An adaptive active disturbance rejection control strategy integrating DRL(deep reinforcement learning)with enhanced PSO(particle swarm optimization)was presented, aiming to improve the speed and thrust control performance of PMSLMs(permanent magnet synchronous linear motors).A mathematical model of the motor was established to analyze its dynamic characteristics, followed by the design of a DRLPSO control framework.This framework leveraged reward mechanisms in reinforcement learning to interact with the environment, dynamically optimized ADRC(active disturbance rejection controller)parameters to accommodate varying operating conditions and external disturbances.The modified PSO algorithm incorporated partitioned inertia weights and cyclically utilized historical global optimal data to iteratively update control policies, refining neural network weights and thereby enhancing search efficiency and optimization accuracy.Experimental results show that the proposedDRLPSO-ADRCmethod achieves significantly higher tracking precision in position and velocity, along with improved system stability and resistance to thrust disturbances, compared to conventionalPSO-ADRCalgorithms.These findings validate the effectiveness of the innovative control strategy.

Modular multilevel converters exhibit significant capacitor voltage ripple under low-speed, high-torque operating conditions. Existing high-frequency injection suppression schemes increase device current stress and losses while introducing overmodulation risk, and their parameter optimization lacks full operational-condition adaptability.To resolve this issue, a high-frequency injection parameter adaptive optimization strategy considering multiple constraints was proposed.Based on system characteristics and a steady-state model, a variable-step gradient descent algorithm was employed offline to generate a minimum injection-amplitude base parameter reference table that satisfies both capacitor voltage ripple and modulation wave constraints.Subsequently, an online adaptive correction mechanism was designed.Injection parameters were dynamically adjusted in real-time according to acquired capacitor voltage ripple and modulation information, compensating for model deviations and operational variations, forming a coordinated architecture of offline global optimization and online local refinement.Simulation and experimental results show that the proposed strategy maintains the capacitor voltage ripple suppression effect while significantly reducing high-frequency circulating currents, demonstrating dynamic tracking capability for the optimal objective.

To improve the design performance of long-range guided rockets, a multidisciplinary parametric model of long-range guided rockets was first established to achieve high-precision performance simulation of guided rockets.A sequence approximation optimization method based on an improved augmented radial basis function was proposed, which enhanced the generalization ability of the augmented radial basis function model through anisotropic techniques.Recursive evolution experimental design and fast cross-validation were used to improve the efficiency of approximation modeling, and an imprecise search strategy was applied for sequence sampling.The effectiveness of the proposed optimization method was verified through numerical examples.A sequence approximate optimization design of the long-range guided rocket was carried out, and the maximum range increase by 16.7% compared to before optimization while satisfying design constraints.

To meet the requirements of lightweight and low error sensitivity in the optimization design of stiffened panels, the optimization design of stiffened panels was carried out considering the twist angle error of stringers.The finite element model of post-buckling instability of stiffened panels under axial compression was established, and the sensitivity of the load-carrying capacity to the twist angle error on stringers and the distribution position of the torsional stringer was analyzed.On this basis, a sequential approximate optimization method based on surrogate model was proposed by using parallel sequential sampling strategy, and the lightweight design of stiffened panel was carried out under the influence of twist angle error of stringers.The optimized results show that, compared with the optimization design scheme without error influence, the optimization scheme considering the twist angle error of stringers has lower sensitivity to the twist angle error when the weight is reduced by more than 32%, which can effectively improve the reliability and engineering application value of the optimized structure.

Thermally-induced oscillatory rarefied gas flow inside a two-dimensional rectangular cavity was investigated.The effects of the Knudsen numbers and the oscillation frequency of lid temperature on the flow parameters were analyzed.The Shakhov model equation was solved numerically based on the mesoscopic approach in the near-wall region, and the macroscopic approach was adopted in the bulk flow region to reduce the computational cost.To close the numerical iteration procedure, the velocity distribution functions, served as the pseudo boundary between macroscopic and mesoscopic methods, were reconstructed using the high-order Hermite polynomials.Numerical simulations demonstrate that the temperature profile at the central vertical of the cavity predicted by the hybrid method is in good agreement with results from the mesoscopic method, with maximum error 0.23%.Besides, the computational memory cost can be saved up to about 69.91%.The hybrid approach is able to capture the nonlinear phenomenon in the thermally-induced oscillatory rarefied gas flow under high Kn numbers, where the horizontal velocity no longer obeys the law of periodic oscillating cosine function, and the rise time of the horizontal velocity is much longer than the fall time.The thickness of the viscous penetration layer and the disturbed region increases as the Kn number increases, and decreases as the St number increases.

In monocular vision-guided high-precision inter-platform pose measurement, existing methods require an accurate 3D model of the target platform and are unable to eliminate the impact of 3D model errors on pose measurement.To address this issue, iterative optimization was performed on the 3D model of the target platform and pose, and a new monocular vision measurement method was proposed.Specifically, the target platform′s 3D model was modeled using a set of sparse 3D keypoints.By leveraging multi-view geometric constraint information in sequential images, the sparse 3D keypoint set of the target and 6D pose were treated as parameters to be solved.An objective function was established to minimize object-space residuals, and through solving this optimization problem, iterative optimization of the sparse 3D keypoint set and pose was achieved.Additionally, a sliding window combined with a keyframe selection strategy was adopted to realize real-time and online high-precision monocular vision measurement.Experimental results demonstrate that, through iterative optimization of the sparse 3D keypoint set and pose, the proposed method achieves real-time, online high-precision monocular pose measurement under the condition of an inaccurate 3D model of target platform, while simultaneously improving the accuracy of the target′s 3D model.

The StS(state-to-state)model and MT(multi-temperature)model were used to numerically simulate and analyze the high-temperature air flow of 11 chemical species behind normal shock waves.The StS model resolved vibrational levels of neutral molecules and electronic levels of neutral atoms;the MT model distinguished the translational-rotational temperature, vibrational temperatures of neutral molecules, and the electron temperature.Simulation results for velocities ranging from 5 km/s to 11 km/s before the shock front demonstrate that immediately behind the shock wave, due to the dissociation and ionization reactions, the higher vibrational levels of molecules and the higher excited electronic levels of atoms are underpopulated relative to the Boltzmann distribution at the corresponding temperatures.Compared to the StS model, the MT model shows that the excitation of vibrational and electronic energies and the attainment of thermal equilibrium in different energy modes occur later, while chemical reactions also take place later but reach chemical equilibrium earlier.The MT model underpredicts vibrational energy loss from chemical reactions while overpredicting electronic energy loss due to electron-impact ionization.Moreover, obtained derived vibrational temperatures of molecules and electron temperature fail to accurately characterize the nonequilibrium population distributions of particle energy levels.

To investigate the effect of interlaminar properties on the tensile properties of fiber hybrid composites, two kinds of epoxy resins with different toughness, 7901 and 9A16, were used as the matrix.Interlayer carbon/glass hybrid composites with different numbers of carbon fiber layers were designed and manufactured.The effects of mode Ⅱ interlaminar fracture toughness(G_ⅡC)on the failure mode and mechanical properties of carbon/glass hybrid composites were investigated through both theoretical and experimental investigation.The results show that, the higher mode Ⅱ interlaminar fracture toughness is, the more the carbon layer tends to fail in fragmentation, achieving a higher critical thickness for fragmentation, which is beneficial for achieving pseudo-ductility.In addition, the G_ⅡC on the modulus and strength of hybrid composites is marginal, as the variation is within 5%.However, the G_ⅡCdemonstrates a significant impact on the pseudo-ductility strain, which is decreased by 40.7% when the G_ⅡC is increased from 1.75 N/mm to 2.08 N/mm.

Power semiconductor modules are the core energy conversion units in power converters.By optimizing their design, the power density can be significantly enhanced.However, current design methods lack systematic summaries.To address this, a systematic summary across four levels(material, chip, packaging and drive)was presented.This included utilizing wide bandgap materials, enhancing chip structure, adopting advanced packaging and improving gate drive design.The underlying principles behind these methods for increasing power density were summarized, and classified and compared the existing research on improving the power density of converters based on power semiconductor module design.The primary challenges in current research were combed, and the future development trend was forecasted.

The issues of small UAV(unmanned aerial vehicle)target size, limited pixel coverage in images, weak texture detail information, and the difficulty in effectively extracting infrared UAV target features, which lead to low detection accuracy, were addressed by proposing a multiscale learning-based target detection algorithm.A multi-scale feature fusion structure was constructed in the neck network of the model, and a multi-scale feature learning module was introduced.Features from both deep and shallow networks were cascaded to capture target features at multiple scales, enriching the semantic and feature information of the feature map, which significantly improved the detection accuracy of small UAV targets.During training, SIoU was used in place of CIoU loss, minimizing the network model′s loss and enhancing the regression accuracy. Experimental results demonstrate that, compared to other infrared small target detection algorithms and mainstream methods, the proposed approach effectively improves the detection accuracy of UAV targets and meet the detection accuracy requirements for UAV target detection in practical applications.