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.

During constant-force operations in complex marine environments, underwater manipulators are affected by hydrodynamic disturbances and unknown, time-varying environment stiffness. Under classical impedance control (IC), this often leads to large transient contact forces and steady-state force errors, making high-precision compliant control difficult to achieve. To address this issue, this study proposes a Bayesian recursive least-squares-based fuzzy adaptive impedance control (BRLS-FAIC) strategy with displacement correction for underwater manipulators. Within a position-based impedance-control framework, a Bayesian Recursive Least Squares (BRLS) stiffness identifier is constructed by incorporating process and measurement noise into a stochastic regression model, enabling online estimation of the environment stiffness and its covariance under noisy, time-varying conditions. The identified stiffness is used in a displacement-correction law derived from the contact model to update the reference position, thereby removing dependence on the unknown environment location and reducing steady-state force bias. On this basis, a three-input/two-output fuzzy adaptive impedance tuner, driven by the force error, its rate of change, and a stiffness-perception index, adjusts the desired damping and stiffness online under amplitude limitation and first-order filtering. Using an underwater manipulator dynamic model that includes buoyancy and hydrodynamic effects, MATLAB simulations are carried out for step, ramp, and sinusoidal stiffness variations and for planar, inclined, and curved contact scenarios. The results show that, compared with classical IC and fuzzy adaptive impedance control (FAIC), the proposed BRLS-FAIC strategy reduces steady-state force errors, shortens force and position settling times, and suppresses peak contact forces in variable-stiffness underwater environments.

To address the motion control challenges of remotely operated vehicles (ROVs) under model uncertainties, external disturbances, and uncertain hydrodynamic parameters, this study proposes a fractional-order super-twisting sliding mode control (FOST-SMC) strategy based on improved active disturbance rejection control (IADRC). The proposed method reduces dependence on accurate dynamic models and enhances disturbance rejection capability by integrating IADRC with FOST-SMC. A sine-function-based nonlinear extended state observer (ESO) was developed to improve lumped disturbance estimation and noise robustness. The proposed ESO reduces the root mean square (RMS) estimation error from 2.226 × 10⁻⁵ to 5.224 × 10⁻⁶, corresponding to a 76.5% reduction compared with the conventional ESO. Lyapunov analysis verified the stability of the closed-loop system. MATLAB/Simulink version R2024a (MathWorks, Natick, MA, USA) simulations based on the Falcon ROV model demonstrated improved tracking performance under step response, sinusoidal tracking, and three-dimensional trajectory tracking with time-varying disturbances and Gaussian white noise. Compared with conventional active disturbance rejection control (ADRC), the proposed controller achieved average RMSE reductions of 87.0%, 57.2%, and 49.4 to 65.4% in different tracking scenarios, respectively. The proposed strategy provides an effective approach for robust ROV motion control in uncertain underwater environments.