Aimed at the stability problem of quadrotor Unmanned Aerial Vehicle (UAV) flight attitudes under random airflow disturbance conditions, a robust H-infinity-based dual cascade Model Predictive Control (MPC) attitude control method is proposed. Model Predictive Control itself has the capability to minimize the deviation between the prediction error and the control target by optimizing the control algorithm. The robust H-infinity controller can maintain stability in the face of system model uncertainty and external disturbances. The controller designed in this paper divides the attitude control loop into the following two parts: the angle loop and the angular velocity loop. The angle loop, serving as the main control loop of the attitude control, employs the robust H-infinity controller to process the angle of the quadrotor UAV and then transmits the processed value to the MPC controller. This approach reduces the computational load of the MPC controller. Simultaneously, by optimizing the algorithm, MPC minimizes the prediction error and the deviation from the control target. Simulation experiments confirm that the proposed algorithm improves the stability of the UAV attitude under random airflow disturbance conditions, while also achieving accurate tracking of the UAV’s position.

Autonomous underwater vehicle is an effective tool for humans to explore the ocean. It can be used for the monitoring of underwater structures and facilities, which puts forward more accurate and stable requirements for the system operation of the autonomous underwater vehicle. This paper studies the system and structural design, including the parameter identification design and control system design, of a novel autonomous underwater vehicle called “Arctic AUV”. The dynamic mathematical model of the “Arctic AUV” was established, and the system parameter identification method based on the multi-sensor least squares centralized fusion algorithm was proposed. The simplification of the mathematical model of the robot was theoretically derived, and the online parameter identification and motion control were combined, so that the robot could cope with the influence of the arctic water velocity and external turbulence. Based on the hybrid control scheme of adaptive PID and predictive control, the accurate motion control of the “Arctic AUV” was realized. A prototype of “Arctic AUV” was developed, and system parameter identification experiments were carried out in indoor pool water. Hybrid adaptive and predictive control experiments were also carried out. The validity of the parametric design method in this paper was verified, and by comparative experiment, the effect of the control method proposed in this paper was better than the traditional method.

The quest for efficient and safe trajectory planning in robotic manipulation poses significant challenges, particularly in complex obstacle environments where the risk of encountering singularities and obstacles is high. Addressing this critical issue, our study presents a novel enhancement of the Fast Marching Tree (FMT) algorithm, ingeniously designed to navigate the complex terrain of Cartesian space with an unprecedented level of finesse. At the heart of our approach lies a sophisticated two-stage path point sampling strategy, ingeniously coupled with a singularity avoidance mechanism that leverages geometric perception to assess and mitigate the risk of encountering problematic configurations. This innovative method not only facilitates seamless obstacle navigation but also adeptly circumvents the perilous zones of singularity, ensuring a smooth and uninterrupted path for the robotic arm. To further refine the trajectory, we incorporate a quasi-uniform cubic B-spline curve, optimizing the path for both efficiency and smoothness. Our comprehensive simulation experiments underscore the superiority of our algorithm, showcasing its ability to consistently achieve shorter, more efficient paths while steadfastly avoiding obstacles and singularities. The practical applicability of our method is further corroborated through successful implementation in real-world robotic arm trajectory planning scenarios, highlighting its potential to revolutionize the field with its robustness and adaptability.

In response to the limitations of vibration suppression performance caused by the difficulty in accurately modeling nonlinear friction during robotic manipulator dynamics parameter identification, this paper proposes a hybrid identification method based on a Broad Learning System (BLS) optimized by Particle Swarm Optimization (PSO). First, a joint excitation trajectory is designed using a fifth-order Fourier series with zero boundary conditions to ensure sufficient excitation of system dynamics. Then, a linear regression formulation of the manipulator’s structural dynamics is established, and the BLS network is employed to model the unstructured residuals—primarily arising from nonlinear friction—with high precision. Finally, the PSO algorithm is applied to optimize the hyperparameters of the BLS network, achieving global model optimality. Simulation results demonstrate that under typical motion conditions of the manipulator, the proposed method exhibits excellent capability in capturing nonlinear disturbances, maintaining joint prediction errors below 6 × 10⁻¹² N·m. This significantly improves the accuracy and robustness of the feedforward vibration suppression control. Moreover, by integrating PSO-based hyperparameter optimization and trajectory design with sufficient excitation, the proposed method enhances data efficiency during the identification process, offering a novel and practical identification strategy for precise modeling and control of complex mechanical systems.

In response to the inherent nonlinearity and complex coupling of quadrotor UAV systems, as well as the challenge of maintaining a stable flight attitude under external disturbances, this paper proposes a UAV pose control method based on a fusion of Active Disturbance Rejection Control (ADRC) and Super-Twisting Sliding Mode Control (ST-SMC). By combining the strengths of ADRC and the super-twisting sliding mode algorithm, this approach achieves complementary performance—enhancing the system’s disturbance rejection capability and response speed while effectively mitigating the high-frequency chattering problem commonly caused by switching functions in traditional sliding mode control. Under random airflow disturbances, the designed fusion algorithm leverages the dynamic compensation characteristics of ADRC to stabilize external perturbations, while the robustness of ST-SMC suppresses the effects of system nonlinearities and uncertainties on control accuracy. Finally, MATLAB simulation experiments validate the effectiveness of this method, showing significantly better performance in terms of response speed, overshoot, and settling time compared to traditional control algorithms. This approach greatly improves the UAV’s pose stability and self-balancing capability in complex environments, ensuring strong dynamic and static control performance under random disturbances while maintaining high real-time performance and control efficiency.

This study proposes a hierarchical composite adaptive sliding-mode control strategy to address the strong nonlinear dynamics of a hexarotor Unmanned Aerial Vehicle (UAV) and the external disturbances encountered during flight. First, within the position-control loop, a Terminal Sliding Mode Control (TSMC) is designed to guarantee finite-time convergence of the system states, thereby significantly improving the UAV’s rapid response to complex trajectories. Concurrently, an online Adaptive rates mechanism is introduced to estimate and compensate unknown disturbances and modeling uncertainties in real time, further enhancing disturbance rejection. In the attitude-control loop, a Super-twisting Sliding Mode Control (STSMC) method is employed, where an Adaptive rate law dynamically adjusts the sliding gain to prevent overestimation and high-frequency chattering, while ensuring fast convergence and smooth response. To comprehensively validate the feasibility and superiority of the proposed scheme, a representative helical trajectory-tracking experiment was conducted and systematically compared, via simulation, against conventional control methods. Experimental results demonstrate that the proposed approach achieves stable control within 0.15 s, with maximum position and attitude tracking errors of 0.05 m and 0.15°, respectively. Moreover, it exhibits enhanced robustness and adaptability to external disturbances and parameter uncertainties, effectively improving the motion-control performance of hexacopter UAVs in complex missions.