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In this article, we consider the formation tracking problem of nonholonomic multiagent systems only using relative bearing measurements between the agents. Such a practical and important yet challenging issue has been taken into limited consideration by existing approaches, which usually requires additional measurements such as relative positions. The contributions of this article are two-fold. First, a fully distributed reference velocity estimator is proposed. Under the proposed adaptive estimator, each agent can estimate the time-varying reference velocity asymptotically. Second, an input-to-state stable controller is designed according to the bearing rigid theory. Under the proposed controller, the formation with bearing-only constraints can be achieved. Finally, the proposed scheme is demonstrated and its effectiveness is verified by presenting some simulation and experimental tests.

This paper puts forward an unprecedented avoidance-striking-arrival problem aiming to address the need for tank's uncertain mechanical systems on the intelligent battlefield. The associated system uncertainties (possibly rapid) are time-varying but bounded (possibly unknown). The goal is to design a controller that enables the tank to aim at and attack the enemy tank while keeping itself (out of the enemy fire zone). The tank maintains this condition until reaching the predefined region. In this paper, an approximate constraint following control method is adopted to solve this problem, and the original constraints are creatively divided into two categories: the avoidance - tracking constraint and the striking - arrival constraint. An adaptive robust control method is proposed and consequently verified through simulation experiments. It is proved that the system fully obeys the avoidance - tracking - constraint and strictly obeys the striking - arrival constraint under the control input τ . Besides, the control of the tank vehicle running system and tank gun bidirectional stabilization system are unified to deal with the control signal delay caused by complex uncertainties on the battlefield. Overall, this paper reduced the delay of signal transmission in the system while solved the avoidance - striking - arrival problem.

The existing studies for tracking consensus of multiagent systems (MASs) are all restricted to networks with only cooperative relationships among agents. Tracking consensus, however, requires beyond these traditional models due to the ubiquitous competition in many real-world MASs, such as biological systems and social systems. Taking into account this fact, this article aims to extend the dynamics of tracking consensus to signed networks containing both cooperative and competitive relationships among agents. A group of agents with general linear dynamics is considered. The cases of the fixed network as well as switching networks are analyzed, respectively. In the end, some algebraic conditions related to the network structure and the positive/negative edge weight are established to ensure the implementation of tracking consensus. Moreover, the single decoupling system is allowed to be strictly unstable in theory, and the upper bound of the eigenvalue modulus of the system matrix related to the system instability is given.

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.

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.

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.

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 an adaptive fractional-order nonsingular terminal sliding mode (AFNTSMC) control scheme combined with the independent joint control approach for trajectory tracking of three-axis gimbal platforms (GPs) mounted on a moving vehicle subjected to external disturbances. To achieve accurate images taken by the camera mounted on the GP, the motions and vibrations of the vehicle must be isolated from the camera. Thus, precise mathematical modeling of a three-axis GP with considering the external disturbances is studied, such that the GP tracks the target accurately and holds the line of sight stationary. Various tests with different vehicle conditions are performed to collect the movement data to be considered as the desired input for the GP. Thanks to the utilization of AFNTSMC, fast convergence together with simultaneous accurate trajectory tracking and strong robustness can be ensured. Corresponding comparative simulation results validate the effectiveness of the theoretical design results and superiorities of the proposed method over the existing methods.

This paper considers the problem of tracking the players in handball videos using a single video source. Tracking of handball players in the video is a difficult task as they can frequently leave and re-enter the camera field of view, often change directions quickly and occlude each other. Players wear similar team uniforms, play indoor under artificial illumination, with the background than can vary significantly as the handball court is often painted in various colors. The continually improving accuracy of CNN-based object detectors makes tracking-by-detection methods an attractive choice for tracking players in sports videos as they can perform online and with low computational requirements on top of object detection. Here we consider the use of three tracking-by-detection methods in conjunction with the YOLO object detector, namely the standard Hungarian assignment algorithm, the Simple Online, and Real-time Tracking (SORT) algorithm that adds a motion model, and its extension Deep SORT. The methods are tested on a custom dataset of handball video scenes.

To date, the majority of positioning systems have been designed to operate within environments that have a long-term stable macro-structure with potential small-scale dynamics. These assumptions allow the existing positioning systems to produce and utilize stable maps. However, in highly dynamic industrial settings these assumptions are no longer valid and the task of tracking people is more challenging due to the rapid large-scale changes in structure. In this paper, we propose a novel positioning system for tracking people in highly dynamic industrial environments, such as construction sites. The proposed system leverages the existing CCTV camera infrastructure found in many industrial settings along with radio and inertial sensors within each worker’s mobile phone to accurately track multiple people. This multi-target multi-sensor tracking framework also allows our system to use cross-modality training in order to deal with the environment dynamics. In particular, we show how our system uses cross-modality training in order to automatically keep track environmental changes (i.e., new walls) by utilizing occlusion maps. In addition, we show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. We have conducted extensive real-world experiments in a construction site showing significant accuracy improvement via cross-modality training and the use of social forces.

In this paper, a fixed-time controller under the mechanism of event-trigger is designed for a class of nonlinear pure-feedback systems with non-differentiable non-affine functions. By properly modeling non-affine terms, the limitation of the partial derivatives of non-affine functions is eliminated. In our design process, we first develop a fixed-time adaptive controller using decoupling method. Then, a relative threshold event-trigger mechanism (ETM) is introduced in Section 3.1. The proposed controller can not only stabilize the system within a fixed-time, but also save communication resources more effectively. Lastly, the feasibility of the proposed control scheme is verified by two simulation examples.

UAV swarms have triggered wide concern due to their potential application values in recent years. While there are studies proposed in terms of the architecture design for UAV swarms, two main challenges still exist: (1) Scalability, supporting a large scale of vehicles; (2) Versatility, integrating diversified missions. To this end, a multi-layered and distributed architecture for mission oriented miniature fixed-wing UAV swarms is presented in this paper. The proposed architecture is built on the concept of modularity. It divides the overall system to five layers: low-level control, high-level control, coordination, communication and human interaction layers, and many modules that can be viewed as black boxes with interfaces of inputs and outputs. In this way, not only the complexity of developing a large system can be reduced, but also the versatility of supporting diversified missions can be ensured. Furthermore, the proposed architecture is fully distributed that each UAV performs the decision-making procedure autonomously so as to achieve better scalability. Moreover, different kinds of aerial platforms can be feasibly extended by using the control allocation matrices and the integrated hardware box. A prototype swarm system based on the proposed architecture is built and the proposed architecture is evaluated through field experiments with a scale of 21 fixed-wing UAVs. Particularly, to the best of our knowledge, this paper is the first work which successfully demonstrates formation flight, target recognition and tracking missions within an integrated architecture for fixed-wing UAV swarms through field experiments.

For scenarios to pass through narrow and irregular channels, we develop a pragmatic distributed flexible formation protocol for multiple unmanned surface vessel systems (Multi-USVs). Therein, for path tracking with two leaders USVs, we propose a model predictive trajectory tracking scheme. Specifically, we design a scanning-formation controller with a trajectory state estimator for the followers to follow the leaders in order to efficiently fulfill the passing-through mission. Asymptotic stability conditions are derived to guarantee the feasibility of the developed multi-USV controller. Finally, both numerical simulations and experiments are conducted to show the effectiveness of the proposed multi-USV control strategies.

This paper proposes an adaptive trajectory tracking control scheme for low-speed car-like vehicles with less efforts in tuning of the control gains. An interesting way of integrating adaptive control gains with consideration of steering saturation by using the backstepping technique is designed to enhance trajectory tracking while ensuring the commanded inputs within the input boundaries. The design of such adaptive control gains is also based on enhancing the convergence rate of tracking errors, especially for lateral deviation from the reference trajectory. It is further theoretically proven that, even under the influence of steering saturation, the proposed controller can make the closed-loop system approximately globally asymptotically stable at zero errors. Comparative MATLAB/ Simulink simulations and experimental tests based on Quanser latest self-driving car (QCar) have been conducted to verify the effectiveness of the proposed control scheme in accurate tracking without violating the input constraints.

Due to the flammability, explosibility and toxicity of continuous objects (e.g. chemical gas, oil spill, radioactive waste) in petrochemical and nuclear industry, boundary tracking of continuous objects is becoming a critical issue for industrial wireless sensor networks (IWSNs) to achieve disaster reduction. In this article, we fully exploit the collective intelligence and machine learning capability of senor nodes, and propose a continuous objects boundary tracking algorithm for IWSNs. The proposed algorithm first determines an upper bound of the event region covered by continuous objects. A full binary tree-based partition is performed within the event region for coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is further transformed into a binary classification problem. A hierarchical soft margin support vector machines training strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm achieves high tracking accuracy with less boundary nodes. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to the false sensor readings reported by low proportion of faulty nodes, which is expected in harsh industrial environments.

In this paper, we propose RF-Dial to realize a light-weight, battery-free and functional 2D human-computer interaction solution via RFID. What RF-Dial shines is that it can easily turn an ordinary object, e.g., a board eraser, into an intelligent interaction device. By deploying a tag array on the side face of the object together with a dipole tag on the top face, RF-Dial can track the rigid motion of the object and detect the touch gesture of a user on the surface of the object, including translation, rotation, click, press and hold, and swipe. To do the motion tracking, RF-Dial builds a phase-based model that captures the translation and rotation of the tagged object simultaneously. To detect the touch gesture, RF-Dial builds an RSSI-based model that uses the impact of the touching finger on the tag antenna's impedance to estimate the touch position. We implemented RF-Dial and evaluated its performance. Extensive experiments show that RF-Dial achieves an accurate rigid motion tracking, with a small error of 0.6cm for translation tracking, and a small error of 1.9 degrees for rotation estimation. Besides, RF-Dial can also detect the touch gesture accurately, as the 90 percent of touch position errors are less than 2.09mm.

This work considers the observability problem in bearings-only tracking (BOT). Although the rank criterion has always been used for observability analysis, the evident drawback that it can only give a qualitative result prevents us from exploiting more information from the observability matrix. In reality, we want to know not only if the system is observable but also the degree of the observability. In order to quantitatively describe the system observability, we propose a series of metrics for BOT. Being derived based on the condition number, the novel metrics can reflect the observability degree as well as evaluate the tracking performance. Besides, the metrics can be extended to be used for sensor trajectory optimization and sensor configuration, which are crucial to enhancing the tracking performance.

This paper focuses on the problem of adaptive fuzzy consensus tracking control with event-triggered input for a class of fractional-order multi-agent systems (FOMASs), where systems are considered to be with unmeasurable states, mismatched uncertain external disturbances and unknown nonlinearities. An event-triggered mechanism with a decreasing threshold function is proposed in this paper, which mitigates communication burden and avoids large control impulse. To achieve better tracking performance in distributed control strategy, a compensating fractional-order adaptation law associated with consensus tracking error is introduced when agents can not obtain the information of reference trajectory directly. Under the fractional-order stability theory, a novel distributed adaptive event-triggered controller with fuzzy observers is constructed, which ensures the boundedness of tracking errors. A simulation example demonstrates the correctness of the presented scheme.

Coordinated taxiing planning for multiple aircraft on flight deck is of vital importance which can dramatically improve the dispatching efficiency. In this paper, first, the coordinated taxiing path planning problem is transformed into a centralized optimal control problem where collision-free conditions and mechanical limits are considered. Since the formulated optimal control problem is of large state space and highly nonlinear, an efficient hierarchical initialization technique based on the Dubins-curve method is proposed. Then, a model predictive controller is designed to track the obtained reference trajectory in the presence of initial state error and external disturbances. Numerical experiments demonstrate that the proposed “offline planning + online tracking” framework can achieve efficient and robust coordinated taxiing planning and tracking even in the presence of initial state error and continuous external disturbances.

This article intends to address an adaptive asymptotic tracking control with prescribed performance function for a class of nonaffine systems with unknown disturbances. First, the nonaffine system is transformed into an affine system by using a set of alternative state variables. Subsequently, a prescribed performance function with predefined convergence rate, maximum overshoot and steady-state error is introduced. To achieve the asymptotic tracking control performance, the robust integral of the sign of the error (RISE) feedback term is utilized in the control design to reject the unknown external disturbances and NN approximation errors. Finally, an adaptive controller is presented so that the asymptotic tracking performance with guaranteed prescribed performance is achieved. Comparative experiments are provided to show the effectiveness of the proposed control scheme.

This paper is devoted to the fuzzy fault-tolerant tracking control of Markov jump systems with unknown mismatched faults. To reconstruct the faults and the system states, a sequence of proportional-integral observers are established via the system outputs. With the help of a structure separation technique, the proportional-integral gains and the observer gains are solved by a unified linear matrix inequality framework. Resorting to the rebuilt faults and states from an iterative estimation algorithm, a backstepping based fuzzy fault-tolerant tracking control scheme against the mismatched faults is established to make the resultant closed-loop system be uniform ultimate boundedness. Simulations are provided to verify the effectiveness of the proposed methods.

This paper studies the iterative learning control (ILC) algorithm for first-order hyperbolic systems. Unlike most of the ILC literature of distributed parameter systems, in the iteration domain, that require identical desired trajectories. Here the desired trajectories are iteratively varying and described by a high-order internal model (HOIM). The HOIM-based P-type ILC design is firstly introduced in this paper to the first-order hyperbolic systems, which enable the systems to achieve the perfect tracking for the iteration-varying desired trajectories on L 2 space. Meanwhile, the convergence theorem of the proposed algorithm is established for first-order time-delay hyperbolic systems. Finally, simulation results testify the validity of the algorithm.

Optimal performance is extremely important for hypersonic flight control. Different from the most existing methodologies which only consider basic control performance including stability, robustness and transient performance, this article deals with the design of nearly optimal tracking controllers for hypersonic flight vehicles (HFVs). Firstly, main controllers are developed for the velocity subsystem and the altitude subsystem of HFVs via concise fuzzy approximations. Then, optimal controllers are nearly implemented utilizing single-network adaptive critic design. Moreover, the stability of closed-loop systems and the convergence of optimal controllers are theoretically proved. Finally, compared simulation results are given to verify the superiority. The special contribution is the application of low-complex control structure owing to the critic-only network and advanced learning laws developed for fuzzy approximations, which is expected to guarantee satisfied real-time performance.

Infrared (IR) small target detection in complex backgrounds is one of the key technologies in IR search and tracking applications. Although significant progress has been made over the past few decades, how to separate a small target from complex backgrounds remains a challenging task. In this letter, a novel small target detection method via multidirectional derivative-based weighted contrast measure (MDWCM) is proposed. Initially, multidirectional derivative subbands are quickly obtained by the facet model. Then, an effective division scheme of surrounding area is performed to capture the derivative properties of the target. A new local contrast measure is constructed to simultaneously enhance the target and suppress the background clutter. Third, the MDWCM maps constructed from all derivative subbands are integrated to enhance the robustness of detection. Finally, the small target is extracted by an adaptive segmentation method. The experimental results demonstrate that the proposed algorithm performs favorably compared to other state-of-the-art methods.

This article copes with vibration suppression and angular position tracking problems of a robotic manipulator system comprised of a rotating hub and a length-varying manipulator. To obtain precise dynamic response, the manipulator system is modeled in infinite-dimension with partial differential equations. S-curve acceleration/deceleration (S-CA/D) scheme is employed for speed regulation of the length-varying manipulator. Two novel observers are developed to estimate both the unknown disturbances and their time-derivatives, and two auxiliary systems are put forward to tackle input constraints. With assistance of the auxiliary systems and observers, two boundary control laws are put forward to manage vibration suppression and angular position tracking of the proposed manipulator system. Through Lyapunov's theory, the closed-loop system is proved to be bounded. Numerical simulations have displayed the effectiveness of the observers and boundary control laws.

High energy efficiency and tracking accuracy are both vital for hydraulic lifting servo systems. However, the appropriate hardware configuration and its corresponding nonlinear controller is always a problem to be solved. To address this multi-objective task, an integrated energy-saving and position tracking controller is developed. Specifically, to reduce the substantial energy dissipation, a system configuration referring to the structure of the three-position-six-way valve is proposed. To ensure accurate tracking performance, a hybrid observer-based output feedback controller is developed. By doing so, the function of flow regeneration and the property of flow matching are achieved, and the tracking accuracy is guaranteed by using only position signal. To validate the effectiveness of the proposed method, a common Lyapunov function is used to prove the stability of the multi-model system, and case studies are conducted to demonstrate the system performance.

This article considers the event-triggered control for the active seat suspension system with time-varying full-state constraints. Consider that communication resources may be limited, this article proposes a dynamic relative threshold strategy to reduce the communication burden of actuator and controller. Compared with the fixed value as a trigger condition, the dynamically changing thresholds as trigger conditions are more general and universal. The time-varying full-state constraint problem is solved by using the barrier Lyapunov function. In addition, the radial basis function neural networks are employed to approximate the unknown terms. Then, all signals in the resulted system are bounded, and the Zeno behavior can be avoided successfully. Moreover, all the system states satisfy their corresponding constraint condition. Finally, the feasibility and rationality of this method are proved by the simulation analysis of a real example of a seat suspension system.

Through-wall moving target tracking is an essential part of applications with the purpose of sensing moving human in the sealed building. However, the major challenge stems from the multipath effect in the actual environment, which creates errors in the range delay of moving target. In this letter, an effective through-wall moving target tracking algorithm in multipath is proposed. This method based on the correlation of human motion can restore and estimate the range delay of the moving target in the strong multipath interference. Both simulation and experiment results indicate that the proposed method can not only suppress multipath but also smooth the trajectory.

Fast and robust small infrared (IR) target detection is a challenging task and critical to the performance of IR searching and tracking (IRST) systems. However, the current algorithms generally have difficulty in striking a good balance between speed and performance. In this letter, we propose a new approach to small IR target detection that can significantly accelerate the detection process by first performing a fast adaptive masking and scaling algorithm. We then propose to enhance the target characteristics and suppress the background clutter using both contrast and gradient information. Finally, we propose to accurately extract the targets via iterative segmentation. The experimental results demonstrated that our proposed method yields the best and the most robust performance, with a speed of at least two times faster than the state-of-the-art methods.

Particle tracking techniques such as magnetic particle tracking, radioactive particle tracking and positron emission particle tracking are widely used in academia and industry to image the dynamics of particulate and multiphase systems. These techniques can provide detailed data concerning a range of important, whole-field quantities based only on the time-averaged dynamics of a small number of tracer particles. However, in order for this data to be reliable, the duration over which these time-averages are taken must be suitably long. Further, the ‘minimum averaging time’ required to produce good statistics depends sensitively on the system in question and, at present, cannot be determined a priori in advance of an experiment. In this paper, we take a step toward resolving this issue, using discrete element method simulations of a simple vibrofluidised granular bed to develop a series of scaling laws relating said minimum averaging time to a variety of key system variables. The scaling laws developed may be used by future experimentalists to predict the required averaging time for each given system during an experimental campaign, thus improving both the efficiency with which particle tracking techniques may be applied, and the reliability of the data produced thereby.

The great popularity of smartphones, together with the increasingly important aim of providing context-aware services, has spurred interest in developing indoor tracking systems. Accurate tracking and localization systems are seen as key services for most context-aware applications. Research projects making use of radio signals detected by radio interfaces and the data captured by sensors commonly integrated in most smartphones have already shown promising and better results than location solutions based on a single data source. In this paper, we present a multi-sensor tracking system built by incrementally integrating state-of-the-art models of the Wi-Fi interface and the accelerometer, gyroscope and magnetometer sensors of a smartphone. Our proposal consists of a simple calibration phase of the tracking system, which involves enabling simultaneous data gathering from all three sensors and the Wi-Fi interface. Taking the Wi-Fi signal model as baseline, four different configurations are evaluated by incrementally adding and integrating the models of the other three sensors. The experimental results reveal a mean error accuracy of 60 cm in the case when the tracking system makes use of all four data sources. Our results also include a spatial characterization of the accuracy and processing power requirements of the proposed solution. Our main findings demonstrate the feasibility of developing accurate localization indoor tracking systems using current smartphones without the need for additional hardware.

Unmanned Aerial Vehicle/Unmanned Ground Vehicle heterogeneous collaborative system can complete complex tasks. In order to monitor and control the two agents simultaneously, a comprehensive ground station is designed. First, the preliminary work is introduced. Then, the architecture of the ground station of the system is proposed. In data communication module, since the amount of data from the system is large, the data of structure and multi-threaded processing are designed in detail. In data display module, the ground station provides various ways for displaying data. In image display module, in order to balance the accuracy and real-time performance of the system, an Efficient Convolution Operators tracking algorithm is improved for tracking moving target. In digital map module, a three-dimensional path planning method based on improved Ant Colony is proposed. The experiment results show that the ground station works well in real-time performance and accuracy.

In this study, the performance of sequence tracking methods for multiple interaction events in specific CdZnTe Compton imagers was evaluated using Monte Carlo simulations. The Compton imager consisted of a 6 × 6 array of virtual Frisch-grid CZT crystals, where the dimensions of each crystal were 5 × 5 × 12 mm3. The sequence tracking methods for another Compton imager that consists of two identical CZT crystals arrays were also evaluated. When 662 keV radiation was incident on the detectors, the percentages of the correct sequences determined by the simple comparison and deterministic methods for two sequential interactions were identical (∼80 %), while those evaluated using the minimum squared difference method (55–59 %) and Three Compton method (45–55 %) for three sequential interactions, differed from each other. The reconstructed images of a 662 keV point source detected using single and double arrays were evaluated based on their angular resolution and signal-to-noise ratio, and the results showed that the double arrays outperformed single arrays.

This article investigates adaptive control problems for unknown second-order nonlinear multiagent systems (MASs) via an event-triggered approach. An adaptive event-triggered consensus controller is given to second-order MAS with unknown nonlinear dynamics. We prove that the proposed consensus controller is free from Zeno behavior. Next, an adaptive event-triggered tracking controller is developed for leader-follower MAS with the leader having bounded nonzero control input. Both consensus and tracking controllers are fully distributed, which means that event-triggered controllers only use local cooperative information. Finally, an unknown second-order nonlinear MAS is used to verify the given event-triggered controllers.

We present mean-nonovershooting tracking control designs for stochastic strict-feedback nonlinear systems. Compared with the existing stochastic nonlinear tracking designs, the advantage of our design is that arbitrary reference trajectories can be tracked “from below” for arbitrary initial conditions (as long as the initial value of the plant output is strictly below the initial value of the reference trajectory). We first consider a special class of stochastic nonlinear systems where the diffusion terms satisfy the linear growth condition and the drift terms are in special triangular forms. A new controller, with gains dependent on initial conditions, is designed to guarantee that the mean of the system output can asymptotically track a given trajectory without overshooting while keeping all of the states mean-square bounded. Then we design a new controller for general stochastic strict-feedback nonlinear systems to ensure that the mean of overshoot can be tuned arbitrarily small while all the states of the closed-loop system remain bounded in probability. Finally, two simulation examples are given to illustrate the stochastic nonlinear nonovershooting trac

Data acquisition using security cameras for pedestrian tracking and counting is inherently inaccurate due to limited video quality and the dynamics of pedestrian mobility. In this article we explore how specific patterns of pedestrian behavior in transport terminals can improve accuracy in tracking and counting. We show the effectiveness of our proposed techniques using data from the New York Grand Central Station. The proposed tool improves the accuracy of pedestrian labeling based on information about the dynamics of pedestrian mobility in the specific context of transport terminals.

This article focuses on the issue of scaled tracking consensus for discrete-time second-order multiagent systems under random packet dropouts, where the cases with a static leader and a dynamic leader are considered, respectively. The scaled tracking consensus means that all agents reach a consensus value determined by the leader but with different scales, and the phenomenon of packet dropout on each communication link is described as a Bernoulli variable independent of other communication links. By virtue of random environment-based scaled consensus algorithms, it is shown how to reconstruct the original system into augmented error systems with random coefficient matrices. With the kind assistance of substochastic matrix and super-stochastic matrix, sufficient conditions for the cases with a static leader and a dynamic leader are derived, respectively. Moreover, computer simulations are performed to demonstrate the dynamics of network agents under random packet dropouts.

High dynamic conditions impose critical challenges on Global Navigation Satellite System (GNSS) receivers, leading to large tracking errors or even loss of tracking. Current methods that intend to improve receivers’ adaptability for high dynamics require either complicated structures or prior statistical information of noises. This paper proposes a high dynamics algorithm based on steepest ascent method that can circumvent the deficiencies of existing methods. First, the relationship between the error of carrier tracking and the maximum of Fast Fourier Transform (FFT) outputs is established, and a performance function based on the steepest ascent method is designed. It can keep stable in high dynamics. Second, a new carrier-tracking loop is constructed by deploying the performance function. When the variation of GPS receiver acceleration ranges from 10 g to 100 g, compared with the PLL that either loses lock or keeps tracking accuracy less than 33.89 Hz, the experimental results show that the proposed method can not only keep tracking, but also achieve tracking accuracy more than 2.77 Hz.

We consider output tracking for a class of viscous nonlinear fluid flows including the incompressible 2D Navier–Stokes equations. The fluid is subject to in-domain inputs and disturbances. We construct an error feedback controller which guarantees approximate local velocity output tracking for a class of reference outputs. The control solution covers point velocity observations and assumes a smooth enough state space. Efficacy of the control solution is illustrated through a numerical example. publishedVersion

Approximate Differentiators have been extensively used in the estimation of joint velocity measurements of industrial manipulators. In this letter, we exploit key differences between the continuous-time model of the first-order Approximate Differentiator, commonly known as the Dirty-derivatives Filter (DF), and its discrete-time model. We show that the discrete-time filter shares the characteristics of an exponentially weighted moving average; in particular, the filter smooths the derivative of its input. We numerically and experimentally demonstrate how the filter estimation performance follows a convex trend in function of the filter bandwidth. We further demonstrate how the bandwidth at which the filter achieves “optimal” performance varies with the frequency of the filter’s input. Inspired by the latter, we propose an Approximate Differentiator with Varying Bandwidth (ADVB) where the filter bandwidth varies based on the magnitude of the position tracking error. We illustrate the superiority of the proposed ADVB over various differentiation schemes both numerically and experimentally.

Tracking the movement of droplets in digital microfluidics is essential to improve its control stability and obtain dynamic information for its applications such as point-of-care testing, environment monitoring and chemical synthesis. Herein, an intelligent, accurate and fast droplet tracking method based on machine vision is developed for applications of digital microfluidics. To continuously recognize the transparent droplets in real-time and avoid the interferes from background patterns or inhomogeneous illumination, we introduced the correlation filter tracker, enabling online learning of the multi-features of the droplets in Fourier domain. Results show the proposed droplet tracking method could accurately locate the droplets. We also demonstrated the capacity of the proposed method for estimation of the droplet velocity as faster as 20 mm/s, and its application in online monitoring the Griess reaction for both colorimetric assay of nitrite and study of reaction kinetics.

The present study presents a new method for optimal identification of the output voltage of a Proton Exchange Membrane Fuel Cell based on an optimized Deep Belief Network. According to the designed method, the Deep Belief Network structure is optimized based on a modified version of the Water Strider Algorithm. The main idea is to minimize the error value between the observed and the obtained output voltages of the Proton Exchange Membrane Fuel Cell. Simulations specified that the suggested Deep Belief Network-Modified Water Strider Algorithm method provides accurate tracking for the voltage signal prediction of the Proton Exchange Membrane Fuel Cell. Final results are also compared with Deep Belief Network-Water Strider Algorithm, and DBN/TLBO-DE from the literature to show the proposed method dominance.

In this paper, we aim to solve the optimal tracking control problem for a class of nonaffine discrete-time systems with actuator saturation. First, a data-based neural identifier is constructed to learn the unknown system dynamics. Then, according to the expression of the trained neural identifier, we can obtain the steady control corresponding to the reference trajectory. Next, by involving the iterative dual heuristic dynamic programming algorithm, the new costate function and the tracking control law are developed. Two other neural networks are used to estimate the costate function and approximate the tracking control law. Considering approximation errors of neural networks, the stability analysis of the proposed algorithm for the specific systems is provided by introducing the Lyapunov approach. Finally, via conducting simulation and comparison, the superiority of the developed optimal tracking method is confirmed. Moreover, the trajectory tracking performance of the wastewater treatment application is also involved for further verifying the proposed approach.

In order to solve the problem of inaccurate positioning in the process of target tracking due to illumination, scale change and occlusion, a correlation filtering tracking algorithm based on generate compression network is proposed. Firstly, the encoder is used to compress the high-dimensional depth features extracted from VGG16 network, and the soft quantizer reduces floating-point operation so that improves the operation speed. Secondly, the discriminator of generative adversarial network guides the encoder and generator to better compress and recover the original depth features by generating a discriminator against the network, so as to enhance the ability of the encoder to extract the key features of the target, then, the compressed depth features, gray features and hog features are combined to improve the ability of target representation. Thirdly, the correlation filter and PCA feature dimensionality reduction are used to complete the target precise location and discriminant scale estimation. The experimental results show that the tracking accuracy of the proposed algorithm is 87.1%, and the tracking rate is up to 70fps.

This article proposes two kinds of terminal sliding mode control (TSMC) strategies for implementing the finite-time attitude tracking of spacecraft under environmental disturbances and model uncertainties. First, the integral disturbance observer (IDO) is designed to estimate the disturbances and uncertainties. Second, the IDO-based continuous TSMC (CTSMC) method is developed to achieve active disturbance rejection and better tracking performance. Third, to further mitigate the chattering, a modified TSMC (MTSMC) method is constructed by employing an adaptive method and incorporating a piecewise smooth function. Finally, simulations are performed to show the feasibility of the proposed TSMC laws.

Accurate set-point tracking across a broad operating region with minimum transients is a crucial requirement during the start-up and shutdown phases of a process plant operation. The emotional learning based controller, with its model-independent and nonlinear features, is extremely suitable for plant-based operations. However, the design of most of the existing emotional-learning strategies is based on narrow operating regions, and, hence, they may not yield satisfactory tracking performance when operated on a broader region. In this correspondence, a new design approach is adopted, which takes into consideration the specific requirements of multiple set-point tracking. This approach fuses a regression model with a constrained optimization problem to generate stimuli weights as well as the learning and inhibition parameters. Experimental validation of the proposed method on a quadruple tank system and comparison of its performance with an optimized second-order sliding mode controller illustrates its effectiveness.

International audience

Magnetic tracking systems have been widely investigated in biomedical engineering due to the transparency of the human body to static magnetic fields. We recently proposed a novel human-machine interface for prosthetic application, namely the myokinetic interface. This controls multi-articulated prostheses by tracking magnets implanted in the residual muscles of individuals with amputation. Previous studies in this area focused solely on the choice and tuning of the localization algorithm. Here, we addressed the role of the intrinsic properties of the sensors, by analysing their effects on the tracking accuracy and on the computation time of the localization algorithm, through experimentally-verified computer simulations. We observed that the tracking accuracy is primarily affected by the localization rate, which is directly related to the sampling frequency of the sensors, and less significantly affected by the sensor resolution. The computation time, instead, proved positively correlated to the number of MMs, and negatively correlated with the localization rate. Our results may contribute to the development of novel human-machine interfaces for prosthetic limbs and could be extended to a broad range of applications involving magnetic tracking.

This article discusses the tracking control problem of full-state constrained nonlinear systems with unknown time-varying powers. With the help of a nonlinear state-dependent transformation, by introducing lower and higher powers in control design and skillfully combining the adding a power integrator method with the dynamic gain method, a full-state constrained tracking control design is developed without frequently used feasibility conditions. It is rigorously proved that all the signals of the closed-loop system are bounded, full-state constraints are not violated, and the tracking error converges to a compact set around the origin in a finite-time.