To enhance the modeling accuracy of high-speed maglev linear synchronous motors, a distributed parameter modeling method considering the influence of the suspension system was proposed based on the magnetic co-energy reconstruction of the electromagnetic module. Firstly, a finite element model of the electromagnetic module of a high-speed maglev train was established. Finite element numerical analysis was conducted to obtain magnetic co-energy data of the electromagnet module under different operating conditions. The magnetic co-energy was then subjected to Fourier series expansion and polynomial fitting to construct an analytical model of the magnetic co-energy. Subsequently, based on the analytical model of the magnetic co-energy, equations for the flux linkage, voltage, and thrust force of the electromagnetic module were derived. Then, mathematical models for the left and right linear synchronous motors based on the number of train formations and the number of electromagnetic modules were established, and the position and velocity of the high-speed maglev train were calculated through kinematic equations. Finally, the proposed modeling method was validated through experiments using a hardware-in-the-loop simulation system. The experimental results indicate that compared to traditional modeling methods, the proposed modeling method increases the amplitude of thrust fluctuations by more than 6.8%. Moreover, the proposed method can accurately characterize the influence of the suspension system on traction control. When the harmonic amplitude of the excitation current increases by 0.5 A, 1.0 A, and 2.0 A, the maximum increase in the amplitude of thrust fluctuations is 54.3%, 26.2%, and 83.7%, respectively. Furthermore, when the harmonic frequency of the excitation current is at 5 Hz, 10 Hz, and 20 Hz, the harmonic frequency of the thrust reaches up to 5.14%, 21.75%, and 14.17%, respectively.

A spectral identification method for calculating the magnitude of inherent misalignment in a rotor-magnetic bearing system was proposed to study and identify mixed parallel misalignment of the rotor system occurring at the bearing. The momentum moment theorem was used to equate the effect of the disc unbalance force on the rotating shaft to the axial force of the rotor and establish a dynamics model of the rigid double offset disc rotor-magnetic bearing system considering the axial and radial coupling effects. The SIMULINK simulation was used to calculate the displacement and current response of the system in the time domain, and the dynamics characteristics of the rotor system under the misalignment condition were analyzed. Furthermore, the fast Fourier transform was utilized to convert the response in the time domain into that in the frequency domain. The magnitude of the misalignment of the rotor system was then calculated based on the least squares algorithm in the frequency domain. The results show that the error in the calculated magnitude of misalignment by using this method is within 5.0% under the influence of shock excitation, indicating that even if the rotor is affected by external disturbance forces, the algorithm can accurately quantify the rotor’s misalignment. This provides a theoretical reference for fault diagnosis and self-repair of misaligned rotor-magnetic bearing systems.

In the multi-electric aircraft engine, an active magnetic bearing can break through the limitation of temperature on the support part due to its high temperature resistance and non-contact characteristics, which enables its support part to be closer to the combustion chamber. In order to investigate the influence of temperature on the dynamic characteristics of the magnetic bearing-rotor system, a dynamics modeling method for a high-temperature magnetic bearing-rotor system was proposed. The temperature distributions of the rotor at different temperatures were obtained through simulation, and the axial temperature distribution of the rotor was fitted using polynomials. Based on the finite element method, the dynamics model of the flexible rotor unit was derived. The temperature influence was introduced, and the overall dynamics model of the magnetic bearing- rotor system considering the temperature influence was established. The accuracy of the model was verified by a modal test. The dynamic characteristics of the system were analyzed based on the theoretical dynamics model, and the results show that an increase in temperature leads to a decrease in the first three orders of the support modal frequency of the rotor and an increase in the amplitude of the amplitude frequency response of each order. When the temperature increases from room temperature to 450 ℃, the first three orders of the bending support modal frequency of the rotor decrease by 3.818%, 5.670%, and 3.183%, respectively, and the amplitudes of the first three orders of the bending modal amplitude frequency response increase by 83.4%, 34.4%, and 24.1%, respectively.

In magnetic bearing-rotor systems, the bending mode vibration may be excited by the interface contact formed by bolt joints during rotor levitation, and the vibration frequency varies with rotation speed. To actively control bending mode vibration at any speed, the design method of a robust H∞ controller considering frequency uncertainty was proposed. Firstly, the dynamic model considering interface contact was established for numerical simulation, and the vibration frequency variation was obtained. Then, the rotor transfer function was reconstructed by frequency response fitting, and the variation range of vibration frequency obtained by simulation was introduced into the reconstructed transfer function by means of additive uncertainty. As a result, a controlled object model considering mode frequency uncertainty was obtained. Finally, based on the model, the robust H∞ controller was designed by taking the robustness to parameter perturbations and external disturbance, closed-loop system stability, control voltage saturation, and other functions into account. The numerical simulation results show that the controller has the frequency response characteristic of wide band resistance at the mode frequency, which is able to suppress the bending mode vibration of the magnetic bearing-rotor system. After the robust H∞ controller designed by this method is used, the bending mode vibration amplitude of the rotor is reduced by more than 90%.

In view of the high-precision motion control problem of the maglev rotary table with nonlinearity, coupling, and uncertainty, a fractional-order sliding mode control method based on a nonlinear disturbance observer was proposed to improve the tracking accuracy. Firstly, based on the electromagnetic force model of the system and the dynamic decoupling method, the dynamical model of the six-degree-of-freedom maglev rotary table system was constructed. Secondly, a nonlinear disturbance observer was designed to estimate the lumped disturbance including system error, coupling term between six degrees of freedom, and external interference. It was proved that the estimation error was bounded and could be made arbitrarily small. Then, a fractional-order sliding surface was proposed in the discrete domain, where the fractional power function was used instead of the traditional symbolic function to suppress jitter, and the fractional calculus was introduced to reduce the tracking error. Finally, a fractional-order sliding mode control strategy with finite time convergence was designed, and the stability of the closed-loop system was proved by Lyapunov stability theory. The experimental results reveal that compared to the integer-order sliding mode control method, the proposed method reduces the root mean square of tracking error for triangular waves by 12.8%, 16.8%, and 23.7% for the two horizontal degrees of freedom and the rotational degree about the vertical axis, respectively, while the maximum tracking errors are reduced by 9.26%, 13.00%, and 33.20% respectively. When tracking a circular trajectory, the mean square values of tracking errors for two horizontal degrees of freedom are decreased by 6.39% and 12.40%, and the maximum tracking errors are reduced by 9.90% and 12.10%, respectively.

Due to the special structure and dynamic end effect of linear induction motors, the change mechanism and law of their excitation inductance and secondary loss resistance are complicated. In order to improve the identification accuracy and performance of the observer for excitation inductance and secondary loss resistance, an online dual-parameter identification method of linear induction motors based on an improved interconnected full-order observer was proposed. Firstly, based on the T-type equivalent circuit of the linear induction motor considering dynamic end effects, the state space equations with dual-parameter changes were established, and the influence of parameter changes and coupling characteristics on motor poles was analyzed. Secondly, to reduce the impact of parameter coupling on identification accuracy, a low-coupling identification model with dual-parameter interconnection was established, and an interconnected full-order adaptive observer was designed. The adaptive laws for online identification of excitation inductance and secondary resistance were derived using Popov hyperstability theory, realizing online dual-parameter identification. Then, to improve the stability and convergence speed of the observer, a feedback gain matrix was derived and designed by using a novel pole configuration method. Finally, a simulation model and hardware-in-the-loop identification model were built for experimental verification. The results show that the new full-order adaptive observer achieves excitation inductance and loss resistance identification errors of around 0.01% during the startup acceleration phase and around 0.03% during dynamic loading.

In order to improve the accuracy and efficiency of the magnetic force calculation of hybrid electromagnets, this paper took into account the advantages of fast calculation speed of the analytical method and high calculation accuracy of the finite element method and proposed a calculation method of the magnetic force of hybrid electromagnets based on the nonlinear inductance. The paper first analyzed the relationship between the inductance and current of the hybrid electromagnet and established a nonlinear inductance model considering magnetic saturation. Then, the equivalent surface current method was used to equate two typical hybrid electromagnet structures (structure a, structure b) into pure electromagnet structures with multi-electromagnetic coils. The magnetic force expression of the series magnetic circuit type hybrid electromagnet was derived by using the energy balance method, in which the parameter variables of the nonlinear inductance model were fitted by the finite element simulation method. The research results show that the average deviations between the electromagnetic force calculation results of structures a and b obtained by the proposed method and the traditional finite element simulation are 2.54% and 2.37%, and the average deviation between structure a and experimental measurement is 2.63%. Compared with the traditional finite element method, the calculation efficiency is greatly improved. In other words, the proposed method obtains electromagnetic force calculation results that are much more accurate than the existing analytical formulas through finite element simulation with fewer tasks.

The new method of continuous centrifugal separation instead of dialysis membrane has improved the quality of life of patients with kidney disease who depend on hemodialysis treatment. As a result, the research on artificial kidney pumps has been paid much attention by many scholars, but the conventional artificial kidney pump is supported by rolling bearings, and it thus causes problems such as high hemolysis and high thrombosis rate. In order to solve these problems, this paper developed a compact and energy-saving single-degree-of-freedom controlled magnetic levitation bearing applied to an artificial kidney pump by using the advantages of non-contact, non-lubrication, and high rotation speed of magnetic levitation bearing. The finite element analysis software was used for simulation to explore the design parameters of the radial passive control part and the axial active control part, and the overall simulation was verified. Then the structural performance of the magnetic levitation bearing was evaluated. The results show that the simulated and experimental radial displacement stiffness coefficients are 47.432 N/mm and 49.531 N/mm; the axial current stiffness coefficients are 0.144 N/AT and 0.135 N/AT, and the axial displacement stiffness coefficient is 223.071 N/mm, which meet the requirements of five-degree-of-freedom stable suspension of this magnetic levitation bearing. The designed magnetic levitation bearing simplifies the system structure, reduces the control difficulty, and lowers the power consumption of the system.

The permanent magnet suspension (PMS) transportation system utilizes the magnetic repulsion between the on-board permanent magnet and the permanent magnetic rail to achieve levitation. Understanding and mastering the relationship between the magnetic force of the permanent magnet and its spatial pose parameter are crucial for designing the running gear of the maglev train and track structure. A three-dimensional magnetic field finite element model was developed for the on-board permanent magnet and the permanent magnetic rail based on the PMS transportation system of Xingguo County. The magnetic force on the on-board permanent magnet was calculated under different levitation gaps, lateral offsets, pitching angles, rolling angles, and yawing angles. The variation patterns of levitation force and lateral force of the on-board permanent magnet with respect to these five pose parameters were analyzed, as well as their correlation degree. The results indicate that the magnetic force of the on-board permanent magnet is primarily influenced by the levitation gap and lateral offset, with a greater impact from the yawing angle and minimal effect from the pitching angle and rolling angle. Within the specified range of parameter variations, the ratios of the minimum and maximum values of the levitation force to the rated levitation force are approximately 0.75 and 1.16, respectively. In addition, the maximum value of the lateral force reaches 50.42% of the rated levitation force. The direction of the lateral force of the on-board permanent magnet aligns with its lateral offset direction, while the yawing torque aligns with its yawing angle direction. Consequently, a guiding device is necessary for the PMS system to prevent lateral bobbing and yawing movements of the on-board permanent magnet.

In order to study the influence of controller time lag on the stability of the levitation system of the magnetic levitation train, firstly, the two-degree-of-freedom magnetic levitation train levitation system model is established by taking displacement-velocity as the feedback control parameter, and the controller time lag is taken into account; secondly, the stability region of the time lag-free system is obtained by the stability criterion of Routh-Hurwitz, meanwhile, based on the characteristic root crossing the imaginary axis boundary condition, we obtain the critical value of the time lag of the controller when the system undergoes Hopf bifurcation; finally, we analyze the relationship between the feedback control parameters and the system parameters and the critical value of the controller time lag. The results show that: when the system parameters are certain, the critical value of the controller time lag decreases with the increase of the displacement control gain, and increases and then decreases with the increase of the velocity control gain; when the feedback control parameters are certain, the critical value of the controller time lag decreases with the increase of the secondary suspension stiffness, and increases with the increase of the secondary suspension damping; as the time lag of the system increases asymptotically by an order of magnitude 10⁻⁶ around the critical value of time lag, the system will gradually change from stable-periodic motion-unstable, during which the supercritical Hopf bifurcation occurs.

To explore the dynamic responses of medium-low speed maglev trains and bridges under stochastic track irregularities, the pseudo excitation method was introduced into the vibration analysis of the maglev train-bridge system. A stochastic vibration analysis method for the medium-low speed maglev train, suspension control system, and bridge coupling system was proposed. The medium-low speed maglev train was simplified as rigid bodies connected by spring dampers, and the current in the suspension system was actively controlled using the proportional-differential (PD) control method. The bridge was modeled by using a finite element method, and the stochastic track irregularity was converted into a pseudo excitation composed of a series of simple harmonic waves. The stochastic vibration analysis program for the medium-low speed maglev train-bridge dynamic system was developed, which could automatically generate the stochastic vibration equations of the system, and the separation iteration method was used to solve the control equation of the maglev train and the dynamic equation of the bridge. The research results indicate that the pseudo excitation method can efficiently calculate the stochastic dynamic response of the medium-low speed maglev train-bridge system, with a calculation efficiency of about 1/11 of the Monte Carlo method. Based on the pseudo excitation method, statistical results such as mean, standard deviation, and time-varying power spectral density of the medium-low speed maglev train-bridge dynamic system can be obtained.

The positioning and speed measurement method for normal-conducting high-speed maglev trains based on tooth slot detection of long stator may have inaccurate positioning caused by the lack of speed measurement and positioning signal, interference, and installation error of speed measurement and positioning during maglev train operation. Therefore, in order to improve the accuracy of positioning and speed measurement of high-speed maglev trains, an unscented Kalman filter (UKF) speed measurement and positioning algorithm for normal-conducting high-speed maglev trains based weighted fusion was proposed. The speed measurement and positioning method for high-speed maglev trains based on the tooth slot of a long stator was introduced, and the multi-channel redundant speed and position information was pre-treated, adaptively weighted, and fused. The UKF speed measurement and positioning algorithm for normal-conducting high-speed maglev trains based on weighted fusion was given. Based on the speed measurement and positioning in-loop test of the maglev train on a testbed, the improved UKF maglev position algorithm was compared with the original positioning algorithm. The analysis shows that the average speed error of the maglev train is reduced by 32.6%, and the speed range is reduced by 49.3%, which effectively eliminates the signal acquisition noise and improves the accuracy of speed measurement and positioning of maglev trains.

Accurate and smooth parking is an essential goal for automatic driving braking control of maglev trains. The strong coupling of the electro-hydraulic hybrid braking state affects the medium and low-speed maglev trains during the stopping braking process, and the traditional braking control method based on the theoretical model of braking features makes it difficult to guarantee the parking accuracy and comfort of the maglev train. This paper proposed a reinforcement learning braking control method for maglev trains based on self-learning of hybrid braking features. First, a long short-term memory (LSTM) network was used to establish a hybrid braking feature model for maglev trains, and the self-learning of dynamic braking features was performed based on the operating environment and status data of maglev trains. Then, the reward function and learning strategy of reinforcement learning were updated according to the learning results of dynamic features, and a train braking optimization control method based on deep reinforcement learning was proposed. Finally, simulation experiments were carried out by using on-site operation data of medium and low-speed maglev trains. The experimental results show that the braking control method proposed in this paper improves comfort and parking accuracy by 41.18% and 22%, respectively, compared with the traditional method. It proves the effectiveness of the modeling and braking optimization control method in this paper.

Improper control parameters of the suspension system of maglev trains may lead to abnormal vibration of the train-bridge system. Therefore, it is important to clarify the relationship between the control parameters of the suspension system and the dynamic response of the maglev train-bridge system. Firstly, the dynamic model of a 5-car maglev train with proportional-differential control, as well as the finite element model of a 20-span simply supported beam bridge was established. Secondly, the correctness of the models was verified by comparing them with the measured results. Finally, the dynamic responses of the train and bridge under different control parameters at 430 km/h were calculated. The results show that increasing the proportional coefficient will increase the stiffness of the suspension and guidance system, and increasing the differential coefficient will increase the damping of the suspension and guidance system. The vertical acceleration of the car body increases with the increase in the proportional and differential coefficients, and the lateral acceleration of the car body increases with the increase in the proportional coefficient. The suspension gap and the vertical acceleration of the bridge decrease with the increase in the proportional coefficient, and they increase with the increase in the differential coefficient. The guidance gap decreases with the increase in the differential coefficient, and the proportional coefficient has little effect on the guidance gap. The lateral acceleration of the bridge decreases with the increase in the proportional coefficient and increases with the increase in the differential coefficient. The vertical acceleration of the bridge is mainly affected by a characteristic frequency of 1–12 times of the length of the suspended electromagnet in the electromagnetic force, and the lateral acceleration of the bridge is mainly affected by the characteristic frequency and frequency of 2 times of the length of the guidance pole, as well as the characteristic frequency of 2 times and 4 times of the length of the guidance electromagnet. In order to reduce the dynamic response of the train-bridge system, it is suggested that the values of vertical proportional and differential coefficients should be 3 000–4 000 and 10–25, respectively, and the values of lateral proportional and differential coefficients should be 4 000–5 000 and 10–25, respectively.

Traditional linear vibration isolation system fails to achieve a lower initial vibration isolation frequency after setting the dimensional parameters. To address this issue, this article presented an electromagnetic vibration isolation system with variable stiffness based on the structure of a permanent magnet nested in an electromagnetic coil. To be specific, the system was characterized by high static stiffness and low dynamic stiffness. The mathematical model of the magnetic force of the system was created using the molecular current method. In addition, the strongly nonlinear dynamic model of the single-degree-of-freedom passive vibration isolation system was established by fully considering the quadratic and cubic nonlinear stiffness terms in the mechanical model of the vibration isolation system. The article used the incremental harmonic balance (IHB) method to solve the dynamic model and analyze the influence of excitation, current, and other factors on the displacement transmissibility of the system. An experimental test system was then created to validate the effectiveness of the proposed vibration isolation system. The experimental results and theoretical calculation demonstrate that the initial vibration isolation frequency of the system is reduced by 19.25% after introducing the current. This expands the frequency range of vibration isolation and improves system adaptability to different vibration sources.

To solve problems such as the large construction height of separated track beams for medium and low speed maglev and the inability to consider the stiffness contribution of F rail, an integrated track beam structure with a perforated steel plate was proposed. In addition, a full-scale model test and finite element simulation calculation were carried out to investigate the static performance of the steel-concrete joint. Firstly, the engineering background of the integrated track beam and the structural characteristics of the steel-concrete joint were introduced. Secondly, the static load model test of the steel-concrete joint was designed to test the stress and displacement of F rail, steel connector, and concrete components under various load scenarios. Finally, a solid finite element model of the steel-concrete joint was established, and the mechanical properties, force transmission mechanism, and design parameters of the steel-concrete joint were analyzed based on the test data. The results show that: 1) within 1.50 times the design load, the steel-concrete joint is basically in an elastic state, and the bearing capacity of the connector meets the design requirements. With the increase in load, the load–stress curve of F rail shows certain nonlinearity. Under 5.47 times the design load, the concrete beam cracks. 2) The displacements of the internal and external magnetic poles of F rail are small, and the stiffness of the steel-concrete joint is large. The displacement difference of the internal and external magnetic poles of F rail meets the design limit under the design load. Under 1.58 times the design load, the displacement difference reaches 0.54 mm, which begins to exceed the limit value. It shows that the stiffness abundance of the steel-concrete joint is less than the strength abundance, indicating that the design of the steel connector should be controlled by stiffness. 3) Under normal operational conditions, the maglev train load is mainly transmitted by the steel bearing plate of the steel connector, and the force transmission ratio between the welding nail and the perforated steel plate is small. The diameter of the hole and the diameter of the steel bar in the steel connector have little influence on the force transmission, and the increase in the thickness of the web of the steel connector makes the force transmission at the steel-concrete joint smooth.

Unmanned aerial vehicles (UAVs) can be rapidly and cost-effectively deployed. By deploying the base station equipment to the launching UAV platform, the air-ground networks of UAVs can quickly build ground coverage network from the air, so it has broad application prospects in emergency relief, remote area coverage, intelligent transportation, smart city, and other aspects and has received wide attention in recent years. Based on the application scenario of air-ground networks of UAVs, the characteristics of UAVs’maneuver, network, and load were considered. From the four dimensions of coverage performance improvement of air-ground networks, integrated communication-sensing-calculation design of air-ground networks, reconfigurable intelligent surfaces (RIS)-assisted air-ground networks, and robust air-ground networks of UAVs, the research status of air-ground networks of UAVs was reviewed in terms of network scenarios, key technical challenges, and performance optimization control methods. In addition, the future research direction of optimizing the performance of air-ground networks of UAVs was explored.

In order to study the autonomous control problem of unmanned aerial vehicle (UAV) networks in emergency communication scenarios, multiple rotary-wing UAVs equipped with 6G base stations were used, and a low-altitude and single-layer UAV network was formed through the interconnection between UAVs, thus providing wireless network services to users in the ground task area. A numerical model was constructed for typical scenarios, and a multi-agent reinforcement learning method was used to solve the optimization control strategy of the UAV network. The effects of the number of UAV base stations and the communication distance of UAV base stations on the wireless network coverage in the task area were investigated. The research results indicate that the optimization control strategy obtained by using reinforcement learning methods can converge well. The learning curves of the UAV network coverage score and fairness coverage index have similar trends. The curves rapidly increase between the 1000th and 2000th episode and then change slowly. Under the conditions of a communication coverage distance of 1 km and a flight altitude of 300 m for UAV base stations, the UAV network coverage score increases by 53.28%, and the fairness coverage index increases by 43.57% when the number of UAV base stations increases from 3 to 7. Under the condition of five UAV base stations and a flight altitude of 300 m, the UAV network coverage score increases by 86.01%, and the fairness coverage index increases by 41.47% when the communication distance of the base stations increases from 1.0 km to 2.5 km.

Collaborative target perception technology of unmanned aerial vehicles (UAVs) is an important security guarantee for the mixed operation of manned aerial vehicles and UAVs. In view of the perception reliability problem in complex airspace environments, the operation scenarios of large and medium-sized UAVs in complex mixed airspace were analyzed, and the needs of collaborative target perception of UAVs, such as precision, high real-time performance, anti-interference, and low load were determined. A collaborative target perception system architecture of UAVs combining a four-unit array antenna and digital radio frequency was proposed. At the same time, the signal characteristics and antenna system of air traffic control (ATC) radar were utilized to design an azimuth perception algorithm. By modifying the covariance matrix and weighting signal subspace and noise subspace, a spatial spectrum estimation algorithm based on multiple signal classification (MUSIC) was designed. In addition, an online amplitude-phase error estimation algorithm based on subspace decomposition was designed. Finally, the algorithm simulation test and flight test in a real airspace environment were carried out. The research results show that compared with the traditional MUSIC algorithm, the improved algorithm improves the high resolution performance of azimuth perception by 23.3% and enhances the high real-time performance, anti-interference, and low load of the collaborative target azimuth perception of UAVs.

In the ultra-reliable low-latency communications (URLLC) multi-unmanned aerial vehicle (UAV) network, to satisfy the ultra-reliable low-latency requirements, the intelligent reflecting surface (IRS) in the air was introduced to assist in communication, and a multi-intelligent deep deterministic policy gradient (MADDPG) method was proposed. First, the URLLC multi-UAV system model was established, in which multiple primary UAVs acted as airborne base stations to provide services for multiple ground users, and one auxiliary UAV carried an IRS as an airborne passive relay to assist the primary UAV in communicating with the ground users. The composite channel model and the total energy model were established respectively by considering multiple channel conditions and energy consumption. Second, the problem was analyzed to minimize the total decoding error rate by jointly optimizing the communication schedule, IRS phase shift, and block length while satisfying the constraints of finite block length, UAV energy, and IRS phase shift. Finally, the MADDPG framework with centralized training and distributed execution was designed by considering the delay-sensitive constraints of centralized training in URLLC scenarios and the energy constraints of distributed training under the resource limitations of UAVs. The results show that the total decoding error rate decreases sharply with the increase in IRS units. Meanwhile, the total decoding error rate decreases with the increase in block length and transmitted power. To be specific, the total decoding error rate decreases by 91.1% as every 20 symbols are added to the block length.

To enhance the performance of the unmanned aerial vehicle (UAV)-assisted communication network based on the orthogonal frequency division multiple access (OFDMA) mode, the rational network allocation and optimal allocation of communication resources were studied. Firstly, in order to maximize the fairness of the network, a mixed-integer nonlinear maximum-minimum optimization problem was modeled by combining the communication resources including sub-channel allocation, modulation mode selection, and power allocation with UAV position. Then, the iterative optimization method was used to solve the problems of variable coupling and non-convex, and the maximum-minimum problem was converted into two sub-problems: joint optimization of sub-channel allocation and modulation mode selection and joint optimization of UAV position and sub-channel power. Finally, by means of appropriate transformations, the two subproblems were modeled into 0–1 linear optimization problem and convex optimization problem for solution. The experimental simulation results show that the proposed algorithm can jointly optimize multidimensional system parameters such as network allocation and communication resources, effectively enhance the fairness of network users, and improve network performance compared with other benchmark schemes.

In the communication scenario of unmanned aerial vehicles (UAVs), the airborne integrated sensing and communications (ISAC) system is affected by the strong self-interference caused by local signal transmission, which degrades the sensing performance of the system to the target. To solve this issue, a self-interference cancellation technology of the ISAC system based on orthogonal frequency division multiplexing (OFDM) was proposed. Firstly, the echo model of the ISAC system based on OFDM was established, and a self-interference signal was introduced. Then, the channel gain of the self-interference signal was estimated by the least squares algorithm, and the self-interference signal was reconstructed and suppressed by the gain. Finally, the effectiveness of the proposed method was verified by simulation experiments in the communication scenario of UAVs. The results show that the proposed method can suppress the self-interference signal to the noise power level and increase the signal-to-interference plus noise ratio (SINR) of the target echo signal by nearly 10.00 dB, so as to effectively improve the sensing performance of the system.

Traditional federated learning has limitations in unmanned aerial vehicle (UAV) swarm applications, which require all participants to perform the same tasks and have the same model structure. Therefore, a multi-task federated learning (M-Fed) method suitable for UAV swarms was explored, and an innovative encoder-decoder architecture was designed to enhance knowledge sharing among UAVs performing different tasks. Firstly, a direct knowledge-sharing mechanism was established for UAVs performing the same tasks, enabling effective knowledge fusion of the same tasks through direct aggregation. Secondly, for UAVs performing different tasks, the encoder parts were extracted from the encoder-decoder architectures of all UAVs to construct a global encoder. Finally, during the training process, the information from both the local encoder and the global encoder was integrated into the loss function. Iterative updates were then performed to gradually align the local decoder with the global decoder, achieving efficient cross-task knowledge sharing. Experimental results demonstrate that compared to traditional methods, the proposed method improves the performance of UAV swarms by 1.79%, 0.37%, and 2.78% on three single tasks, respectively. Although there is a slight decrease of 0.38% in performance on one task, the overall performance is still significantly increased by 2.38%.

Unmanned aerial vehicles (UAVs) are important weapons and equipment for military powers in the world to carry out tactical operations. Applying UAVs in the battlefield to achieve data storage is an important way to ensure the operation. In order to better meet the data storage requirements of UAV clusters under harsh battlefield conditions, the military application background of UAV data storage was analyzed, and the data resources characteristics of UAVs in the battlefield and military requirements of data storage technologies were summarized. The design process of data storage method suitable for tactical operations of UAVs from Chinese armed forces was put forward. Then, on the basis of the achievements of data storage in the field of academia and industry, the classification and development process of data storage technologies were reviewed, and the main types of data storage were summarized. The key technologies and storage systems of distributed data storage for tactical operations of UAV clusters were presented. Moreover, the research status of the algorithm models of distributed mass data storage and processing, real-time data transmission, and data reliability in China and abroad was introduced. The research direction for the data application method of UAVs in tactical operations was proposed, and it was pointed out that data storage of UAV clusters in battlefields is an important research field in the future.