Game of Drones: Multi-UAV Pursuit-Evasion Game With Online Motion Planning by Deep Reinforcement Learning.

As one of the tiniest flying objects, unmanned aerial vehicles (UAVs) are often expanded as the "swarm" to execute missions. In this article, we investigate the multiquadcopter and target pursuit-evasion game in the obstacles environment. For high-quality simulation of the urban environment, we propose the pursuit-evasion scenario (PES) framework to create the environment with a physics engine, which enables quadcopter agents to take actions and interact with the environment. On this basis, we construct multiagent coronal bidirectionally coordinated with target prediction network (CBC-TP Net) with a vectorized extension of multiagent deep deterministic policy gradient (MADDPG) formulation to ensure the effectiveness of the damaged "swarm" system in pursuit-evasion mission. Unlike traditional reinforcement learning, we design a target prediction network (TP Net) innovatively in the common framework to imitate the way of human thinking: situation prediction is always before decision-making. The experiments of the pursuit-evasion game are conducted to verify the state-of-the-art performance of the proposed strategy, both in the normal and antidamaged situations.

Event-Based Robust Optimal Consensus Control for Nonlinear Multiagent System With Local Adaptive Dynamic Programming.

This article investigates the robust optimal consensus for nonlinear multiagent systems (MASs) through the local adaptive dynamic programming (ADP) approach and the event-triggered control method. Due to the nonlinearities in dynamics, the first part defines a novel measurement error to construct a distributed integral sliding-mode controller, and the consensus errors can approximately converge to the origin in a fixed time. Then, a modified cost function with augmented control is proposed to deal with the unmatched disturbances for the event-based optimal consensus controller. Specifically, a single network local ADP structure with novel concurrent learning is presented to approximate the optimal consensus policies, which guarantees the robustness of the MASs and the uniform ultimate boundedness (UUB) of the neural network (NN) weights' estimation error and relaxes the requirement of initial admissible control. Finally, an illustrative simulation verifies the effectiveness of the method.

Timing theory continuous nursing, resistance training: Rehabilitation and mental health of caregivers and stroke patients with traumatic fractures.

BACKGROUND: Stroke is the leading cause of adult lifelong disability worldwide. A stroke is an acute cerebrovascular disease with a variety of causes and corresponding clinical symptoms. Around 75% of surviving stroke patients experience impaired nerve function, and some suffer from traumatic fractures, which can lead to special care needs. AIM: To determine the effect of timing theory continuous care, with resistance training, on the rehabilitation and mental health of caregivers and stroke patients with traumatic fractures. METHODS: Between January 2017 to March 2021, we selected 100 hospital admissions with post-stroke hemiplegia complicated with a traumatic fracture. Two participant groups were created: (1) Control group: given resistance training; and (2) Observation group: given timing theory continuous care combined with resistance training. The degree of satisfaction and differences in bone and phosphorus metabolism indexes between the two groups were compared. The self-perceived burden scale (SPBS) and caregiver burden questionnaire were used to evaluate the psychological health of patients and caregivers. The Harris hip function score, ability of daily living (ADL) scale, and global quality of life questionnaire (GQOL-74) were used to evaluate hip function, ability of daily living, and quality of life. RESULTS: Data were collected prior to and after intervention. Alkaline phosphatase (ALP), osteocalcin, and vitamin D3 in the observation group and control group increased after intervention (P < 0.05), and carboxy-terminal peptide of type I collagen ß Special sequence (ß-CTX) decreased (P < 0.05). ALP and osteocalcin in the observation group were higher than in the control group (P < 0.05). There was no significant difference in ß-CTX and vitamin D3 between the two groups (P > 0.05). The SPBS score of the observation group was lower and the ADL score was higher than the control group. The burden score was lower and the Harris hip function and GQOL-74 scores were higher than that of the control group (P < 0.05). The observation group's satisfaction rating was 94.00%, which was higher than the rating from the control group (P < 0.05). CONCLUSION: Timing theory continuous nursing with resistance training can reduce hip dysfunction in stroke patients with a traumatic fracture and enhance quality of life and mental health of patients and caregivers.

Finite-Time Dynamic Allocation and Control in Multiagent Coordination for Target Tracking.

A new finite-time dynamic allocation and control scheme is developed in this article for multiple agents tracking a moving target. Based on a competitive manner, the dynamic allocation is achieved by k-winners-take-all (k-WTA), which can be realized by a novel finite-time dual neural network with the adaptive-gain activation function. Then, a finite-time disturbance compensation-based control law is proposed for agents to conduct capturing task or return to the specified point in vigilance. The finite-time stability of the system is guaranteed through Lyapunov analysis. Finally, the efficiency of the proposed scheme is illustrated by simulations in which the situation with higher target velocity than trackers is considered.

Distributed Pursuit of an Evader With Collision and Obstacle Avoidance.

The distributed, real-time algorithms for multiple pursuers cooperating to capture an evader are developed in an obstacle-free and an obstacle-cluttered environment, respectively. The developed algorithm is based on the idea of planning the control action within its safe, collision-free region for each robot. We initially present a greedy capturing strategy for an obstacle-free environment based on the Buffered Voronoi Cell (BVC). For an environment with obstacles, the obstacle-aware BVC (OABVC) is defined as the safe region, which considers the physical radius of each robot, and dynamically weights the Voronoi boundary between robot and obstacle to make it tangent to the obstacle. Each robot continually computes its safe cells and plans its control actions in a recursion fashion. In both cases, the pursuers successfully capture the evader with only relative positions of neighboring robots. A rigorous proof is provided to ensure the collision and obstacle avoidance during the pursuit-evasion games. Simulation results are presented to demonstrate the efficiency of the developed algorithms.

Integrated Fault Estimation and Fault-Tolerant Tracking Control for Lipschitz Nonlinear Multiagent Systems.

An integrated fault estimation (FE) and fault-tolerant tracking control (FTTC) strategy is developed for Lipschitz nonlinear multiagent systems subject to actuator faults, external disturbance, and uncertainties. First, for each agent, a corresponding unknown input observer with reduced/full order is constructed to obtain the FE. Then, a state/output feedback FTTC strategy is proposed based on the integral sliding-mode technique and adaptive super-twisting algorithm. The observer and controller gains are obtained simultaneously via H∞ optimization with a linear matrix inequality formulation. Finally, the comparison simulations are provided to demonstrate the effectiveness of the proposed algorithm.

A novel hybrid deep learning scheme for four-class motor imagery classification.

OBJECTIVE: Learning the structures and unknown correlations of a motor imagery electroencephalogram (MI-EEG) signal is important for its classification. It is also a major challenge to obtain good classification accuracy from the increased number of classes and increased variability from different people. In this study, a four-class MI task is investigated. APPROACH: An end-to-end novel hybrid deep learning scheme is developed to decode the MI task from EEG data. The proposed algorithm consists of two parts: a. A one-versus-rest filter bank common spatial pattern is adopted to preprocess and pre-extract the features of the four-class MI signal. b. A hybrid deep network based on the convolutional neural network and long-term short-term memory network is proposed to extract and learn the spatial and temporal features of the MI signal simultaneously. MAIN RESULTS: The main contribution of this paper is to propose a hybrid deep network framework to improve the classification accuracy of the four-class MI-EEG signal. The hybrid deep network is a subject-independent shared neural network, which means it can be trained by using the training data from all subjects to form one model. SIGNIFICANCE: The classification performance obtained by the proposed algorithm on brain-computer interface (BCI) competition IV dataset 2a in terms of accuracy is 83% and Cohen's kappa value is 0.80. Finally, the shared hybrid deep network is evaluated by every subject respectively, and the experimental results illustrate that the shared neural network has satisfactory accuracy. Thus, the proposed algorithm could be of great interest for real-life BCIs.

Tracking control of an underactuated ship by modified dynamic inversion.

The tracking control problem of an underactuated ship is investigated. We intend to use the underactuated ship as an example to extend the traditional dynamic inversion control method to underactuated systems. The difficulty lies in the fact that the system has no relative degree, which prevents the application of standard dynamic inversion. Three modified dynamic inversion methods are proposed that are applicable to this system. The first is the well-known dynamic extension-based dynamic inversion (DEDI), which treats an input as a state and takes dynamic extension to achieve a relative degree. The second is virtual input-based dynamic inversion (VIDI), which treats a state as a virtual input to achieve a relative degree. The third is output redefinition-based dynamic inversion (ORDI), which selects a particular variable as a new output to achieve a relative degree. The three methods are generalizations of dynamic inversion control and remove some of its inherent limitations, making it applicable to a wide variety of underactuated systems. The effectiveness of the proposed methods is verified by numerical simulations.

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters.

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations.

Control-oriented modeling and adaptive backstepping control for a nonminimum phase hypersonic vehicle.

In this paper, the nonminimum phase problem of a flexible hypersonic vehicle is investigated. The main challenge of nonminimum phase is the prevention of dynamic inversion methods to nonlinear control design. To solve this problem, we make research on the relationship between nonminimum phase and backstepping control, finding that a stable nonlinear controller can be obtained by changing the control loop on the basis of backstepping control. By extending the control loop to cover the internal dynamics in it, the internal states are directly controlled by the inputs and simultaneously serve as virtual control for the external states, making it possible to guarantee output tracking as well as internal stability. Then, based on the extended control loop, a simplified control-oriented model is developed to enable the applicability of adaptive backstepping method. It simplifies the design process and releases some limitations caused by direct use of the no simplified control-oriented model. Next, under proper assumptions, asymptotic stability is proved for constant commands, while bounded stability is proved for varying commands. The proposed method is compared with approximate backstepping control and dynamic surface control and is shown to have superior tracking accuracy as well as robustness from the simulation results. This paper may also provide a beneficial guidance for control design of other complex systems.

Decentralized finite-time attitude synchronization for multiple rigid spacecraft via a novel disturbance observer.

This paper investigates decentralized finite-time attitude synchronization for a group of rigid spacecraft by using quaternion with the consideration of environmental disturbances, inertia uncertainties and actuator saturation. Nonsingular terminal sliding mode (TSM) is used for controller design. Firstly, a theorem is proven that there always exists a kind of TSM that converges faster than fast terminal sliding mode (FTSM) for quaternion-descripted attitude control system. Controller with this kind of TSM has faster convergence and reduced computation than FTSM controller. Then, combining with an adaptive parameter estimation strategy, a novel terminal sliding mode disturbance observer is proposed. The proposed disturbance observer needs no upper bound information of the lumped uncertainties or their derivatives. On the basis of undirected topology and the disturbance observer, decentralized attitude synchronization control laws are designed and all attitude errors are ensured to converge to small regions in finite time. As for actuator saturation problem, an auxiliary variable is introduced and accommodated by the disturbance observer. Finally, simulation results are given and the effectiveness of the proposed control scheme is testified.

Continuous high order sliding mode controller design for a flexible air-breathing hypersonic vehicle.

This paper investigates the problem of tracking control with uncertainties for a flexible air-breathing hypersonic vehicle (FAHV). In order to overcome the analytical intractability of this model, an Input-Output linearization model is constructed for the purpose of feedback control design. Then, the continuous finite time convergence high order sliding mode controller is designed for the Input-Output linearization model without uncertainties. In addition, a nonlinear disturbance observer is applied to estimate the uncertainties in order to compensate the controller and disturbance suppression, where disturbance observer and controller synthesis design is obtained. Finally, the synthesis of controller and disturbance observer is used to achieve the tracking for the velocity and altitude of the FAHV and simulations are presented to illustrate the effectiveness of the control strategies.

One-pot synthesis of metal-organic framework@SiO2 core-shell nanoparticles with enhanced visible-light photoactivity.

This paper presents a novel strategy to prepare Cu3(BTC)2@SiO2 core-shell nanoparticles in the size range of 200-400 nm using a new one-pot strategy under ultrasonic irradiation at room temperature. In this approach, the silica shell thickness could be finely tuned in the size range of 12-60 nm for various reaction times. Nanocomposite thin films were fabricated on the glass substrates by Sol-Gel spin coating using the products for 1.5 h, 2 h and 2.5 h, respectively, and heat treated using an infrared lamp heating system in air. The photocatalytic degradation of phenol in aqueous solution using Cu2(BTC)3@SiO2 thin films was investigated under visible light irradiation at pH 4. After a 45 min reaction with phenol, the degradation rate was up to 93.1%. Moreover, the thin film photocatalysts could be reused 5 times without appreciable loss of photocatalytic activity for degradation of phenol. The present work clearly shows that the films as photocatalysts showed higher photocatalytic performance.

Facile synthesis of nanocrystals of a microporous metal-organic framework by an ultrasonic method and selective sensing of organoamines.

In this work we present the rapid synthesis of nanocrystals of a fluorescent microporous metal-organic framework using an ultrasonic method, and their utility for selective sensing of organoamines.

Simultaneous determination of dissolved anthracene and pyrene in aqueous solution by synchronous fluorimetry.

A synchronous fluorimetry for simultaneous determination of dissolved anthracene and pyrene in aqueous solution has been established. The linear ranges for determination of dissolved anthracene and pyrene were 1.00x10(-8) to 4.50x10(-7)molL(-1) and 5.00x10(-9) to 6.50x10(-7)molL(-1), and the limits of detection (LOD) for anthracene and pyrene were 2.23x10(-9) and 8.24x10(-10)molL(-1) with relative standard deviations (R.S.D.) of 2.90 and 2.34% (n=5), respectively. Satisfactory results were obtained when the established method was used to simultaneously determine anthracene and pyrene in spiked water samples.