Adaptive Attitude Research Articles

Mamba-VNPS: A Visual Navigation and Positioning System with State-Selection Space

This study was designed to address the challenges of autonomous navigation facing UAVs in urban air mobility environments without GPS. Unlike traditional localization methods that rely heavily on GPS and pre-mapped routes, Mamba-VNPS leverages a self-supervised learning framework and advanced feature extraction techniques to achieve robust real-time localization without external signal dependence. The results show that Mamba-VNPS significantly outperforms traditional methods across multiple aspects, including localization error. These innovations provide a scalable and effective solution for UAV navigation, enhancing operational efficiency in complex spaces. This study highlights the urgent need for adaptive positioning systems in urban air mobility (UAM) and provides a methodology for future research on autonomous navigation technologies in both aerial and ground applications.

Container migration for edge computing in industrial Internet with joint latency reduction and reliability enhancement

Edge computing has emerged as a prominent trend in the field of information technology, offering flexible and robust resources for the industrial Internet. How to migrate container accurately is crucial for edge computing in the industrial Internet, as it plays a vital role in enhancing service response speed and safeguarding uninterrupted continuity of production operations. In this paper, we explore the problem of container migration in edge computing within the industrial Internet, aiming to reduce latency and enhance reliability. We establish a two-objective optimization model to comprehensively capture the container migration problem and formulate it as a constrained optimization model. The formulated model provides a systematic framework that effectively balances the trade-off between reducing latency and enhancing reliability. To tackle the migration strategy derived from the optimization model, we propose a migration algorithm based on the improved binary whale optimization algorithm. Our migration algorithm incorporates the adaptive probability and adaptive position weight within the hunting and searching operations, effectively enhancing the search efficiency during the solving process. The experimental results demonstrate the effectiveness of the established model in reducing the objective value, while the proposed migration algorithm surpasses existing algorithms by achieving an average reduction of at least 15.59% in the objective value.

Adaptive probe position correction for automated complex weld inspection based on structural signal monitoring

ABSTRACT To ensure comprehensive coverage of welds, multi-axis motions are necessary for the probe during the automatic detection of complex welds. Guaranteeing the effectiveness of ultrasonic data during this process is crucial for ensuring detection accuracy and reliability. Taking tubular Y-joints as an example, this paper presents an adaptive probe posture correction method based on structural signal monitoring: (1) The structural and acoustic model of tubular Y-joints is established to select and predict the target structural signal. (2) Feature extraction and signal tracking algorithms are developed to achieve real-time monitoring of signals. (3) A posture correction method is proposed based on signal characteristics: time-domain characteristics correspond to position adjustments, while amplitudes correspond to orientation corrections. Building upon this, a feedback mechanism for probe posture is established. The effectiveness of the developed method is verified through underwater automatic detection experiments of tubular Y-joints. Results demonstrate that, with the assistance of the probe posture correction method, the coverage of the effective area increases to more than 95%, and embedded defects are fully detected through a single scan. In conclusion, the adaptive probe posture correction method based on structural signal monitoring presents a promising approach for the automatic ultrasonic examination of complex welds.

Gorilla algorithm based on double random perturbation and its engineering application

Aiming at the problems of artificial gorilla troop optimization algorithm, such as easy to fall into local optimum, slow convergence speed and low optimization accuracy, an artificial gorilla troop optimization algorithm based on double random perturbation strategy is proposed. Firstly, the Halton sequence is introduced to initialize the population to increase the diversity of the population; secondly, a multi-dimensional random number strategy is used in the algorithm optimization stage and an adaptive position search mechanism is proposed in the exploration stage to improve the convergence speed of the algorithm; thirdly, a double random perturbation strategy is proposed to solve the group effect of gorillas and enhance the ability of the algorithm to jump out of the local optimum; finally, a dimension-by-dimensional update strategy is adopted to update individual positions, which improves the convergence accuracy of the algorithm. Through the comparison of the optimization results of 10 benchmark test functions and the Wilcoxon rank sum test, it can be seen that the improved algorithm has greatly improved the optimization accuracy and convergence speed. In addition, through the experimental comparative analysis of an engineering optimization problem, the superiority of the proposed algorithm in dealing with real engineering problems is further verified.

Adaptive anti-unwinding attitude stabilisation with predefined time for rigid spacecraft

In this paper, the problem of attitude stabilisation without unwinding is investigated for rigid spacecraft. To guarantee predefined-time convergence and unwinding-free performance of the closed-loop system, a predefined-time sliding mode function with adjustable parameters is proposed. This sliding mode function contains two equilibrium points. When the states of the closed-loop system are on the sliding mode surface, they converge to the equilibrium point near the initial states. Based on this sliding mode function, an adaptive attitude stabilising control law with tuning parameters is designed to compensate the external disturbances and inertia uncertainty. The presented control law guarantees that the settling time of the closed-loop system and the bounds of the system states can be explicitly determined in advance by adjusting parameters of the sliding mode function and the control law. Finally, numerical simulations are carried out to demonstrate the effectiveness of the presented control law. The simulation results illustrate that the predefined-time convergence and unwinding-free property of the closed-loop system are guaranteed by adopting the presented control law.

A Model-Free Adaptive Positioning Control Method for Underactuated Unmanned Surface Vessels in Unknown Ocean Currents

Aiming to address the problem of underactuated unmanned surface vehicles (USVs) performing fixed-point operations at sea without dynamic positioning control systems, this paper introduces an original approach to positioning control: the virtual anchor control method. This method is applicable in environments with currents that change slowly and does not require prior knowledge of current information or vessel motion model parameters, thus offering convenient usability. This method comprises four steps. First, a concise linear motion model with unknown disturbances is proposed. Then, a motion planning law is designed by imitating underlying principles of ship anchoring. Next, an adaptive disturbance observer is proposed to estimate uncertainties in the motion model. In the last step, based on the observer, a sliding-mode method is used to design a heading control law, and a thrust control law is also designed by applying the Lyapunov method. Numerical simulation experiments with significant disturbances and tidal current variations are conducted, which demonstrate that the proposed method has a good control effect and is robust.

Adaptive positioning of large-scale aircraft components using a simplified image-based visual servoing via online measurement of ball-head center

In digital alignment systems, ensuring fast and accurate alignment between the ball head on large aircraft components and the ball socket of the positioner is the key to aircraft assembly efficiency and quality. Image-based visual servoing is one of the most advanced techniques with high efficiency and positioning accuracy, but it does not account for measuring the ball-head position. Moreover, variable target positions, similar feature interference, and poor lighting conditions pose great challenges to the fast and robust visual measurement of ball-head position. Hence, an online visual measurement method is introduced to measure the absolute position of the ball head in real time. An adaptive region of interest extraction algorithm and an improved edge following method are introduced to extract the elliptical contour from the ball-head image, and ellipse detection is achieved through a simple triplet selection algorithm. A geometric model based on the perspective projection of the sphere is established for estimating the ball-head center. Then, the simplified image-based visual servoing method is developed by constantizing the image Jacobian matrix. Next, an adaptive positioning method of large-scale aircraft components with multiple ball heads is proposed from a holistic perspective. Finally, experiments show that the final positioning accuracy of each ball head in the X/Y plane is less than ± 0.05 mm, with an efficiency almost ten times that of the conventional method. After adaptive adjustment, the position and angle errors of the simulated component are reduced within 0.6 mm and 0.3°, respectively.

Force Tracking Control With Adaptive Stiffness and Iterative Position of Hip-Assistive Soft Exosuits

Force Tracking Control With Adaptive Stiffness and Iterative Position of Hip-Assistive Soft Exosuits

Fault Diagnosis of an Excitation System Using a Fuzzy Neural Network Optimized by a Novel Adaptive Grey Wolf Optimizer

As the excitation system is the core control component of a synchronous condenser system, its fault diagnosis is crucial for maximizing the reactive power compensation capability of the synchronous condenser. To achieve accurate diagnosis of excitation system faults, this paper proposes a novel adaptive grey wolf optimizer (AGWO) to optimize the initial weights and biases of the fuzzy neural network (FNN), thereby enhancing the diagnostic performance of the FNN model. Firstly, an improved nonlinear convergence factor is introduced to balance the algorithm’s global exploration and local exploitation capabilities. Secondly, a new adaptive position update strategy that enhances the interaction capability of the position information is proposed to improve the algorithm’s ability to jump out of the local optimum and accelerate the convergence speed. In addition, it is demonstrated that the proposed AGWO algorithm has global convergence. By selecting real fault waveforms of the excitation system for case validation, the results show that the proposed AGWO has a better convergence performance compared to the grey wolf optimizer (GWO), particle swarm optimization (PSO), whale optimization algorithm (WOA), and marine predator algorithm (MPA). Specifically, compared to the FNN and GWO-FNN models, the AGWO-FNN model improves average diagnostic accuracy on the test set by 4.2% and 2.5%, respectively. Therefore, the proposed AGWO-FNN effectively enhances the accuracy of fault diagnosis in the excitation system and exhibits stronger diagnostic capability.

Scene Chinese Recognition with Local and Global Attention

Recently, scene Chinese recognition has attracted increasing attention. While mainstream scene text recognition methods exhibit outstanding performance in English recognition, they are considerably limited in Chinese recognition, due to inter-class similarity, intra-class variability, and complex combination of components in scene Chinese text. In this paper, we design Adaptive Position Encoding(APE) to enhance the model’s ability to perceive spatial information. Based on APE, we have innovatively designed Local Attention Module (LAM) and Global Attention Module (GAM). Specifically, LAM captures local features to identify common characteristics among characters of the same category, addressing the issue of intra-class variability. Meanwhile, LAM captures global features to identify the subordination relationships of Chinese character components. By integrating LAM and GAM, combining both local and global features, it is possible to find differences in the details among features that are fundamentally similar, thus solving the problem of inter-class similarity. Further, we contrive the transformer encoder–decoder structure to identify the vast variety of Chinese characters. Based on the Local/Global Attention Module and transformer encoder–decoder framework, we devise the novel sequence-to-sequence Local and Global Attention Network(LGANet), where both the backbone and the encoder/decoder are composed of attention mechanisms. Subsequent experiments on the Chinese scene dataset show that the recognition accuracy of our proposed LGANet is 77.3% and the normalized editing distance is 88.6%, both of which achieve the SOTA results in Fig. 1.

Improved Deadbeat Predictive Current Control of PMSM Based on a Resistance Adaptive Position Observer

Improved Deadbeat Predictive Current Control of PMSM Based on a Resistance Adaptive Position Observer

PROSES KREATIF MUA (MAKE UP ARTIST) PEKALONGAN DALAM MENGHADAPI TANTANGAN DI ERA DIGITAL

ABSTRACTThe arts industry is an industry that requires talent, creativity and innovation. Bridal make-up is an industry that must be equipped with these three elements. This is interesting to research because creativity must compete in the midst of the digital era, where a MUA (Make Up Artist) must be clever at brainstorming by continuing to be creative and innovative and have an adaptive attitude to adapt to challenges in the digital era in order to maintain its existence. This research aims to determine the creative process of a MUA (Make Up Artist) in Pekalongan and the challenges of a Pekalongan MUA (Make Up Artist) in facing challenges in the digital era. This research is a type of qualitative research. The research method used is the descriptive-analysis method. Data collection was carried out through observation, interviews, literature study and documentation study. The analysis used is data analysis starting from data collection, data reduction, data presentation and drawing conclusions. This writing will begin with a description of (1) The creative process of MUA (Make Up Artist) in Pekalongan, (2) Challenges of MUA (Make Up Artist) facing the digital era.

Background adaptive PosMarker based on online generation and detection for locating watermarked regions in photographs

Robust watermarking technology can embed invisible messages in screens to trace the source of unauthorized screen photographs. Locating the four vertices of the embedded region in the photograph is necessary, as existing watermarking methods require geometric correction of the embedded region before revealing the message. Existing localization methods suffer from a performance trade-off: either causing unaesthetic visual quality by embedding visible markers or achieving poor localization precision, leading to message extraction failure. To address this issue, we propose a background adaptive position marker, PosMarker, based on the gray level co-occurrence matrix and the noise visibility function. Besides, we propose an online generation scheme that employs a learnable generator to cooperate with the detector, allowing joint optimization between the two. This simultaneously improves both visual quality and detection precision. Extensive experiments demonstrate the superior localization precision of our PosMarker-based method compared to others.

Adaptive Attitude Roll Control of Guided Projectile Based on a Novel Unidirectional Global Sliding Mode Algorithm

Adaptive Attitude Roll Control of Guided Projectile Based on a Novel Unidirectional Global Sliding Mode Algorithm

Attitudes Toward Gender Equality of Nursing Students in a University of Guayaquil

Gender equality is not only a human right but also a fundamental pillar for building a society with full equity in the social, political, and cultural spheres. The objective of this study was to determine the attitude of UCSG nursing students towards gender equality. The study population consisted of 154 university students who met the inclusion criteria. The study employed a virtual survey and a questionnaire of questions using a Likert scale of attitudes by García Pérez. The participants in the study were university students, with an average age of 23 years. The majority (73.5%) were women, while 26.3% were men. Of the men, 79.2% were single. The questionnaire of questions with the Likert scale of attitudes of García Pérez was applied to determine the students' sensitivity to the topic of gender equality. The results indicated that the women exhibited greater sensitivity than the men. The present study found that there is a global adaptive attitude in the three scales (sociocultural, relational, and personal). However, sexist attitudes are present, particularly in the relational level in relation to gender roles, gender violence, leadership, and affectivity. In contrast, in the sociocultural level, there are adaptive attitudes, while in the personal level, there are egalitarian attitudes.

Adaptive configuration control of combined UAVs based on leader-wingman mode

Modular Unmanned Aerial Vehicles (UAVs) can adapt to rapidly changing payload requirements based on the shape and weight of the load by adding or subtracting units, reconfiguring, or changing the type of units. The existing research has addressed aerial docking and hover control post-docking but fails to achieve coordinated flight following combination, leading to delayed response and oscillations as the number of UAV units increases. Moreover, the configuration of modular UAVs is complex and variable, making it challenging to adjust the controller parameters of each unit online. Therefore, this paper presents: (A) Adaptive attitude allocation method for different combined UAV configurations: establishing a mapping relationship between constant controller parameters of the unit and the combination angular acceleration. The desired torque of the combination is allocated based on the size of the lever arm, enabling adaptive attitude control of the combination for varying configurations by controlling the attitude of the local unit; (B) A power allocation strategy based on a leader-wingman mode: employing a leader to control the entire combination, distributing the combination’s force and torque to wingman units according to the mapping relationship of the attitude allocation method. This transforms the complex control of the combination into unit control in the leader-wingman mode. Compared to current average allocation methods, the step response of attitude angle improves by about 60% on average, and spatial trajectory tracking increases by an average of 11.5%. As the number of units grows, the response of the combination becomes similar to that of a single, independently flying UAV, resolving the oscillation issue in combined flight. Additionally, this approach eliminates the need to change the controller parameters of all units, facilitating convenient reconfiguration and coordinated flight for modular UAVs post-combination.

Event-Triggered Distributed Fixed-Time Adaptive Attitude Control With Prescribed Performance for Multiple QUAVs

Event-Triggered Distributed Fixed-Time Adaptive Attitude Control With Prescribed Performance for Multiple QUAVs

Event-triggered adaptive deep neural network sliding mode control design for unmanned aerial vehicle systems

This paper proposes an event-triggered adaptive attitude maneuver control strategy based on deep neural network (DNN) for quadcopters to improve the attitude tracking performance and attenuate the effects of nonlinear and uncertainty. First, we establish an affine function model constructed to describe the dynamic model of quadcopters. The DNN trained over a large time scale is used to approximate the nonlinearity and uncertainty. Then, an adaptive sliding mode control (SMC) is combined with the DNN to achieve accurate tracking of attitude trajectory. Furthermore, to reduce unnecessary data transmissions, an event-trigger mechanism is combined with the designed DNN-driven adaptive SMC. The Lyapunov-based stability analysis is used to prove the stability of the closed loop system. The simulation results show the good performance of the proposed control strategy.

Dynamic event‐triggered adaptive neural nonsingular fixed‐time attitude control for multi‐UAVs systems

SummaryThis article looks into the dynamic event‐triggered fixed‐time adaptive attitude control problem for nonlinear six‐rotor unmanned aerial vehicle (UAV) with external disturbances. The multiple six‐rotor UAVs considered are regarded as nonlinear multi‐agent systems (MASs), and each subsystem has multiple inputs. Under the framework of backstepping recursive design, an effective adaptive fixed‐time control method is proposed by combining neural networks (NNs) technology and fixed‐time theory. NNs are utilized to handle unknown nonlinearity and unmodeled parts in attitude systems. The hyperbolic tangent function is ushered to address the singularity problem that may occur in the derivative of the controller, thereby averting the phenomenon of chattering. For the sake of alleviating the correspondence burden of multiple UAVs attitude systems, a modified dynamic event‐triggered mechanism (DETM) is ushered. The developed controller swears for that all signals of the six‐rotor UAV attitude systems are bounded and the tracking errors converge to a small neighborhood of the origin within a fixed‐time interval. Eventually, with the help of simulation results, the effectiveness of the proposed control scheme was verified.

MOBILE ROBOT TRACKING SYSTEM BASED ON MACHINE VISION AND LASER RADAR

The proposed solution addresses the issue of insufficient real-time performance and accuracy in mobile robot path tracking by introducing a system that combines machine vision and laser radar. In this study, the Broadcom BCM2711 microcontroller chip is connected to the RS232 communication interface for transmitting information to the ARM embedded processor. Users can access position distance, direction, and other robot-related data through the man-machine interface's LCD display in a Windows operating system environment. By initiating an adaptive position tracking algorithm program identified by the robot within the position tracking unit, mobile position tracking of the robot is achieved. Experimental results demonstrate significant improvements in both real-time performance and accuracy of this mobile robot tracking system.