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This paper proposes an online predictive control method for fixed-wing unmanned aerial vehicles (UAVs) with a pan-tilt camera in target tracking. It aims to achieve long-term tracking while concurrently maintaining the target near the image center. Particularly, this work takes the UAV and pan-tilt camera as an overall system and deals with the target tracking problem via joint optimization, so that the tracking ability of the UAV can be improved. The image captured by the pan-tilt camera is the unique input associated with the target, and model predictive control (MPC) is used to solve the optimization problem with constraints that cannot be performed by the classic image-based visual servoing (IBVS). In addition to the dynamic constraint of the UAV, the perception constraint of the camera is also taken into consideration, which is described by the maximum distance between the target and the camera. The accurate detection of the target depends on the amount of its feature information contained in the image, which is highly related to the relative distance between the target and the camera. Moreover, considering the real-time requirements of practical applications, an MPC strategy based on soft constraints and a warm start is presented. Furthermore, a switching-based approach is proposed to return the target back to the perception range quickly once it exceeds the range, and the exponential asymptotic stability of the switched controller is proven as well. Both numerical and hardware-in-the-loop (HITL) simulations are conducted to verify the effectiveness and superiority of the proposed method compared with the existing method.

Fault detection and localization are vital for ensuring the stability of data center networks (DCNs). Specifically, adaptive fault diagnosis is deemed a fundamental technology in achieving the fault tolerance of systems. The highly scalable data center network (HSDC) is a promising structure of server-centric DCNs, as it exhibits the capacity for incremental scalability, coupled with the assurance of low cost and energy consumption, low diameter, and high bisection width. In this paper, we first determine that both the connectivity and diagnosability of the m-dimensional complete HSDC, denoted by HSDCm(m), are m. Further, we propose an efficient adaptive fault diagnosis algorithm to diagnose an HSDCm(m) within three test rounds, and at most N+4m(m−2) tests with m≥3 (resp. at most nine tests with m=2), where N=m·2m is the total number of nodes in HSDCm(m). Our experimental outcomes demonstrate that this diagnosis scheme of HSDC can achieve complete diagnosis and significantly reduce the number of required tests.

As a result of increasing urbanization, traffic monitoring in cities has become a challenging task. The use of Unmanned Aerial Vehicles (UAVs) provides an attractive solution to this problem. Multi-Object Tracking (MOT) for UAVs is a key technology to fulfill this task. Traditional detection-based-tracking (DBT) methods begin by employing an object detector to retrieve targets in each image and then track them based on a matching algorithm. Recently, the popular multi-task learning methods have been dominating this area, since they can detect targets and extract Re-Identification (Re-ID) features in a computationally efficient way. However, the detection task and the tracking task have conflicting requirements on image features, leading to the poor performance of the joint learning model compared to separate detection and tracking methods. The problem is more severe when it comes to UAV images due to the presence of irregular motion of a large number of small targets. In this paper, we propose using a balanced Joint Detection and Re-ID learning (JDR) network to address the MOT problem in UAV vision. To better handle the non-uniform motion of objects in UAV videos, the Set-Membership Filter is applied, which describes object state as a bounded set. An appearance-matching cascade is then proposed based on the target state set. Furthermore, a Motion-Mutation module is designed to address the challenges posed by the abrupt motion of UAV. Extensive experiments on the VisDrone2019-MOT dataset certify that our proposed model, referred to as SMFMOT, outperforms the state-of-the-art models by a wide margin and achieves superior performance in the MOT tasks in UAV videos.

The multi-UAV task assignment problem in large-scale group-to-group interception scenarios presents challenges in terms of large computational complexity and the lack of accurate evaluation models. This paper proposes an effective evaluation model and hierarchical task assignment framework to address these challenges. The evaluation model incorporates the dynamics constraints specific to fixed-wing UAVs and improves the Apollonius circle model to accurately describe the cooperative interception effectiveness of multiple UAVs. By evaluating the interception effectiveness during the interception process, the assignment scheme of the multiple UAVs could be given based on the model. To optimize the configuration of UAVs and targets, a hierarchical framework based on the network flow algorithm is employed. This framework utilizes a clustering method based on feature similarity and interception advantage to decompose the large-scale task assignment problem into smaller, complete submodels. Following the assignment, Dubins curves are planned to the optimal interception points, ensuring the effectiveness of the interception task. Simulation results demonstrate the feasibility and effectiveness of the proposed scheme. With the increase in the model scale, the proposed scheme has a greater descending rate of runtime. In a large-scale scenario involving 200 UAVs and 100 targets, the runtime is reduced by 84.86%.

Clustering is an effective solution to improve the management efficiency of large-scale systems. One one hand, UAVs performing the same task have a similar moving tendency. One the other hand, the network topology of the UAV swarms is dynamically changing. Considering the above two aspects, our main contributrons are designing a group-oriented distributed clustering algorithm based on the coalition game that couples task attributes and communication attributes. The clustering goal is to divide UAVs performing the same task into one cluster and make each cluster have more UAVs under the cluster size limits, thus improving communication efficiency. The proposed algorithm comprehensively considers the task group information and communication link stability as the coalition value. UAVs decide whether to leave their current coalition based on coalition values. Through periodic parallel switch operations selection, UAVs are divided into the desired clustering structure. Simulations verify that our clustering algorithm is effective and better than the existing ones, especially in communication link stability, cluster numbers, and load balance.

This paper proposes an approximate optimal curve-path-tracking control algorithm for partially unknown nonlinear systems subject to asymmetric control input constraints. Firstly, the problem is simplified by introducing a feedforward control law, and a dedicated design for optimal control with asymmetric input constraints is provided by redesigning the control cost function in a non-quadratic form. Then, the optimality and stability of the derived optimal control policy is demonstrated. To solve the underlying tracking Hamilton–Jacobi–Bellman (HJB) equation in consideration of partially unknown systems, an integral reinforcement learning (IRL) algorithm is utilized using the neural network (NN)-based value function approximation. Finally, the effectiveness and generalization of the proposed method is verified by experiments carried out on a high-fidelity hardware-in-the-loop (HIL) simulation system for fixed-wing unmanned aerial vehicles (UAVs) in comparison with three other typical path-tracking control algorithms.

Taking advantage of faster speed, less resource consumption and better biological interpretability of spiking neural networks, this paper developed a novel spiking neural network reinforcement learning method using actor-critic architecture and temporal coding. The simple improved leaky integrate-and-fire (LIF) model was used to describe the behavior of a spike neuron. Then the actor-critic network structure and the update formulas using temporally encoded information were provided. The current model was finally examined in the decision-making task, the gridworld task, the UAV flying through a window task and the avoiding a flying basketball task. In the 5 × 5 grid map, the value function learned was close to the ideal situation and the quickest way from one state to another was found. A UAV trained by this method was able to fly through the window quickly in simulation. An actual flight test of a UAV avoiding a flying basketball was conducted. With this model, the success rate of the test was 96% and the average decision time was 41.3 ms. The results show the effectiveness and accuracy of the temporal coded spiking neural network RL method. In conclusion, an attempt was made to provide insights into developing spiking neural network reinforcement learning methods for decision-making and autonomous control of unmanned systems.

This paper proposes a new algorithm to solve the control problem for a special class of distance-based directed formations, namely directed-triangulated Laman graphs. The central idea of the algorithm is to construct a virtual point for the agents who have more than two neighbors by employing the information of the desired formation. Compared with the existing methods, the proposed algorithm can make the distance error between the agents converge faster and the path consumption is less. Furthermore, the proposed algorithm is modified to be operable for the small fixed-wing UAV model with nonholonomic and input constraints. Finally, the effectiveness of the proposed method is verified by a series of simulation experiments.

Due to the nonlinear and asymmetric input constraints of the fixed-wing UAVs, it is a challenging task to design controllers for the fixed-wing UAV formation control. Distance-based formation control does not require global positions as well as the alignment of coordinates, which brings in great convenience for designing a distributed control law. Motivated by the facts mentioned above, in this paper, the problem of distance-based formation of fixed-wing UAVs with input constraints is studied. A low-gain formation controller, which is a generalized gradient controller of the potential function, is proposed. The desired formation can be achieved by the designed controller under the input constraints of the fixed-wing UAVs with proven stability. Finally, the effectiveness of the proposed method is verified by the numerical simulation and the semi-physical simulation.

With the fast development of industry and nuclear energy, large amounts of different radionuclides are inevitably released into the environment. The efficient solidification or elimination of radionuclides is thereby crucial to environmental pollution and human health because of the radioactive hazardous of long-lived radionuclides. The properties of negatively or positively charged radionuclides are quite different, which informs the difficulty of simultaneous elimination of the radionuclides. Herein, we summarized recent works about the selective sorption or catalytic reduction of target radionuclides using different kinds of nanomaterials, such as carbon-based nanomaterials, metal–organic frameworks, and covalent organic frameworks, and their interaction mechanisms are discussed in detail on the basis of batch sorption results, spectroscopy analysis and computational calculations. The sorption-photocatalytic/electrocatalytic reduction of radionuclides from high valent to low valent is an efficient strategy for in situ solidification/immobilization of radionuclides. The special functional groups for the high complexation of target radionuclides and the controlled structures of nanomaterials can selectively bind radionuclides from complicated systems. The challenges and future perspective are finally described, summarized, and discussed.

The selective elimination of long-lived radioactive actinides from complicated solutions is crucial for pollution management of the environment. Knowledge about the species, structures and interaction mechanism of actinides at solid–water interfaces is helpful to understand and to evaluate physicochemical behavior in the natural environment. In this review, we summarize recent works about the sorption and interaction mechanism of actinides (using U, Np, Pu, Cm and Am as representative actinides) on natural clay minerals and man-made nanomaterials. The species and microstructures of actinides on solid particles were investigated by advanced spectroscopy techniques and computational theoretical calculations. The reduction and solidification of actinides on solid particles is the most effective way to immobilize actinides in the natural environment. The contents of this review may be helpful in evaluating the migration of actinides in near-field nuclear waste repositories and the mobilization properties of radionuclides in the environment.

In this paper, we consider a control problem where a group of fixed-wing unmanned aerial vehicles (UAVs) with uncertain dynamics tracks the target vehicle cooperatively in the case of external disturbance. Based on the Gaussian process regression, a data-driven model is established, whose uniform error is bounded with probability. Then a learning-based consensus protocol for multi-UAVs is designed. The stability of the system is proven via Lyapunov function, and the tracking error is guaranteed to be bounded with a high probability. Finally, the effectiveness of the proposed method is shown in the numerical simulation.

The tilt tri-rotor unmanned aerial vehicle (UAV) has three flight modes: the hover mode, the transition mode, and the fixed-wing mode. Controller design in the hover mode is the premise of realizing stable flight of this kind of UAV. Due to the particular structure with odd rotors and strong nonlinearity, the modeling and control of the tilt tri-rotor UAV remain an active and ongoing research topic. To overcome these problems and achieve stable flight control, this paper proposes a sliding mode-based nonlinear control scheme for the hovering flight of a tilt tri-rotor UAV, consisting of position control, attitude control, and control allocation. First, the mathematical model of the UAV is given by using the Newton–Euler formulation. Second, a cascade flight controller consisting of the position controller and the attitude controller is developed based on sliding mode control (SMC). For the position controller, an auxiliary dynamic system composed of the hyperbolic tangent functions is introduced to the SMC approach for constraining the output magnitude of the thrust and the reference attitudes. Besides, a disturbance observer is applied to the attitude controller to alleviate the chattering and improve robustness. Furthermore, according to the structural characteristics of the tilt tri-rotor UAV, a control allocation algorithm is developed to map the virtual control quantities calculated by the cascade flight controller to the actual actuators. Simulations are conducted to verify the robustness against the external disturbances and parameter variations, and the performance comparisons with two other control schemes are also given. Finally, the experiment is also carried out to validate the performance of the proposed control scheme.

In order to explore the effect of –OH functional groups in Cs+ adsorption, we herein used the low temperature plasma-induced grafting method to graft chitosan onto carbon nanotubes (denoted as CTS-g-CNTs), as raw-CNTs have few functional groups and chitosan has a large number of –OH functional groups. The synthesized CTS-g-CNT composites were characterized using different techniques. The effect of –OH functional groups in the Cs+ adsorption process was evaluated by comparison of the adsorption properties of raw-CNTs with and without grafting chitosan. The variation of environmental conditions such as pH and contact time was investigated. A comparison of contaminated seawater and simulated groundwater was also evaluated. The results indicated that: (1) the adsorption of Cs+ ions was strongly dependent on pH and the competitive cations; (2) for CNT-based material, the –OH functional groups have a positive effect on Cs+ removal; (3) simulated contaminated groundwater can be used to model contaminated seawater to evaluate the adsorption property of CNTs-based material. These results showed direct observational evidence on the effect of –OH functional groups for Cs+ adsorption. Our findings are important in providing future directions to design and to choose effective material to remedy the removal of radioactive cesium from contaminated groundwater and seawater, crucial for public health and the human social environment.

It is highly desirable to design optical devices with diverse optomechanical functions. Here, we investigate lateral optical force exerted on subwavelength-diameter (SD) optical fibers harnessed by input light modes with different polarizations. It is interesting to find that input light modes of circular or elliptical polarizations would bring about lateral optical force in new directions, which has not been observed in previous studies. By means of finite-difference time-domain (FDTD) simulations, detailed spatial distributions of the asymmetric transverse force density are revealed, meanwhile dependence of optical force on input light polarizations, fiber diameters, and inclination angles of fiber endfaces are all carefully discussed. It is believed that polarization-sensitive reflection, refraction, and diffraction of optical fields occur at the interface, i.e., fiber oblique endfaces, resulting in asymmetrically distributed optical fields and thereafter non-zero transverse optical force. We believe our new findings could be helpful for constructing future steerable optomechanical devices with more flexibility.

This paper focuses on one of the collision avoidance scenarios for unmanned aerial vehicles (UAVs), where the UAV needs to avoid collision with the enemy UAV during its flying path to the goal point. Such a type of problem is defined as the enemy avoidance problem in this paper. To deal with this problem, a learning based framework is proposed. Under this framework, the enemy avoidance problem is formulated as a Markov Decision Process (MDP), and the maneuver policies for the UAV are learned based on a temporal-difference reinforcement learning method called Sarsa. To handle the enemy avoidance problem in continuous state space, the Cerebellar Model Arithmetic Computer (CMAC) function approximation technique is embodied in the proposed framework. Furthermore, a hardware-in-the-loop (HITL) simulation environment is established. Simulation results show that the UAV agent can learn a satisfying policy under the proposed framework. Comparing with the random policy and the fixed-rule policy, the learned policy can achieve a far higher possibility in reaching the goal point without colliding with the enemy UAV.

Transfer Learning (TL) has received a great deal of attention because of its ability to speed up Reinforcement Learning (RL) by reusing learned knowledge from other tasks. This paper proposes a new transfer learning framework, referred to as Transfer Learning via Artificial Neural Network Approximator (TL-ANNA). It builds an Artificial Neural Network (ANN) transfer approximator to transfer the related knowledge from the source task into the target task and reuses the transferred knowledge with a Probabilistic Policy Reuse (PPR) scheme. Specifically, the transfer approximator maps the state of the target task symmetrically to states of the source task with a certain mapping rule, and activates the related knowledge (components of the action-value function) of the source task as the input of the ANNs; it then predicts the quality of the actions in the target task with the ANNs. The target learner uses the PPR scheme to bias the RL with the suggested action from the transfer approximator. In this way, the transfer approximator builds a symmetric knowledge path between the target task and the source task. In addition, two mapping rules for the transfer approximator are designed, namely, Full Mapping Rule and Group Mapping Rule. Experiments performed on the RoboCup soccer Keepaway task verified that the proposed transfer learning methods outperform two other transfer learning methods in both jumpstart and time to threshold metrics and are more robust to the quality of source knowledge. In addition, the TL-ANNA with the group mapping rule exhibits slightly worse performance than the one with the full mapping rule, but with less computation and space cost when appropriate grouping method is used.

Lanthanides/actinides sorption speciation on minerals and oxides by means of time resolved laser fluorescence spectroscopy (TRLFS), extended X-ray absorption fine structure spectroscopy (EXAFS) and density functional theory (DFT) is reviewed in the field of nuclear disposal safety research. The theoretical aspects of the methods are concisely presented. Examples of recent research results of lanthanide/actinide speciation and local atomic structures using TRLFS, EXAFS and DFT are discussed. The interaction of lanthanides/actinides with oxides and minerals as well as their uptake are also of common interest in radionuclide chemistry. Especially the sorption and inclusion of radionuclides into several minerals lead to an improvement in knowledge of minor components in solids. In the solid-liquid interface, the speciation and local atomic structures of Eu(III), Cm(III), U(VI), and Np(IV/VI) in several natural and synthetic minerals and oxides are also reviewed and discussed. The review is important to understand the physicochemical behavior of lanthanides/actinides at a molecular level in the natural environment.