To address the challenge of simultaneously ensuring tracking accuracy, safe bottom clearance, and compliance with actuator constraints for an autonomous undersea vehicle(AUV) operating near complex seabed terrain, this paper proposes a terrain-following method based on sliding-window B-spline planning and a meta-learning-based, weight-adaptive linear time-varying model predictive control(LTV-MPC) strategy. The planning layer employs cubic B-splines subject to slope constraints to generate a reference path that maintains a prescribed altitude margin, while a sliding-window mechanism balances local optimization with global continuity. The control layer implements an LTV-MPC controller equipped with a meta-learning network that adaptively adjusts the cost-function weights online based on features such as terrain curvature, depth error, and attitude error, thus accommodating variations in tracking difficulty across different terrain conditions. The meta-network parameters are trained offline through Bayesian optimization using closed-loop simulation data from multiple synthetic terrains, enabling the network to learn the mapping between terrain features and optimal weights. In the online stage, only forward inference is required, resulting in high computational efficiency. In the specified simulation scenarios, validation using synthetic and measured terrain data demonstrates that the proposed method reduces the root-mean-square(RMS) tracking error by 42.5% and 31.3%, respectively, compared with line-of-sight guidance combined with proportional-integral-derivative control(LOS-PID). Compared with standard LTV-MPC, the RMS tracking error is reduced by 4.5% and 6.1%, respectively. Meanwhile, the pitch-angle and rudder-angle constraints are satisfied, effectively resolving the trade-off between tracking accuracy and safety under complex terrain conditions.
The low-speed control performance constitutes a fundamental prerequisite for the propulsion system of unmanned undersea vehicle to execute critical missions such as deep-sea exploration and military reconnaissance effectively. In response to the need for enhanced control capabilities during low-speed operations, limitations in permanent magnet synchronous motor drive systems employing both schemes with and without position sensor were systematically examined. Resolvers tend to introduce position detection errors under harsh environmental conditions. However, among dominant sensorless solutions, due to inherent observation dead zones near zero speed in back-electromotive-force observers, high-frequency signal injection methods improve low-speed observation performance, but their accuracy remains susceptible to motor parameter variations. Moreover, the accuracy of all sensorless control schemes exhibits high dependence on current sampling precision, making such schemes vulnerable to severe engineering challenges in complex disturbance-intensive operating conditions. To resolve these issues, a hybrid observation-based low-speed disturbance rejection control strategy integrating resolver with high-frequency square wave injection was proposed. By applying hardware redundancy and information fusion techniques, the deep integration was achieved between the absolute position reference provided by resolvers and dynamic observations generated through high-frequency square wave injection. An advantage-complementary observation architecture was established to significantly enhance system robustness in difficult scenarios including low-speed operations, variable loading conditions, and signal interference contexts. Simulation results verify the capability of the proposed method to effectively suppress detection error disturbance from position sensors and current sensors, enabling stable and precise rotor position observation and delivering a high-reliability control solution for underwater equipment power systems.
With the continuous development of marine conservation and exploration, traditional underwater actuation devices have inherent drawbacks such as complicated structures and low motion efficiency. Flexible materials have gradually become a research focus in the field of underwater biomimetic actuation due to their advantages of strong adaptability, high safety, and sufficient flexibility. Relying on the material advantages of high energy density and high electromechanical coupling efficiency of electroactive polymer(EAP), and combining the flexural deformation and elastic recovery effects of a spring, this paper designed a cylindrical biomimetic caudal fin actuator by simulating the periodic deformation process of contraction and relaxation of the body-caudal fin(BCF) propulsion mode, which could achieve the continuous compliant changes of the caudal fin muscle. Based on hydrodynamic theory, this study systematically analyzed the coupling mechanism between the movement law of the caudal fin and the propulsion force, constructed an instantaneous mechanical model of fin ray oscillation, and integrated experimental data for solution. Furthermore, this study established a three-dimensional numerical simulation model using Fluent software and verified the effectiveness of the model through the comparative analysis between the dynamic mesh calculation results and the mechanical model prediction results. This study provides theoretical support and experimental basis for the design and development of new biomimetic fish.
In the complex underwater acoustic countermeasures where underwater high-speed maneuvering platforms face the coexistence of true and false targets, incorrect target selection may lead to the escape of true high-value targets. Traditional non-targeted research strategies are inefficient, necessitating an in-depth analysis of high-value target countermeasure strategies, comprehensive consideration of their maneuverability and tactical choices, and the formulation of targeted counter-countermeasure and research strategies. Based on the analysis of high-value target countermeasure strategies, a target escape area model was constructed, and a probability estimation method was proposed for lost target escape areas to predict the escape probability of targets within the countermeasure area. The simulation results indicate that this method helps improve the success rate of target re-search and reduce the search time of underwater high-speed maneuvering platforms, thus enhancing overall search efficiency.
To counter the threat posed by undersea vehicle stealth and meet the demand for the development of non-acoustic detection technology, this study focused on the action mechanism of electromagnetic effects induced by vehicle wakes in density-stratified ocean environments. Existing studies on wake electromagnetic fields are mostly based on the uniform fluid assumption, neglecting the effects of internal waves induced by stratification. To this end, this paper innovatively built a mathematical model for the velocity field of undersea vehicle wakes in stratified fluid, decomposing the wake into a linear superposition of surface wave and internal wave components, with the expression for wake-induced electromagnetic fields derived. By conducting numerical simulations, the spatial distribution, attenuation patterns, and component contributions of the induced magnetic field were analyzed for undersea vehicles at depths ranging from 10 m to 50 m. The results indicate that under stratified environments, the surface wave-induced magnetic field reaches the peak value of 0.15 nT in the near field but decays rapidly with distance. In contrast, the internal wave-induced magnetic field only has a peak value of 0.006 nT in the near field, and it features stable waveform coherence and slow decay, becoming dominant in the far field. Furthermore, as the submergence depth of the vehicle increases, the contribution of the internal wave grows significantly, reaching 84.9% in the near field at a depth of 50 m. This study reveals that as the key physical quantity for far-field detection, internal waves provide a theoretical basis for the development of long-range non-acoustic detection technologies for undersea vehicles.
