Simulation Experiments Research Articles

Optimization study of air-cooled heat dissipation structure based on waterborne electric airplane

Abstract Taking the water electric aircraft as the research object, to solve the problem of high temperature of the motor, controller and radiator in the powerhouse when the aircraft is travelling under the high temperature condition in summer. Firstly, the three-dimensional model of the power module of the electric aircraft is established and simulation experiments are carried out, and then according to the simulation results, the power module is optimized and improved, and after simulation experiments, it is known that the highest temperature of the surface of the electric motor is 432.83K, the highest temperature of the surface of the controller is 370.82K, and the highest temperature of the surface of the radiator is 368.67K, and the final experimental results show that the optimization meets the cooling requirements of the aircraft flight. The final experimental results show that the optimisation meets the heat dissipation requirements of the aircraft flight.

Study on the development and dust suppression performance of a novel composite dust suppressant for metal mines

The significant amount of dust generated during the metal mining process affects both personnel health and the environment. Some mines, such as magnetite mines, produce magnetic dust that is easily adsorbed onto equipment, causing equipment failure and other issues. To solve such problems, a composite dust suppressant was synthesized in this study using carboxymethyl cellulose sodium (CMC-Na), primary alcohol ethoxylate (AEO), and polyethylene glycol (PEG) as raw materials. Firstly, the optimal ratio was determined according to the response surface method. Subsequently, the performance and dust reduction efficiency of the suppressant was evaluated. According to the optimal formulation ratio, the dust suppressant exhibited a viscosity of 34.5 mPa·s and a surface tension of 28.46 mN/m. The wind erosion resistance, compressive strength, and water retention of the dust samples were improved following the dust suppressant application. The loss rates were only 0.025% and 0.9% at wind speeds of 3.5 m/s and 20 m/s, respectively. Simulation experiments indicated that the suppressant achieved a dust suppression efficiency of 82.7% for respirable dust and 90.91% for total dust. These results demonstrate that the suppressant possesses high dust removal efficiency and effective dust suppression, aligning with the application requirements of metal mines and contributing to the mitigation of dust issues during mining operations.

A Probabilistic Approach to Correct for Oblique Biases in Sea Ice Parametrization from Shipborne Cameras

Abstract This work investigates the perception bias caused by ice floes occluding open water regions behind them when analyzing sea ice from shipborne cameras. We propose a method to model this effect by predicting an occlusion probability for a given surface region based on a stochastic representation of the sea ice fields. We further show how the proposed framework can be used to design an unbiased estimator for image-based sea ice concentration by correcting for these occlusions. The occlusion model and proposed sea ice concentration estimator have been verified through simulated experiments. Results from the experiments showed that predictions from the occlusion model align with the simulated observations and that the proposed estimator remains unbiased for all environments tested. The contributions of this study can be used in algorithms for automated identification and characterization of ice environments from surface ships and are thus an essential step towards improved autonomy in Arctic navigation.

Event-triggered sliding mode control for networked T–S fuzzy delayed systems against random injection attacks

This paper investigates the event-triggered sliding mode control (SMC) problem for networked T–S fuzzy delayed systems subject to random injection attacks, where the event-triggered mechanism (ETM) is introduced to alleviate data congestion and improve network efficiency. Under an open network environment, malicious attackers may randomly inject false information into the controller–actuator channel to compromise the integrity of the data. A new fuzzy sliding mode controller is constructed based on a non-parallel distributed compensation strategy, which introduces the distribution information of the attack probability. Then, the membership function dependent (MFD) analysis technique is adopted to reconstruct the membership functions in order to reduce the conservatism caused by the mismatch between the membership functions of fuzzy model and controller. Furthermore, the co-design conditions for the ETM and the fuzzy sliding mode controller are provided, which can ensure that the resulting closed-loop fuzzy system is mean-square exponentially ultimately bounded (EUB) and the sliding surface is reachable. Finally, a simulation experiment with tunnel diode circuit application is utilized to demonstrate the validity of the proposed SMC scheme.

Exploring the improvement path of virtual simulation experiments: based on the influencing factors and mediating effects of learning satisfaction

BackgroundVirtual simulation experiment is a vital component of higher education digitalization, emerging from the deep integration of specialty disciplines and information technology. Chinese universities have invested heavily in the construction of virtual simulation experiment projects, and the number of projects is considerably large. However, the popularity and effectiveness of these projects are not satisfactory. As an important index to measure the teaching quality, analyzing the influencing factors and mediating effects of learning satisfaction of virtual simulation experiment is an effective entry point to improve the quality and efficiency of virtual simulation experiment teaching and improve the learning effect.MethodsThis study used quantitative research methods. The research sample includes 538 students from various levels of higher education institutions such as Nanjing University, Nantong University, Nanjing Medical University, and Jiangsu University, covering 11 majors. They started to study the virtual simulation experiment course for one academic year in September 2023 and conducted a questionnaire survey at the end of the course. The questionnaire survey involved four aspects, mainly from the student dimension, course dimension, the technical dimension, and the embodied dimension to evaluate the learning satisfaction of the virtual simulation experiment course. The data were analyzed using SPSS and AMOS.ResultsAccording to the results of the structural equation modeling (SEM), all of the student dimension (learning motivation {β = 0.658,p < 0.001} and task value {β = 0.339,p < 0.001}), course dimension (course content {β = 0.275,p < 0.001}, course flexibility {β = 0.052,p < 0.002}, and course quality {β = 0.635,p < 0.001}), technical dimension (interface design {β = 0.445,p < 0.001}, interaction design {β = 0.151,p < 0.001}, technical adaptability {β = 0.225,p < 0.001}, and technical reliability y{β = 0.140,p < 0.001}), and embodied dimension (social presence {β = 0.270,p < 0.001} and spatial presence {β = 0.689,p < 0.001}) have positive effects on improving the learning satisfaction of virtual simulation experiments. Meanwhile, the technical dimension exhibited a mediating effect in the influence of embodied dimension on learning satisfaction.ConclusionsTo enhance the learning effectiveness of virtual simulation experiments, we can put forward the strategies from four dimensions including students, course, technical, and embodied to specifically address learning satisfaction. This approach would provide directional guidance for the development of medical virtual simulation experiments.

Evaluation of source rocks and prediction of oil and gas resources distribution in Baiyun Sag, Pearl River Mouth Basin, China

By conducting organic geochemical analysis of the samples taken from the drilled wells in Baiyun Sag of Pearl River Mouth Basin, China, the development characteristics of hydrocarbon source rocks in the sag are clarified. Reconstruct the current geothermal field of the sag and restore the tectonic-thermal evolution process to predict the type, scale, and distribution of resources in Baiyun Sag through thermal pressure simulation experiments and numerical simulation. The Baiyun Sag is characterized by the development of Paleogene shallow lacustrine source rocks, which are deposited in a slightly oxidizing environment. The source rocks are mainly composed of terrestrial higher plants, with algae making a certain contribution, and are oil and gas source rocks. Current geothermal field of the sag was reconstructed, in which the range of geothermal gradients is (3.5–5.2) °C/100 m, showing an overall increasing trend from northwest to southeast, with significant differences in geothermal gradients across different sub-sags. Baiyun Sag has undergone two distinct periods of extensional process, the Eocene and Miocene, since the Cenozoic era. These two periods of heating and warming events have been identified, accelerating the maturation and evolution of source rocks. The main body of ancient basal heat flow value reached its highest at 13.82 Ma. The basin modelling results show that the maturity of source rocks is significantly higher in Baiyun main sub-sag than that in other sub-sags. The Eocene Wenchang Formation is currently in the stage of high maturity to over maturity, while the Eocene Enping Formation has reached the stage of maturity to high maturity. The rock thermal simulation experiment shows that the shallow lacustrine mudstone of the Wenchang Formation has a good potential of generating gas from kerogen cracking with high gas yield and long period of gas window. Shallow lacustrine mudstone of the Enping Formation has a good ability to generate light oil, and has ability to generate kerogen cracking gas in the late stage. The gas yield of shallow lacustrine mudstone of the Enping Formation is less than that of shallow lacustrine mudstone of the Wenchang Formation and the delta coal-bearing mudstone of the Enping Formation. The numerical simulation results indicate that the source rocks of Baiyun main sub-sag generate hydrocarbons earlier and have significantly higher hydrocarbon generation intensity than other sub-sags, with an average of about 1 200×104 t/km2. Oil and gas resources were mainly distributed in Baiyun main sub-sag and the main source rocks are distributed in the 3rd and 4th members of Wenchang Formation. Four favorable zones are selected for the division and evaluation of migration and aggregation units: No. ▪ Panyu 30 nose-shaped structural belt, No. ▪ Liuhua 29 nose-shaped uplift belt and Liwan 3 nose-shaped uplift belt, No. ▪ gentle slope belt of Baiyun east sag, and No. ▪ Baiyun 1 low-uplift.

Current-Voltage-Friction Characteristics of Grease in Electromechanically Loaded Sliding Contacts

Abstract Electromechanically loaded contacts which have relative motion between the contacting parts experience severe damage compared to mechanical-loaded contacts. Such electromechanical environment occurs in the bearings of traction motors through which different types of currents flow, due to the usage of electronic speed control devices. The passage of current through the contact depends on the voltage potential developed across the contact. Grease is frequently used as a lubricant, and degradation and evaporation of lubricant due to the joule heating effect is a concern in the electromechanical contacts. In this study, the current-voltage-friction characteristics of lithium mineral oil grease are reported using a ball-on-disk configuration under combined electrical and mechanical loading. The characteristics indicated a transition of the lubricated contact from a non-conducting state to a conducting state with an increase in applied voltage. Two critical voltages are identified: one where the friction is observed to rise and the other is where the current flow rapidly increases leading to accelerated damage to the lubricant by inducing a significantly high temperature. The study helps in identifying permissible voltage levels to operate bearings safely from the perspective of grease lubricant using simplified ball-on-disk simulated experiments.

Computer Simulation and Simulation of Mechanical and Electrical Equipment based on Artificial Intelligence Algorithms

The computer in mechanical and electrical equipment can now detect equipment faults through simulation thanks to the advancement of artificial intelligence (AI) technology, which makes it convenient to monitor mechanical and electrical equipment. This paper begins with a quick introduction to Agent and the Agent system, explains how Agent is applied in the current context, then analyzes the hierarchical fault diagnostic model, identifies its flaws, and suggests improvement techniques. To increase the model’s accuracy and speed of operation, the contract net model and D-S (Dempster-Shafer) evidence theory are then incorporated. Finally, simulation experiments are used to confirm the accuracy and consistency of this model. According to the experimental findings, this optimization model runs at a pace that is noticeably faster than that of other models when subjected to the same workload, demonstrating the model’s efficacy. Models 1, 2, and 3 are put side by side to demonstrate how clearly multi-task processing may cut down on the model’s running time. In the second experiment, samples of mechanical and electrical equipment defect data are taken from two groups. The results of the comparative experiments demonstrate that the optimized model in this work can be reliable up to a maximum of 0.91 and a minimum of 0.63. It is demonstrated that the model in this work is rational by the fact that among the four types of fault prediction, the optimized model’s reliability is significantly greater than the traditional model’s. The research described in this publication therefore has some reference value for computer simulation of electromechanical equipment.

Nonlinear coupled asymmetric stochastic resonance for weak signal detection based on intelligent algorithm optimization

Stochastic resonance has been extensively studied for detecting weak signals. To improve the diagnostic ability of weak signals, a novel nonlinear coupled asymmetric stochastic resonance (NCASR) system is investigated in this paper. Firstly, the NCASR system is established by coupling the asymmetric bistable system with the monostable system. Next, the expressions for the steady-state probability density (SPD) function, the mean first passage time (MFPT) and the signal-to-noise ratio (SNR) of the proposed system are derived based on the adiabatic approximation theory. Furthermore, the impact of system parameters on the SPD, the MFPT and the SNR is analyzed. Then, by simulation experiments, we verify the effectiveness of detecting weak signals for the NCASR system with Lévy noise. Finally, the NCASR system optimized by Adaptive Weighted Particle Swarm Optimization (AWPSO) algorithm is applied to detect the bearing fault signal. Compared with the optimized classical bistable stochastic resonance (CBSR) system, it is found that the detection performance of the NCASR system is superior to the CBSR system in detecting bearing fault signals.

Experimental Validation of a Novel Hybrid Equilibrium Slime Mould Optimization for Solar Photovoltaic System

Maximizing Power Point Tracking (MPPT) is an essential technique in photovoltaic (PV) systems that guarantees the highest potential conversion of sunlight energy under any irradiance changes. Efficient and reliable MPPT technique is a challenge faced by researchers due to factors such as fluctuations in irradiance and the presence of partial shading. This paper introduced a novel hybrid Equilibrium Slime Mould Optimization (ESMO) MPPT-based algorithm combining the advantages of two recent algorithms, Slime Mould Optimization (SMO) and Equilibrium Optimizer (EO). The ESMO algorithm is compared with highly efficient MPPT-based techniques such as SMO, EO, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA), both under a Simulink environment and a real-time experimental laboratory setup using a Dspace1104 controller and PV emulator. The comparison focuses on performance under several irradiance cases, including instant irradiance change, partial shading, complex partial shading, and dynamic partial shading. The key advantage of ESMO is the fact that it has a single tunable parameter, which makes implementation much easier and, at the same time, reduces the computational resources that are required by the control system. Extensive testing proves the superiority of ESMO over all other techniques, the average efficiency of which is 99.98% under all conditions. Additionally, ESMO provides fast average tracking times of 244 ms under simulation experiments and 200 ms for real-time experiments. These results show that ESMO can be very important for future implementation in large-scale solar PV systems.

A fusion model for multiple partial-order quotient spaces based on three-way decision

With a quotient space, a complex problem can be transformed into an appropriate granularity for processing. However, within a quotient space, there are no partial order relations between information granules. In practical decision-making, it may be necessary to deal with quotient spaces that have partial order relations between information granules. In this paper, first, the concept of a partial-order quotient space is proposed and some of its properties are studied. Second, a fusion model for multiple partial-order quotient spaces based on three-way decision is proposed, and some related theorems are proved. Finally, a case study of teacher evaluation, an experiment on real data, and several simulation experiments are presented to demonstrate the practicality of the proposed model in this paper.

The Security and Protection System of Electromechanical Equipment in Smart Campus using the Improved Data Mining Algorithm

In order to improve the maintenance and management efficiency of campus electromechanical equipment and reduce or even avoid the safety risks brought by campus electromechanical equipment, this work uses the data mining algorithm to design the security and protection system of campus electromechanical equipment. First, this work constructs the campus electromechanical equipment classification model using the Bayesian algorithm of data mining algorithm and designs a simulation experiment to verify the effect of the classification model. Then, the security and protection system for the campus electromechanical equipment is designed. It includes the system business process, system function design, system core module’s function design and system implementation. Finally, a simulation experiment is designed to verify the system’s performance. The results show that: (1) Bayesian algorithm is superior to the K-Nearest Neighbor (KNN) algorithm in both classification effect and running time. (2) When the browser concurrency in the system increases, the server processor and memory usage also increases, but the value meets the expected requirements. It shows that the system has a certain browser concurrency-bearing capacity. Moreover, as the browser concurrency of the system increases, the response time of the test also increases, but the value meets the expected requirements. This work aims to improve the maintenance efficiency of campus electromechanical equipment and provide a reference for the safety protection work of electromechanical equipment in other enterprises or units.

Fixation behavior of oceanic manganese nodule-sodium alginate composite microspheres for heavy metal release during manganese nodule mining

Research on the environmental impact of deep-sea mining is crucial, particularly for fragile deep-sea ecosystems. This research focuses on the issue of heavy metal release during mining activities. Through simulation experiments, we investigated the release of Cu2+, Co2+, and Ni2+ from sediments under disturbance conditions and the fixation behavior during the deployment of ocean manganese nodule-sodium alginate composite microspheres (OMN@SA). The experimental results revealed that mining disturbances cause the release of 0.291% of Cu2+, 7.34% of Co2+, and 4.13% of Ni2+ from sediments into the water, primarily in the form of exchangeable metals. Compared with the bottom adsorption, OMN@SA has a faster adsorption rate in the slow settling process. The removal rates of Cu2+, Co2+ and Ni2+ reached 54.0%, 78.3% and 61.8% for 5 h adsorption, and the bottom adsorption removal rates reached 96.4%, 97.8% and 95.1% for 30 d adsorption, which has a good removal effect. In addition, OMN@SA can effectively block the diffusion of Cu2+, Co2+, and Ni2+ from interstitial water to overlying water, and reduce the influence of interstitial water on overlying water. SEM-EDS, FTIR, and XRD analyses revealed that OMN@SA adsorbs heavy metal ions through its abundant -OH groups and incorporates Cu2+, Co2+, and Ni2+ into the crystal lattices of vernadite and todorokite via substitution or intercalation. This study provides guidance for the remediation of heavy metal release from deep-sea mining using adsorption methods and demonstrates the promising application prospects of OMN@SA.

The Defect Identification System of Electromechanical Equipment on the Edge Side of the Power Grid under Edge Computing

With the development of the industrial Internet of Things, modern industrial systems have developed towards intelligence. Electromechanical Equipment (EE) is essential, and its defect identification is fundamental. Firstly, this research introduces the basic content of Gated Recurrent Unit (GRU), Variational Auto-Encoder (VAE) in Deep Learning (DL), and Edge Computing (EC) to explore the construction of a defect identification system for EE on the edge side of the power grid. Secondly, combined with the advantages of GRU and VAE, a GRU-VAE defect recognition model is proposed. Then, the EC architecture is introduced, and the EE defect identification system based on the GRU-VAE algorithm is constructed. The EC intelligent EE defect identification service system is designed with this as the core. Finally, simulation experiments are carried out using different data sets to verify the performance of the GRU-VAE model. The results show that the GRU-VAE model has higher precision and recall than the separate GRU model and VAE model, and the corresponding F1 value is also higher. The F1 value can reach 0.997 on aperiodic data and 0.966 on periodic data. In addition, the optimal thresholds of different datasets are analyzed, and the relationship between the length of the time window and the model’s performance is studied. When the time window length is 15, the model performance is optimal. This research on the defect identification system of EE on the edge side of the power grid based on EC and DL can provide a new path and inject new vitality into the defect detection of EE.

Development and comparison of model-integrated evidence approaches for bioequivalence studies with pharmacokinetic end points.

By applying nonlinear mixed-effect (NLME) models, model-integrated evidence (MIE) approaches are able to analyze bioequivalence (BE) data with pharmacokinetic end points that have sparse sampling, which is problematic for non-compartmental analysis (NCA). However, MIE approaches may suffer from inflation of type I error due to underestimation of parameter uncertainty and to the assumption of asymptotic normality. In this study, we developed a MIE BE analysis method that is based on a pre-defined model and consists of several steps including model fitting, uncertainty assessment, simulation, and BE determination. The presented MIE approach has several improvements compared with the previously reported model-integrated methods: (1) treatment, sequence, and period effects are only added to absorption parameters (such as relative bioavailability and rate of absorption) instead of all PK parameters; (2) a simulation step is performed to generate confidence intervals of the pharmacokinetic metrics for BE assessment; and (3) in an effort to maintain type I error, two more advanced parameter uncertainty evaluation approaches are explored, a nonparametric (case resampling) bootstrap, and sampling importance resampling (SIR). To evaluate the developed method and compare the uncertainty assessment methods, simulation experiments were performed for BE studies using a two-way crossover design with different amounts of information (sparse to rich designs) and levels of variability. Based on the simulation results, the method using SIR for parameter uncertainty quantification controls type I error at the nominal level of 0.05 (i.e., the significance level set for BE evaluation) even for studies with small sample size and/or sparse sampling. As expected, our MIE approach for BE assessment exhibited higher power than the NCA-based method, especially as the data becomes sparser and/or more variable.

Mitigating critical nodes in brain simulations via edge removal

Brain simulation holds promise for advancing our comprehension of brain mechanisms, brain-inspired intelligence, and addressing brain-related disorders. However, during brain simulations on high-performance computing platforms, the sparse and irregular communication patterns within the brain can lead to the emergence of critical nodes in the simulated network, which in turn become bottlenecks for inter-process communication. Therefore, effective moderation of critical nodes is crucial for the smooth conducting of brain simulation. In this paper, we formulate the routing communication problem commonly encountered in brain simulation networks running on supercomputers. To address this issue, we firstly propose the Node-Edge Centrality Addressing Algorithm (NCA) for identifying critical nodes and edges, based on an enhanced closeness centrality metric. Furthermore, drawing on the homology of spikes observed in biological brains, we develop the Edge Removal Transit Algorithm (ERT) to reorganize sparse and unbalanced inter-process communication in brain simulation, thereby diminishing the information centrality of critical nodes. Through extensive simulation experiments, we evaluate the performance of the proposed communication scheme and find that the algorithm accurately identifies critical nodes with a high accuracy. Our simulation experiments on 1600 GPU cards demonstrate that our approach can reduce communication latency by up to 25.4%, significantly shortening simulation time in large-scale brain simulations.

Secure virtual coupling control of connected train platoons under cyber attacks

Virtual coupling of automated trains has emerged as a promising technology to enhance the operational efficiency and line capacity of railway transportation systems. However, the vulnerability of such systems to malicious cyber attacks poses a significant threat to their stability, reliability and applicability. This article addresses the challenge of ensuring secure virtual coupling control of connected train platoons subject to various cyber attacks, including false data injection attacks, denial-of-service attacks, and replay attacks. First, based on the low-dimensional and noisy sensor measurements, novel set-valued observers are designed for each follower train to estimate their inaccessible full longitudinal motion states despite uncertainties and unknown attacks and noises. Then, distributed secure attack-compensation-based virtual coupling control laws are developed to ensure the platoon's stability and resilience against cyber attacks. Additionally, formal stability analysis of the resultant estimation error system and platoon tracking error system is provided. Furthermore, two algorithms are presented to elaborate the offline observer and controller design process as well as their online implementation. Finally, based on the data of a realistic urban rail transit line, simulation experiments are conducted to validate the efficacy of the proposed theoretical findings.

Dynamic workload balancing to part input sequencing for integrated scheduling decision-making of flexible manufacturing systems

Flexible manufacturing systems (FMSs) are crucial in contemporary manufacturing environments because they provide the required agility and adaptability to face volatile customer demands. Balancing workload among different work stations of an FMS is essential to ensure optimal resource utilization, minimize idle times, and improve overall system performance. This research is aimed to study the application of dynamic workload balancing to part input sequencing for integrated FMS scheduling decision-making. In this work, we propose an online algorithm that balances workload dynamically in FMSs. A mathematical model of FMS scheduling integration is presented. The proposed approach schedules tasks not only on machines but also on the robot responsible for part movement within a shop floor. The proposed approach is evaluated in two aspects of comparison and analysis. Data analysis is made by simulation experiment and statistical analysis. Conclusions with managerial implications for practical applications and future research suggestions are provided.

Edge Computing Method for False Data Injection Attack Detection in Electromechanical Transient Simulation Grid

Because edge computing is close to the terminal, it has significant advantages of low latency and high-security realtime control and has huge application prospects in the current popular smart grid. Because its edge side is close to the terminal, it can also effectively avoid the risk of information leakage when control instructions or communication data are transmitted to remote cloud center servers over a long distance. However, edge devices have a greater probability of encountering false data injection attacks (FDIAs) from illegal terminals because of their proximity to terminals. The transient electromechanical situation of the smart grid due to FDIAs is analyzed under edge computing. Due to the characteristics that the status values of grid nodes have temporal correlation before and after and spatial correlation between nodes, the Long Short-Term Memory (LSTM) for training time series data is selected to predict the status values of grid nodes in advance. The FDIA detection method is also proposed based on the LSTM network. By calculating the predicted value at each historical moment and the root means square error (RMSE) of the system state estimation at that moment, the detection threshold of the scheme is calculated through the cumulative distribution function of RMSE. Simulation experiments are conducted on the Institute of Electrical and Electronics Engineers (IEEE)-14 node standard system. The detection rate is as high as 99.71%, which verifies the effectiveness of the proposed FDIA detection scheme. This paper studies the security threat of FDIA to the stable power system operation and provides theoretical analysis and practical reference for other power grid security protection strategies.

Design of motion control system for unmanned wheeled electric vehicle

Abstract The unmanned wheeled electric vehicle is a weapon platform that draws significant attention in current and future battlefields, and research on its motion control is fundamental. This paper introduces the design of a 6×6 unmanned wheeled electric vehicle and discusses the motion control methods applicable to this vehicle. The electric drive system of this vehicle comprises six independent asynchronous motors and corresponding motor controllers. A comprehensive controller designed based on DSP connects the six motor controllers. As it is an experimental platform, the motion control algorithms are deployed on an onboard computer, which connects to the comprehensive controller. Using the Backstepping method, the kinematic control algorithm for the 6×6 unmanned wheeled electric vehicle is designed. The global asymptotic stability of the control system is proven through the Lyapunov function, and its control performance is verified via simulation experiments. Finally, the system is deployed and tested on the actual vehicle. Experimental results show that the motion control system designed in this paper enables the unmanned wheeled electric vehicle to achieve effective motion control. The control laws derived from the Backstepping method are relatively simple and have low requirements for the sampling period. In practical systems, output speed magnitude and rate of change must be limited to stay within allowable system parameters.