Simulation Platform Research Articles

Waste-Heat Renewable Gasifier Design through Taguchi's Method and MANFIS

Ensuring a global average temperature increase of no more than 1.5°C is crucial for aligning with the objectives of the Paris Agreement. The Intergovernmental Panel on Climate Change (IPCC) estimates that a 45% reduction in carbon dioxide emissions by 2030 or a 25% reduction by the same year is necessary to cap the temperature rise at 2°C. Failure to achieve this target by 2030 would necessitate substantial subsequent reductions to compensate for the delayed progress toward net zero emissions, potentially incurring higher costs. Elevating the focus on enhancing energy efficiency has emerged as a key strategy in expediting progress toward these climate goals. Hydrogen, renowned for its high efficiency and emission-free combustion, is anticipated to play a pivotal role in this endeavor. Traditionally, gasifiers have served as the conduit for transforming inexpensive fuels like coal or biomass into hydrogen. However, their energy-intensive nature poses a challenge.Addressing this inherent issue, an innovative approach known as the external-heating gasifier has been proposed and designed. The design of the external-heating gasifier represents a quintessential nonlinear and multiple-input multiple-output (MIMO) problem. To achieve optimal performance, the integration of Taguchi's method and inverse-model technology has been employed, with a simulation platform developed using the COMSOL Multiphysics software.Initially, Taguchi's method was utilized to determine the minimum number of experiments required for a full-factorial design and identify the most critical factors influencing product performance. Subsequently, an adaptive neuro-fuzzy inference system (ANFIS) was developed with a MIMO–ANFIS architecture. This system was employed to train an inverse model, facilitating the mapping of relationships between each input and output set. In essence, this approach enables the identification of manufacturing parameters that result in optimal performance. To illustrate the efficacy of the proposed method, a simulation study on an existing gasifier is presented.

Numerical and experimental study of homogenization mechanism of high shear rotor‐stator mixer

AbstractUtilizing computational fluid dynamics (CFD) for analytical purposes, this study developed a fundamental model employing the multiple reference frame (MRF) method, facilitated by the CFX simulation platform. The investigation conducted numerical simulations of the flow field within the rotor‐stator assembly of a high shear mixer, guided by the Navier–Stokes equations and the standard k‐ε turbulence model. To quantify the homogenization efficacy of the high shear mixer under scenarios with and without energy consumption considerations, the study introduced two distinct parameters: the mixing index (γ) and the energy ratio mixing index (λ). The impact of structural parameters such as the number of rotor‐stator teeth, radial clearance, and tooth apex‐base axial clearance on the local flow characteristics—velocity, pressure, turbulent kinetic energy, shear rate distribution, net power consumption, and the specified indices—was meticulously analyzed. This analysis aimed to identify the optimal structural configurations for the mixer, considering energy efficiency and mixing effectiveness, and to determine the relative influence of these structural variations on the mixing index (γ) and the energy ratio mixing index (λ).

Real-time Assessment of Ride-through Capability of Grid-tied Converter in Renewable Power Applications

This paper presents a comprehensive strategy for controlling grid-tied converters, with the primary goal of seamlessly integrating renewable power sources into the grid while ensuring stability and reliability. The control strategy is designed to achieve unity power factor operation under normal grid conditions, while also adhering to ride-through requirements to withstand voltage dips. Using the Typhoon HIL604 real-time simulation platform, extensive testing of the grid-tied converter is conducted to validate its performance. The results of the performance assessment are thoroughly analyzed and presented to exemplify the efficacy of the proposed control strategy. Notably, the paper emphasizes the converter's operation at unity power factor during normal grid conditions, indicating maximized active power delivery to the grid. Furthermore, the grid-tied converter demonstrates impressive ride-through capability during symmetrical voltage dip, achieved through the dynamic adjustment of grid current references to maintain grid stability. This research contributes to the advancement of grid-tied converter control strategies, offering insights into enhancing the integration of renewable power sources into the power grid while upholding system reliability and stability. Overall, this research offers valuable insights into the practical implementation of grid-tied converters for renewable power integration. It presents a comprehensive control strategy that not only enhances the integration of renewable power sources into the power grid but also prioritizes system reliability and stability.

Quantum Simulation of the Tricritical Ising Model in Tunable Josephson Junction Ladders.

Modern hybrid superconductor-semiconductor Josephson junction arrays are a promising platform for analog quantum simulations. Their controllable and nonsinusoidal energy-phase relation opens the path to implement nontrivial interactions and study the emergence of exotic quantum phase transitions. Here, we propose the analysis of an array of hybrid Josephson junctions defining a two-leg ladder geometry for the quantum simulation of the tricritical Ising phase transition. This transition provides the paradigmatic example of minimal conformal models beyond Ising criticality and its excitations are intimately related to Fibonacci non-Abelian anyons and topological order in two dimensions. We study this superconducting system and its thermodynamic phases based on bosonization and matrix-product-state techniques. Its effective continuous description in terms of a three-frequency sine-Gordon quantum field theory suggests the presence of the targeted tricritical point and the numerical simulations confirm this picture. Our results indicate which experimental observables can be adopted in realistic devices to probe the physics and the phase transitions of the model. Additionally, our proposal provides a useful one-dimensional building block to design exotic topological order in two-dimensional scalable Josephson junction arrays.

Privacy-preserving intelligent fault diagnostics for wind turbine clusters using federated stacked capsule autoencoder

The emergence of Internet of Things (IoT) technologies in the field of health monitoring has introduced the paradigm of Industrial Internet of Things (IIoT) to the industry. IIoT systems provide enterprises with a substantial volume of monitoring data for industrial equipment health monitoring, facilitating the development of artificial intelligence fault diagnosis models. However, a singular industrial entity often encounters limitations in collecting sufficient training data in practical scenarios. Moreover, the sharing of confidential information among entities is strictly prohibited due to concerns regarding intellectual property and data security. This study proposes a fault diagnosis system that addresses this issue by incorporating a capsule-based fault feature expression into the federated learning (FL) framework. The system comprises clients distributed across multiple factories and a central server hosted in the cloud. The client models are trained on local private datasets, and then knowledge fusion is achieved by uploading intrinsic templates and pose matrices to the central server. The proposed method offers the advantage of reducing transmission burden and enhancing data security in comparison to existing FL approaches. Besides, a capsule knowledge alignment algorithm is proposed to update the capsule-based fault feature expression ona central server. To simulate real fault diagnosis application scenarios, two similar fault simulation platforms are built to acquire isolated fault diagnosis datasets. The effectiveness of the proposed method is verified using these datasets.

Power optimization of photovoltaic modules under varying environmental conditions based on current equalization collaborating constant voltage control

Abstract The performance of photovoltaic modules is affected by environmental factors such as irradiance and temperature, which can lead to a decrease in output performance or even damage. This study proposes an improved formula for calculating the real maximum power of photovoltaic modules by analyzing the influence of irradiance and temperature. A simulation model is developed using PLECS software to simulate the global maximum power of photovoltaic modules under different environmental conditions, and the results are compared with the calculated real maximum power. A power optimization scheme for photovoltaic modules is then proposed based on current equalization and constant voltage control. This scheme employs a single-switch multi-winding forward-flyback converter to equalize the mismatched currents between cell strings, thereby enhancing the output performance. Traditional proportional integral controllers are utilized to achieve constant voltage control and obtain the real maximum power of photovoltaic modules. Simulation models are built in the PLECS simulation platform to evaluate the performance of a global maximum power point tracking scheme based on the traditional perturb and observe algorithm with current equalization, a segment perturb and observe algorithm without current equalization, and the proposed power optimization scheme. The simulation results demonstrate that the proposed constant voltage control has greater efficiency than the traditional perturb and observe algorithm. The proposed scheme achieves a significant improvement in efficiency, with a 27.87% increase compared to the segment perturb and observe algorithm without current equalization.

Study on the ablation process and failure mechanism of the buffer layer in high-voltage XLPE cable

In recent years, the frequent occurrence of ablation faults in the buffer layers of high-voltage cross-linked polyethylene (XLPE) cables has significantly undermined the stability of power grid operations. However, existing research cannot fully explain the failure mechanism and transformation of related physical and chemical properties of high-voltage cable buffer layer. To address this, the paper have constructed an integrated analysis framework that includes multiphysics field simulations, an equivalent electrical network model, and simulated ablation experiments to comprehensively reveal the complete ablation failure mechanisms. Firstly, this study conducted a comparative analysis of the voltage distribution and temperature changes in the buffer layer using electro-thermal coupling simulations and an equivalent electrical network model. This analysis provides a detailed study of the dynamic development process of buffer layer ablation and identifies key electrical parameters. Subsequently, it verified the accuracy of the simulations through experiments on temperature variation characteristics, changes in hydrogen content, and other gas concentrations by constructing a simulated buffer layer ablation experimental platform. Finally, based on the study of the mechanisms of the buffer layer ablation failure process, this study divided it into three stages: initial electrochemical corrosion, appearance of white powder, and the onset of ablation discharge in the buffer layer. The comparison of experimental results with simulation data from the first two stages further validated the effectiveness and robustness of our analysis framework. In summary, this study provides a comprehensive analysis of the failure process in high-voltage cable buffer layers through simulation and experimental validation. This study provides a methodological basis for preventing cable ablation faults and detecting and evaluating the status of high-voltage XLPE cable buffer layers.

Impact of Physical Education Informatization on the Physical Fitness of College Students in Guangdong, China

The development of college students' physical fitness has become an area of great concern in Chinese university physical education. This study aims to explore the effects of physical education teaching informatization on college students' physical fitness through quasi-experimental research methods. Through the introduction of informatization teaching methods in university physical education courses, a more effective learning and teaching process can be achieved. In this study, 15 freshmen from Guangzhou Software College were selected as respondents, and relying on the Internet, an eight-week informatized physical education teaching was carried out using video instruction, AI interaction, AR simulation, and Internet platform push. The pre-test and post-test of physical fitness of the respondents at various stages before and after the intervention of informatization technology were conducted by Chinese college students' physical fitness test standards, and the learning experience of the respondents in physical education class was investigated by questionnaire survey method, and then the measured results were statistically counted and analyzed by statistical methods. The results of the study showed that the physical fitness levels of the respondents after receiving the 8-week informationalized physical education intervention were improved to different degrees compared to the pre-intervention level. In addition, the results of the questionnaire about the participants' learning experience showed that the informatized teaching could stimulate the respondents' interest in learning, increase the intensity and frequency of exercise, and cultivate the habit of exercise, and that the students were more actively involved in the classroom activities in the informatized teaching environment, formed a closer cooperative relationship with their peers, and enhanced the learning experience. Therefore, this study preliminarily verified the positive impact of physical education teaching informatization on college students' physical fitness. It is suggested that more informatized teaching means should be gradually introduced into university physical education teaching to promote the overall development of students and improve the level of physical fitness. Future studies can further explore in depth the applicability of informatization teaching in different physical education courses and different disciplines to provide more scientific guidance for university physical education teaching.

Media Image Transmission and Privacy Protection for Internet of Things Security

Due to their restricted resources, processing capacity, and memory, Internet of Things (IoT) gadgets are susceptible to certain security risks. Encryption methodologies were established to provide safe communication between IoT devices with minimal computing cost and bandwidth consumption, due to the rise in media date. This research proposes a revolutionary compact pixel crypto (CPC) technique that can accommodate the modest transmission speeds of IoT gadgets. The suggested techniques reduces the computational burden and data quantity by encrypting the image data using pixel driven selective encryption and bloke scanning compression. The suggested method’s effectiveness is examined using the network simulator (NS-2) platform. According to the findings of the experiments, the suggested method outperforms the previous approaches in terms of both packet rate and energy usage. The proposed approach shortens the time needed for node side encryption and decryption operations with improving the system’s energy effectiveness and connectivity performance.

Acinonyx jubatus-Inspired Quadruped Robotics: Integrating Neural Oscillators for Enhanced Locomotion Control.

This study presents the design, simulation, and prototype creation of a quadruped robot inspired by the Acinonyx jubatus (cheetah), specifically designed to replicate its distinctive walking, trotting, and galloping locomotion patterns. Following a detailed examination of the cheetah's skeletal muscle anatomy and biomechanics, a simplified model of the robot with 12 degrees of freedom was conducted. The mathematical transformation hierarchy model was established, and direct kinematics were simulated. A bio-inspired control approach was introduced, employing a Central Pattern Generator model based on Wilson-Cowan neural oscillators for each limb, interconnected by synaptic weights. This approach assisted in the simulation of oscillatory signals for relative phases corresponding to four distinct gaits in a system-level simulation platform. The design phase was conducted using CAD software (SolidWorks 2018), resulting in a 1:3-scale robot mirroring the cheetah's actual proportions. Movement simulations were performed in a virtual mechanics software environment, leading to the construction of a prototype measuring 35.5 cm in length, 21 cm in width, 27 cm in height (when standing), and weighing approximately 2.1 kg. The experimental validation of the prototype's limb angular positions and trajectories was achieved through the image processing of video-recorded movements, demonstrating a high correlation (0.9025 to 0.9560) in joint angular positions, except for the knee joint, where a correlation of 0.7071 was noted. This comprehensive approach from theoretical analysis to practical implementation showcases the potential of bio-inspired robotics in emulating complex biological locomotion.

Caching Policy in Low Earth Orbit Satellite Mega-Constellation Information-Centric Networking for Internet of Things.

Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a paramount role in network performance. Therefore, the in-network caching policy is one of the hotspot problems. Especially, compared to caching traditional internet content, in-networking caching IoT content is more challenging, since the IoT content lifetime is small and transient. In this paper, firstly, the framework of the LEO satellite mega-constellation Information-Centric Networking for IoT (LEO-SMC-ICN-IoT) is proposed. Then, introducing the concept of "viscosity", the proposed Caching Algorithm based on the Random Forest (CARF) policy of satellite nodes combines both content popularity prediction and satellite nodes location prediction, for achieving good cache matching between the satellite nodes and content. And using the matching rule, the Random Forest (RF) algorithm is adopted to predict the matching relationship among satellite nodes and content for guiding the deployment of caches. Especially, the content is cached in advance at the future satellite to maintain communication with the current ground segment at the time of satellite switchover. Additionally, the policy considers both the IoT content lifetime and the freshness. Finally, a simulation platform with LEO satellite mega-constellation based on ICN is developed in Network Simulator 3 (NS-3). The simulation results show that the proposed caching policy compared with the Leave Copy Everywhere (LCE), the opportunistic (OPP), the Leave Copy down (LCD), and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) yield a significant performance improvement, such as the average number of hops, i.e., delay, cache hit rate, and throughput.

A Geant4-based Monte Carlo X-ray imaging simulation platform

X-ray digital radiography plays a pivotal role in accessing the internal structure of objects. Nevertheless, key imaging parameters, such as source-to-target distance or number of projected images needed for three-dimensional construction, mostly rely on real-life trials thus increasing radiation exposure risks. This paper introduces a recently developed software platform known as GMAX (Geant4-based Monte Carlo Advanced X-ray) that helps addressing above issues from a simulation perspective. GMAX employs Geant4, a Monte Carlo simulation code at its core and facilitates high-fidelity X-ray imaging, requiring no prior user experience. Compared to CAD models that only reflect object's geometrical information, GMAX simulates how objects interact with photon and can accurately evaluate important imaging parameters, such as target object to detector distance. It also provides three-dimensional construction functionality for images and therefore could be used as an effective tool for X-ray non-destructive testing and inspection.

A patient-based realistic simulation platform to train future electrophysiologists to complex ablation procedures

Abstract Title An immersive catheter ablation platform based on patients’ 3D heart model for the simulation and training of future electrophysiologists. Background Arrhythmias are on the rise worldwide due to the ageing of the population. Projections urge to train the next generation of electrophysiologists (EPs) to perform catheter (CATH) ablation (CA) of arrhythmias. Training of future EPs relies mostly on a companionship, where teaching of CATH steerability is made on patients (pts) referred for procedures. While several other domains have established the utility of simulators to speed up learning process, increase safety and establish procedure road maps, dedicated simulators for EPs based on pts’ real anatomy do not exist yet. Purpose To speed up the handling of steerable CATH on MRI-based 3D heart models to optimize ablation road maps in pts referred for complex arrhythmias. Methods ARTS is an Artificial Intelligence and Augmented Reality-based platform comprising two key components: HeARTS and ARTSim. HeARTS is designed to automatically generate 3D heart models from pts’ MRI scans. ARTSim is a CA simulator that utilizes the 3D heart models of the pts for the planning and simulation of CA procedures. This simulator incorporates innovative techniques for digitizing and tracking the movements of a physical catheter in real time, and enabling its navigation within a completely virtual heart. With this technology, the CATH can be precisely steered to various heart locations, including the right and left (LA) atrium, the coronary sinus, and the right and left (LV) ventricles. Results Panel A of the figure shows the mannequin used for the simulator, where a real ablation CATH is introduced within the pt’s heart through a venous introducer. Panel B shows right anterior oblique (RAO) and modified RAO views of a pt suffering from a typical right atrial flutter. Note the presence of yellow tags positioned along the cavotricuspid isthmus (CTI) that the trainee must reach for a programmable duration using the shaft and the handle of the CATH shown in panel A. Panel C shows the virtual ablation CATH in green positioned on the target dots of the CTI. The pink color indicates that the trainee reached the desired target, which became red when the CATH remained stable at the same location for a duration of 5 sec (i.e. stability) with a force >5g (i.e. efficacy) and <20g (i.e. safety). Conclusions Herein, we present ARTS, a realistic CA simulation platform for the training of future EPs, that offers all the typical characteristics of 3D navigation including steerability, stability measurements and the force applied. Importantly, ARTS is based on the true anatomy of the pts, hence might be used to establish and discuss roadmaps to optimize procedures efficacy and safety. Version 2.0, foreseen early 2024, will include 3D late gadolinium enhancement and transseptal puncture for virtual LA and LV procedures, and later on activation times and myocardial voltage.Figure

Research on Stability Control of Distributed Drive Vehicle with Four-Wheel Steering

The four-wheel steering distributed drive vehicle is a novel type of vehicle with independent control over the four-wheel angle and wheel torque. A method for jointly controlling the distribution of the wheel angle and torque is proposed based on this characteristic. Firstly, the two-degrees-of-freedom model and ideal reference model of four-wheel steering vehicle are established; then, the four-wheel steering controller and torque distribution controller are designed. The rear wheel angle is controlled by the feedforward controller and the feedback controller. The feedforward controller takes the side slip angle of the center of mass as the control target, and the feedback controller takes the yaw angle as the control target. Torque is controlled by two control layers, the additional yaw moment of the upper layer is calculated by the vehicle motion state and fuzzy control theory, and the lower layer distributes wheel torque through the road adhesion coefficient and wheel load. Finally, a simulation platform is established to verify the effectiveness of the proposed control algorithm.

Approaches to embryonic neurodevelopment: from neural cell to neural tube formation through mathematical models.

The development of the human central nervous system initiates in the early embryonic period until long after delivery. It has been shown that several neurological and neuropsychiatric diseases originate from prenatal incidents. Mathematical models offer a direct way to understand neurodevelopmental processes better. Mathematical modelling of neurodevelopment during the embryonic period is challenging in terms of how to 'Approach', how to initiate modelling and how to propose the appropriate equations that fit the underlying dynamics of neurodevelopment during the embryonic period while including the variety of elements that are built-in naturally during the process of neurodevelopment. It is imperative to answer where and how to start modelling; in other words, what is the appropriate 'Approach'? Therefore, one objective of this study was to tackle the mathematical issue broadly from different aspects and approaches. The approaches were divided into three embryonic categories: cell division, neural tube growth and neural plate growth. We concluded that the neural plate growth approach provides a suitable platform for simulation of brain formation/neurodevelopment compared to cell division and neural tube growth. We devised a novel equation and designed algorithms that include geometrical and topological algorithms that could fit most of the necessary elements of the neurodevelopmental process during the embryonic period. Hence, the proposed equations and defined mathematical structure would be a platform to generate an artificial neural network that autonomously grows and develops.

AUV Obstacle Avoidance Framework Based on Event-Triggered Reinforcement Learning

Autonomous Underwater Vehicles (AUVs), as a member of the unmanned intelligent ocean vehicle group, can replace human beings to complete dangerous tasks in the ocean. It is of great significance to apply reinforcement learning (RL) to AUVs to realize intelligent control. This paper proposes an AUV obstacle avoidance framework based on event-triggered reinforcement learning. Firstly, an environment perception model is designed to judge the relative position relationship between the AUV and all unknown obstacles and known targets. Secondly, considering that the detection range of AUVs is limited, and the proposed method needs to deal with unknown static obstacles and unknown dynamic obstacles at the same time, two different event-triggered mechanisms are designed. Soft actor–critic (SAC) with a non-policy sampling method is used. Then, improved reinforcement learning and the event-triggered mechanism are combined in this paper. Finally, a simulation experiment of the obstacle avoidance task is carried out on the Gazebo simulation platform. Results show that the proposed method can obtain higher rewards and complete tasks successfully. At the same time, the trajectory and the distance between each obstacle confirm that the AUV can reach the target well while maintaining a safe distance from static and dynamic obstacles.

Power Optimization Strategy of DC Microgrid Based on Droop Control

As an efficient way to consume new energy, DC microgrid has received extensive and international attention. However, the control of the DC microgrids has not taken into account the difference in operating cost of micro sources, resulting in low operating efficiency. Therefore, this paper establishes an economic model for the DC microgrid, with the goal of minimizing operating cost, the optimal operating condition of the system is solved. On this basis, a secondary power optimization control strategy is proposed. Based on the simulation platform, the feasibility of the proposed strategy is verified under different operating conditions. The results show that the proposed power optimization strategy can effectively reduce the system operating cost by about 1.49%.

Towards an understanding of multi-generational higher education cohorts in gamified entrepreneurship education

The increasing inclusion of gamified courses in entrepreneurship programmes in higher education have left gaps in understanding the critical essentials of the multi-generational student cohort undertaking these programmes. In this paper, we interrogate the educational experiences of multi-generational higher education students in a core gamified entrepreneurship course in an undergraduate business school programme. The research analyzed 392 course feedback responses from three generations (X, Y and Z) of a multi-generational cohort. The study developed and validated a behaviour-results model for gamified entrepreneurship courses leading to student entrepreneurial intention and entrepreneurial orientation, and disaggregated student engagement into it's multiple dimensions of cognitive, behavioural and emotional. The model also validated six dimensions of individual entrepreneurship orientation. Using the model, the study found differences in the component variables based on student Generations X, Y, and Z. Also, student cognitive and behavioural engagement led to entrepreneurial intention which also influenced student entrepreneurial orientation. There were marked differences in student grit, cognitive engagement, and emotional engagement between Generations X and Z. Furthermore, generational differences existed amongst Generation Z and Y, and also for Generation Z and X in student entrepreneurial intention. The study also confirmed the difference in entrepreneurial orientation between Generations X and Z. Additionally, the study found that there is a need to contextualize student engagement facilitators such as results demonstrability of the business simulation platform, student grit and user characteristics as they have selective effects on student cognition, behavioural and emotional engagements in a multi-generational student cohort of Generation X, Y and Z.

Advancements and Challenges in Virtual Surgery: A Comprehensive Review

Virtual surgery, also known as surgical simulation, has emerged as a transformative technology in the field of medicine, offering innovative solutions for surgical training, preoperative planning, and surgical skill enhancement. This paper provides a comprehensive review of recent advancements, applications, and challenges in virtual surgery. We discuss various virtual surgery techniques, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), and their contributions to surgical education, patient care, and surgical innovation. Additionally, we examine the current state-of-the-art virtual surgery systems, simulation platforms, and surgical training curricula. Furthermore, we explore the challenges and limitations associated with virtual surgery, such as technological constraints, validation and assessment issues, ethical considerations, and cost-effectiveness. Finally, we discuss future directions and opportunities for research and development in virtual surgery, with a focus on addressing existing limitations and advancing the field towards widespread adoption and integration into clinical practice. Keywords: Virtual surgery, surgical simulation, virtual reality, augmented reality, mixed reality, surgical training, preoperative planning, surgical education, surgical innovation.

Disturbance observer–based finite‐time fault tolerant control of quadrotor unmanned aerial vehicles with command filtered and event‐triggered techniques

AbstractThis article primarily focuses on the finite‐time fault‐tolerant tracking control problem of quadrotor unmanned aerial vehicle (UAV) systems. Our main innovation points are: (1) A simple cubic form of Lyapunov function is applied to the design of the position ring controller of the quadrotor UAV; (2) The command filtering technology effectively solves the challenge of derivation of complex coupling matrixs in the controller design process; (3) An improved disturbance observer and a variable threshold event‐triggered mechanism are applied in the Quadrotor UAV system. In summary, the overall approach combines the backstepping method and finite‐time Lyapunov stability to prove the finite‐time stability of all the signals in the system. Finally, the effectiveness and rationality of the algorithm are verified through simulation experiments and its application on an actual simulation platform.