In this study, a backstepping sliding mode control method (NDO-ABSC) based on nonlinear disturbance observer (NDO) and adaptive line-of-sight guidance (ALOS) is proposed to address the three-dimensional path following control problem of underactuated autonomous underwater vehicles (AUVs) in the presence of ocean currents, unmodeled dynamics, and other unknown disturbances. First, the following error equations in the current environment are established in the Serret-Frenet coordinate system. Then, ALOS is designed to estimate the variations changes in angle-of-attack and crab angle caused by time-varying currents. In the kinematic controller, a parameter adaptive law, a control law for the virtual target point, and a virtual desired angular velocity are designed by backstepping. Subsequently, the composite uncertainty disturbance is observed and compensated by constructing a nonlinear disturbance observer, and a dynamic controller is designed by backstepping method, and a sliding mode control term is introduced in the control law to enhance the robustness of the system to the uncertainty disturbance. Finally, the good performance and strong robustness of the proposed method in 3D path following control are verified by numerical simulation.

Underwater mobile acoustic source target localization encounters several challenges, including the unknown propagation speed of the source signal, uncertainty in the observation platform’s position and velocity (i.e., platform systematic errors), and economic costs. This paper proposes a new two-step closed-form localization algorithm that jointly estimates the angle of arrival (AOA), time difference of arrival (TDOA), and frequency difference of arrival (FDOA) to address these challenges. The algorithm initially introduces auxiliary variables to construct pseudo-linear equations to obtain the initial solution. It then exploits the relationship between the unknown and auxiliary variables to derive the exact solution comprising solely the unknown variables. Both theoretical analyses and simulation experiments demonstrate that the proposed method accurately estimates the position, velocity, and speed of the sound source even with an unknown sound speed and platform systematic errors. It achieves asymptotic optimality within a reasonable error range to approach the Cramér–Rao lower bound (CRLB). Furthermore, the algorithm exhibits low complexity, reduces the number of required localization platforms, and decreases the economic costs. Additionally, the simulation experiments validate the effectiveness of the proposed localization method across various scenarios, outperforming other comparative algorithms.

The parameter estimation of multiple frequency-hopping (multiple FH) signals with frequency-switching time is a great challenge under conditions in which the number of signals is unknown. Due to the increasing mobility of devices such as unmanned aerial vehicles (UAVs), speed of parameter estimation is even more demanding. To solve this problem, an algorithm for estimating parameters of multiple FH signals based on compressed spectrum sensing and maximum likelihood (CSML) theory is proposed in this paper. First, the received signal is split into segments of the same length, and the frequencies contained in each segment are extracted using compressed spectrum sensing and kurtosis threshold. Next, the frequencies contained in adjacent segments are compared to find the signal segment in which frequency hopping occurs and its corresponding frequency. Finally, a hopping-time fast estimation algorithm based on the maximum likelihood theory is used to estimate the hopping time. Simulation results show that the algorithm proposed in this paper can estimate the parameters of multiple FH signals quickly and accurately when the number of signals is unknown and that it is equally effective for multiple FH signals with frequency-switching time.

In the marine environment, the motion characteristics of autonomous underwater vehicles (AUVs) are affected by unknown factors such as time-varying currents and waves, which increase the complexity of path following controller design. In order to solve the effects of unknown currents and external disturbances on the path following of AUV, the kinematics and dynamics of AUV in the current environment are firstly modeled, and the following error equations are constructed in the Serret-Frenet coordinate system. The design of the kinematic controller is accomplished based on the line-of-sight and backstepping methods. In terms of the dynamics control, the adaptive law is utilized to estimate the external disturbances and combined with the sliding mode control method to derive the control law. Further, in order to reduce the deviation caused by the current on the AUV navigation, a current observer is designed to estimate the current speed online. Through theoretical analysis, the cascade control system jointly composed of the current observer and the following controller is proved to be globally asymptotically stable. Simulation and comparison results show that the proposed method can effectively control the AUV to track the expected path in the presence of unknown currents and environmental disturbances, proving the effectiveness and robustness of the control strategy.

With the popularity of carbon-free vehicle obstacle avoidance races, the requirements for the accuracy and reliability of vehicle motion control are getting higher and higher. Aiming at the problems of trajectory deviation and debugging difficulties of the carbon-free vehicle during the movement process, the Revolute-Slider-Slider-Revolute (RSSR) mechanism is adopted as the steering device, which is aimed at improving the motion control precision and obstacle avoidance ability of the vehicle. Firstly, from the kinematics point of view, a kinematics model based on the spatial four-link mechanism is established, and the influence of each parameter change on the trajectory of the vehicle is analyzed. On this basis, the influence of each key parameter on the motion process is further explored through practical debugging, so as to derive a general law of the vehicle’s motion under the drive of the RSSR mechanism. Through numerical simulation analysis, the accuracy of the theoretical model is verified, and the structural design of the vehicle is optimized accordingly. The actual debugging results show that the vehicle can realize smooth operation, which fully proves the practicality and effectiveness of the established mathematical model and the research of the RSSR mechanism.