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The purpose of this paper is to construct an event-triggered finite-time fault-tolerant formation tracking controller, which can achieve a time-varying formation control for multiple unmanned aerial vehicles (UAVs) during actuator failures and external perturbations.

First, this study developed the formation tracking protocol for each follower using UAV formation members, defining the tracking inaccuracy of the UAV followers’ location. Subsequently, this study designed the multilayer event-triggered controller based on the backstepping method framework within finite time. Then, considering the actuator failures, and added self-adaptive thought for fault-tolerant control within finite time, the event-triggered closed-loop system is subsequently shown to be a finite-time stable system. Furthermore, the Zeno behavior is analyzed to prevent infinite triggering instances within a finite time. Finally, simulations are conducted with external disturbances and actuator failure conditions to demonstrate formation tracking controller performance.

It achieves improved performance in the presence of external disturbances and system failures. Combining limited-time adaptive control and event triggering improves system stability, increase robustness to disturbances and calculation efficiency. In addition, the designed formation tracking controller can effectively control the time-varying formation of the leader and followers to complete the task, and by adding a fixed-time observer, it can effectively compensate for external disturbances and improve formation control accuracy.

A formation-following controller is designed, which can handle both external disturbances and internal actuator failures during formation flight, and the proposed method can be applied to a variety of formation control scenarios and does not rely on a specific type of UAV or communication network.

This paper aims to study a neural network-based fault-tolerant controller to improve the tracking control performance of an unmanned autonomous helicopter with system uncertainty, external disturbances and sensor faults, using the prescribed performance method.

To ensure that the tracking error satisfies the prescribed performance, the authors adopt an error transformation function method. A control scheme based on the neural network and high-order disturbance observer is designed to guarantee the boundedness of the closed-loop system. A simulation is performed to prove the validity of the control scheme.

The developed adaptive fault-tolerant control method makes the system with sensor fault realize tracking control. The error transformation function method can effectively handle the prescribed performance requirements. Sensor fault can be regarded as a type of system uncertainty. The uncertainty can be approximated accurately using neural networks. A high-order disturbance observer can effectively suppress compound disturbances.

The tracking performance requirements of unmanned autonomous helicopter system are considered in the design of sensor fault-tolerant control. The inequality constraint that the output tracking error must satisfy is transformed into an unconstrained problem by introducing an error transformation function. The fault state of the velocity sensor is considered as the system uncertainty, and a neural network is used to approach the total uncertainty. Neural network estimation errors and external disturbances are treated as compound disturbances, and a high-order disturbance observer is constructed to compensate for them.

This paper aims to present a novel adaptive sliding mode control (ASMC) method based on the predefined performance barrier function for reusable launch vehicle under attitude constraints and mismatched disturbances.

A novel ASMC based on barrier function is adopted to deal with matched and mismatched disturbances. The upper bounds of the disturbances are not required to be known in advance. Meanwhile, a predefined performance function (PPF) with prescribed convergence time is used to adjust the boundary of the barrier function. The transient performance, including the overshoot, convergence rate and settling time, as well as the steady-state performance of the attitude tracking error are retained in the predetermined region under the barrier function and PPF. The stability of the proposed control method is analyzed via Lyapunov method.

In contrast to conventional adaptive back-stepping methods, the proposed method is comparatively simple and effective which does not need to disassemble the control system into multiple first-order systems. The proposed barrier function based on PPF can adjust not only the switching gain in an adaptive way but also the convergence time and steady-state error. And the efficiency of the proposed method is illustrated by conducting numerical simulations.

A novel barrier function based ASMC method is proposed to fit in the amplitude of the mismatched and matched disturbances. The transient and steady-state performance of attitude tracking error can be selected as prior control parameters.

This paper aims to propose and verify an improved cascade active disturbance rejection control (ADRC) scheme based on output redefinition for hypersonic vehicles (HSVs) with nonminimum phase characteristic and model uncertainties.

To handle the nonminimum phase characteristic, a tuning factor stabilizing internal dynamics is introduced to redefine the system output states; its effective range is determined by analyzing Byrnes–Isidori normalized form of the redefined system. The extended state observers (ESOs) are used to estimate the uncertainties, which include matched and mismatched items in the system. The controller compensates observations in real time and appends integral terms to improve robustness against the estimation errors of ESOs.

Theoretical and simulation results show that the stability of internal dynamics is guaranteed by the tuning factor and the tracking errors of external commands are globally asymptotically stable.

The control scheme in this paper is expected to generate a reliable way for dealing with nonminimum phase characteristic and model uncertainties of HSVs.

In the framework of ADRC, a concise form of redefined outputs is proposed, in which the tuning factor performs a decisive role in stabilizing the internal dynamics of HSVs. By introducing an integral term into the cascade ADRC scheme, the compensation accuracy of matched and mismatched disturbances is improved.

Weak repeatability is observed in handcrafted keypoints, leading to tracking failures in visual simultaneous localization and mapping (SLAM) systems under challenging scenarios such as illumination change, rapid rotation and large angle of view variation. In contrast, learning-based keypoints exhibit higher repetition but entail considerable computational costs. This paper proposes an innovative algorithm for keypoint extraction, aiming to strike an equilibrium between precision and efficiency. This paper aims to attain accurate, robust and versatile visual localization in scenes of formidable complexity.

SiLK-SLAM initially refines the cutting-edge learning-based extractor, SiLK, and introduces an innovative postprocessing algorithm for keypoint homogenization and operational efficiency. Furthermore, SiLK-SLAM devises a reliable relocalization strategy called PCPnP, leveraging progressive and consistent sampling, thereby bolstering its robustness.

Empirical evaluations conducted on TUM, KITTI and EuRoC data sets substantiate SiLK-SLAM’s superior localization accuracy compared to ORB-SLAM3 and other methods. Compared to ORB-SLAM3, SiLK-SLAM demonstrates an enhancement in localization accuracy even by 70.99%, 87.20% and 85.27% across the three data sets. The relocalization experiments demonstrate SiLK-SLAM’s capability in producing precise and repeatable keypoints, showcasing its robustness in challenging environments.

The SiLK-SLAM achieves exceedingly elevated localization accuracy and resilience in formidable scenarios, holding paramount importance in enhancing the autonomy of robots navigating intricate environments. Code is available at https://github.com/Pepper-FlavoredChewingGum/SiLK-SLAM.

Seafood consumption in Sweden is below the national recommendations and limited to very few species. This study aims to explore the factors shaping seafood choices at the point of purchase among a sample of current consumers in Sweden, and examines their attitudes regarding seafood consumption more broadly.

Convenience sampling was used to recruit consumers planning to purchase seafood at a supermarket in Sweden. Participants’ shopping trip was recorded using wearable eye tracking glasses and, upon completion, semi-structured interviews were conducted using a cued retrospective think aloud method. This exploratory study integrates qualitative data (N = 39) with eye tracking data (N = 34), to explore how seafood choices unfold when consumers purchase at the point of purchase.

Purchases were mostly restricted to familiar seafood species. Four interlinked main themes were identified from thematic analysis of the interview data: Ambivalence, Nice and Necessary, Proficiency with Seafood and External Influences. Sustainability information (e.g. certifications) faced strong competition from other visual elements at the point of purchase, receiving less attention than product imagery and pricing information.

This study is the first to explore the factors shaping seafood choices of current consumers at the point of purchase. The unique approach, combining explicit and implicit measures, enriches understanding of the factors influencing seafood choices and how these may interrelate. The results are valuable for the industry and contribute to the literature by identifying possible routes to improve seafood sustainability communication.

The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the dual-arm robot is directly in contact with external environment, controlling the mutual force between robot and external environment is of great importance. Besides, a high compliance of the robot should be guaranteed.

An impedance control based on Particle Swarm Optimization (PSO) algorithm is designed to track the mutual force and achieve compliance control of the robot end.

The experimental results show that the impedance control coefficients can be automatically tuned converged by PSO algorithm.

The system can reach a steady state within 0.03 s with overshoot convergence, and the force fluctuation range at the steady state decreases to about ±0.08 N even under the force mutation condition.

This study sought to understand which food allergen labeling systems (non-directive, semi-directive, and directive) were attended to and preferred by 34 participants with food hypersensitivity and their perceived corporate social responsibility (CSR) and behavioral intention towards a restaurant that identifies food allergens on menus.

This study used an online survey with open-ended and ranking questions, combined with eye-tracking technology, to explore participants' visual attention and design preferences regarding four menus. This study utilized one-way repeated measures analysis of variance (RM-ANOVA) and heat maps to analyze participants' menu-reading behaviors. A content analysis of survey responses and a ranking analysis of menus were conducted to understand the reasons behind consumers' preferred menu designs.

The advisory statement was not much attended to. Participants identified food allergen information significantly quicker with the directive labeling system (icons) than the other two systems, implying they were eye-catching. Semi-directive labeling system (red text) has lower visit count and was more preferred than two other systems; each labeling system has its strengths and limitations. Participants viewed restaurants that disclosed food allergen information on menus as socially responsible, and they would revisit those restaurants in the future.

This study was one of the first to explore, through use of eye-tracking technology, which food allergen labeling systems were attended to by consumers with food hypersensitivity. The use of triangulation methods strengthened the credibility of the results. The study provided empirical data to restauranteurs in the US on the values of food allergen identification on restaurant menus, although it is voluntary.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

The purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.

The main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.

Longitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.

This paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.