse ratios, it can be applied to the existing monopulse radar system without additional configuration. The suppression effect of multipath interference was verified by a simulation conducted for tracking low-altitude targets in a multipath environment. [ABSTRACT FROM AUTHOR]

A method to improve the tracking accuracy of low-altitude sea targets is proposed using an adaptive beamforming algorithm in a multipath environment. It is confirmed that the target elevation can be estimated with relatively high accuracy without repetitive calculation using the proposed method in analog beamforming active electronically scanned array radar. The proposed method improves the tracking accuracy by adding a side-lobe suppression condition to the existing adaptive beamforming constraint for mono pulse inclination control while accounting for the characteristics of the low-altitude sea target with less elevation change. In addition, various adaptive beamforming algorithms were applied to the proposed method, and the results were compared and analyzed to optimize tracking accuracy. [ABSTRACT FROM AUTHOR]

To simultaneously achieve accurate tracking error prediction, rigorous motion accuracy, and certain robustness to parameter variations and unknown disturbances, this article proposes a data-based learning adaptive robust control (LARC) strategy based on gated recurrent unit (GRU) neural network. Firstly, parameter adaptive control and robust control are utilized to guarantee the robustness against parametric uncertainties and unknown disturbances. A GRU neural network is then constructed and capable of precisely predicting the tracking error after training with data collected from a linear-motor-driven stage. Essentially, the GRU network can be viewed as a data-based model, which captures the tracking error dynamic characteristics and provides a prediction even before implementing the real trajectory. Consequently, a reference modification and a feedforward compensation part can be formed, which is the significant part of the whole LARC control structure. Comparative experimental investigation not only validates the effectiveness of the tracking error prediction ability, but also demonstrates the practically satisfactory transient/steady-state tracking performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This review covers global maximum power point tracking (GMPPT) methods for photovoltaic (PV) systems under partial shading conditions. Unlike the previous review works that primarily focused on soft computing and hybrid GMPPT, this study gives exclusive attention to the improvement achieved by the conventional MPPT (perturb and observe, hill climbing, and incremental conductance). The improved methods include the popular 0.8 × Voc model and, more recently, the skipping algorithms. In addition to providing qualitative descriptions of the available techniques, this work also attempts to provide a fair evaluation of GMPPT to determine their comparative performances. The competing algorithms, which are selected to represent every category (conventional and soft computing and hybrid MPPT), are benchmarked under carefully selected operating conditions and shading scenarios. The evaluation is focused on four main criteria: tracking accuracy, convergence time, length of voltage fluctuations, and transient efficiency during the search for the global maximum power point. The results obtained from this study can become a basis for researchers and designers to select the best MPPT technique for their respective applications. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the effects of CT protocol and in‐room x‐ray technique on CyberKnife® (Accuray Inc.) tracking accuracy by evaluating end‐to‐end tests. Methods: End‐to‐end (E2E) tests were performed for the different tracking methods (6D skull, fiducial, spine, and lung) using an anthropomorphic head phantom (Accuray Inc.) and thorax phantom (CIRS Inc.). Bolus was added to the thorax phantom to simulate a large patient and to evaluate the performance of lung tracking in a more realistic condition. The phantoms were scanned with a Siemens Sensation Open 24 slice CT at low dose (120 kV, 70 mAs, 1.5 mm slice thickness) and high dose (120 kV, 700 mAs, 1.5 mm slice thickness) to generate low‐dose and high‐dose digitally reconstructed radiographs (DRRs). The difference in initial phantom alignment, Δ(Align), and in total targeting accuracy, E2E, were obtained for all tracking methods with low‐ and high‐dose DRRs. Additionally, Δ(Align) was determined for different in‐room x‐ray imaging techniques (0.5 to 50 mAs and 100 to 140 kV) using a low‐dose lung tracking plan. Results: Low‐dose CT scans produced images with high noise; however, for these phantoms the targets could be easily delineated on all scans. End‐to‐end results were less than 0.95 mm for all tracking methods and all plans. The greatest difference in initial alignment Δ(Align) and E2E results between low‐ and high‐dose CT protocols was 0.32 and 0.24 mm, respectively. Similar results were observed with a large thorax phantom. Tracking using different in‐room x‐ray imaging techniques (mAs) corresponding to low exposures (resulting in high image noise) or high exposure (resulting in image saturation) had alignment accuracy Δ(Align) greater than 1 mm. Conclusions: End‐to‐end targeting accuracy within tolerance (<0.95 mm) was obtained for all tracking methods using low‐dose CT protocols, suggesting that CT protocol should be set by target contouring needs. Additionally, high tracking accuracy was achieved for in‐room x‐ray imaging techniques that produce high‐quality images. [ABSTRACT FROM AUTHOR]

Enhancing patients' engagement is of great benefit for neural rehabilitation. However, physiological and neurological differences among individuals can cause divergent responses to the same task, and the responses can further change considerably during training; both of these factors make engagement enhancement a challenge. This challenge can be overcome by training task optimization based on subjects' responses. To this end, an engagement enhancement method based on human-in-the-loop optimization is proposed in this paper. Firstly, an interactive speed-tracking riding game is designed as the training task in which four reference speed curves (RSCs) are designed to construct the reference trajectory in each generation. Each RSC is modeled using a piecewise function, which is determined by the starting velocity, transient time, and end velocity. Based on the parameterized model, the difficulty of the training task, which is a key factor affecting the engagement, can be optimized. Then, the objective function is designed with consideration to the tracking accuracy and the surface electromyogram (sEMG)-based muscle activation, and the physical and physiological responses of the subjects can consequently be evaluated simultaneously. Moreover, a covariance matrix adaption evolution strategy, which is relatively tolerant of both measurement noises and human adaptation, is used to generate the optimal parameters of the RSCs periodically. By optimization of the RSCs persistently, the objective function can be maximized, and the subjects' engagement can be enhanced. Finally, the performance of the proposed method is demonstrated by the validation and comparison experiments. The results show that both subjects' sEMG-based motor engagement and electroencephalography based neural engagement can be improved significantly and maintained at a high level.

For the issue of limited filtering accuracy of interactive multiple model particle filter algorithm caused by the resampling particles don't contain the latest observation information, we made improvements on interactive multiple model particle filter algorithm in this paper based on mixed kalman particle filter algorithm. Interactive multiple model particle filter algorithm is proposed. In addition, the composed methods influence to tracking accuracy are discussed. In the new algorithm the system state estimation is generated with unscented kalman filter (UKF) first and then use the extended kalman filter (EKF) to get the proposal distribution of the particles, taking advantage of the measure information to update the particles' state. We compare and analyze the target tracking performance of the proposed algorithm of IMM-MKPF in this paper, IMM-UPF and IMM-EPF through the simulation experiment. The results show that the tracking accuracy of the proposed algorithm is superior to other two algorithms. Thus, the new method in this paper is effective. The method is of important to improve tracking accuracy further for maneuvering target tracking under the non-linear and non-Gaussian circumstances.