Changjin Dong

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.

In this paper, a composite control framework integrating feedback linearization, an extended state observer, and an adaptive fractional-order sliding-mode controller is presented for autonomous underwater vehicles operating under uncertain hydrodynamics and external disturbances. The proposed algorithm, named adaptive fractional-order sliding-mode control with extended state observer, aims to enhance trajectory-tracking accuracy, disturbance rejection, and robustness against model uncertainties beyond what is offered by conventional active disturbance rejection control and integer-order sliding-mode control. First, a fractional-order sliding surface with an extended state observer is introduced to estimate and compensate lumped disturbances, where the fractional operator provides intrinsic filtering and memory effects to reduce chattering. Second, an adaptive exponential reaching law with smooth switching is formulated to overcome the trade-off between convergence speed and chattering, and a Levant differentiator is employed for sensorless velocity estimation. Finally, the uniform ultimate boundedness of the closed-loop system is proved via Lyapunov stability theory. Comparative simulation studies on step, sinusoidal, and circular trajectories under external disturbances, measurement noise, and 50% parametric uncertainties demonstrate that the proposed controller achieves zero overshoot, suppresses position fluctuations by 97%, and reduces root mean square tracking errors by 38–70% relative to conventional methods, confirming its superior performance.

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.

With the rapid development of unmanned aerial vehicle (UAV) technology, quadrotor UAVs have demonstrated extensive application potential in various fields. However, due to parameter uncertainties and strong coupling, the flight attitude of quadrotors is prone to external disturbances, posing challenges for achieving precise control and stable flight. In this paper, we address the tracking control problem under unknown command rate variations by proposing a Modified Linear Active Disturbance Rejection Control (LADRC) strategy, aiming to enhance flight stability and anti-disturbance capability in complex environments. First, based on the attitude dynamics model of quadrotors, an LADRC technique is adopted to realize three-channel decoupling-free control. By integrating a parameter estimator, the proposed method can compensate unknown disturbances in real time, thereby improving the system’s anti-disturbance ability and dynamic response performance. Second, to further enhance system robustness, a linear extended state observer (LESO) is designed to accurately estimate the tracking error rate and total disturbances. Additionally, a Levant differentiator is introduced to replace the traditional differentiation component for optimizing the response speed of command rate. Finally, a modified LADRC controller incorporating error rate estimation is constructed. Simulation results validate that the proposed scheme maintains good tracking accuracy under high-frequency disturbances, providing an effective solution for stable UAV flight in complex scenarios. Compared with traditional control methods, the modified LADRC strategy exhibits significant advantages in tracking performance, anti-disturbance capability, and dynamic response. This research not only offers a novel perspective and solution for quadrotor control problems but also holds important implications for improving UAV performance and reliability in practical applications.