Inertia load change, torque disturbance, and sophisticated mechanical nonlinearity impose great challenge on the high-precision control design of motor servo systems. To achieve better tracking and disturbance rejection performance, a practical control scheme, which combines a fixed-time-convergent generalized extended state observer (FGESO) with a fixed-time-convergent state feedback control law, is proposed in this article. First, an FGESO is designed to estimate the lumped disturbance and the fixed-time convergence of the estimation errors can be achieved. In the FGESO, there are three parameters and the correction term is selected as a continuous switching function of the output estimation error, which can facilitate the tuning process and greatly alleviate the chattering problem. Second, a control law based on the bilimit homogeneity technique is provided to regulate the tracking errors within a finite time independent of the initial condition. The fixed-time stability of the closed-loop system can be rigorously proved based on the Lyapunov theory. To facilitate the application of the proposed method, a practical two-step tuning procedure is established. Finally, extensive simulations and experiments are performed on a brushless direct current motor to validate the effectiveness and robustness of the proposed method.

Considering a class of nonlinear systems subject to actuator saturation and uncertain control input, this article explored the control capability of an extended-state observer-based active disturbance rejection controller in the presence of disturbances and uncertainties. First, the actuator saturation is handled with the convex hull in the extended-state observer-based control scheme, and an ellipsoid invariant set inside the domain of attraction for control systems is obtained using the Lyapunov method. Second, the enlargement problem of the invariant set is also studied, which illustrates that the convex hull of the ellipsoid invariant sets is also an enlargement invariant set. Finally, comprehensive comparisons with existing methods on a brushless dc motor demonstrate the stronger capability of the designed controller on disturbance rejection and actuator saturation. The results show that the disturbance offset and performance index (integrated time and absolute error and integral absolute error) are about half of the classical proportional–integral–derivative(PID) and double-integral sliding-mode control, or the disturbance rejection capability is about twice in this case.

If one cannot obtain the accurate prior information of the controlled object in advance, it will take much effort to get good dynamic performance. To this end, this article proposes an online estimation method to deal with disturbances and uncertainties for a class of systems with uncertain control gain. Based on the extended-state observer scheme, the estimation problem of disturbance and uncertain control gain is transformed to the convex optimization problem and solved by the sign-projected gradient flow, then the estimation can be applied to compensate disturbance and design controller. Moreover, the exponential and ultimate boundedness is obtained for the derived closed-loop system, together with rigorous theoretical analysis and proof. Finally, both the numerical simulations and the experiment on a brushless dc motor demonstrate that the proposed design has a better tracking performance and stronger robustness to deal with disturbances and uncertainties compared with the existing methods.

The statically unstable airbreathing hypersonic vehicle requires a high-bandwidth controller to control the instability, which results in intricate interactions among the aerodynamics, the low-frequency structural modes, and the flight control system. Especially, the structural frequencies are uncertain and time-varying, which further raises the difficulty of controller design. In this paper, two adaptive aeroservoelastic mode stabilization methods are proposed. First, the condition of phase stabilization is given under the linear active disturbance rejection control framework to realize the self-stabilization of the flexible modes. Then, an adaptive notch filter, which employs the lattice type structure and the fast-recursive square root algorithm, is presented. Both methods can achieve the adaptive vibration suppression and extend the control bandwidth. Simulation results demonstrate the effectiveness of the proposed approach.

To enable unmanned aerial vehicles to generate coverage paths that balance inspection quality and efficiency when performing three-dimensional inspection tasks, we propose a quality and efficiency coupled iterative coverage path planning (QECI-CPP) method. First, starting from a cleaned and refined mesh model, this was segmented into narrow and normal spaces, each with distinct constraint settings. During the initialization phase of viewpoint generation, factors such as image resolution and orthogonality degree were considered to enhance the inspection quality along the path. Then, the optimization objective was designed to simultaneously consider inspection quality and efficiency, with the relative importance of these factors adjustable according to specific task requirements. Through iterative adjustments and optimizations, the coverage path was continuously refined. In numerical simulations, the proposed method was compared with three other classic methods, evaluated across five aspects: image resolution, orthogonality degree, path distance, computation time, and total path cost. The comparative simulation results show that the QECI-CPP achieves maximum image resolution and orthogonality degree while maintaining inspection efficiency within a moderate computation time, demonstrating the effectiveness of the proposed method. Additionally, the flexibility of the planned path is validated by adjusting the weight coefficient in the optimized objective function.

In accordance with the PRISMA 2020 guidelines, this systematic review analyzed 73 publications (1997–2025) to summarize advancements in robotic aircraft skin inspection, focusing on the integrated pipeline from data acquisition to defect analysis. The review included studies on Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) for external skin inspection, which present clear technical contributions, while excluding internal inspections and non-technical reports. Literature was retrieved from IEEE conferences, journals, and other academic databases, and key findings were summarized via the categorical analysis of motion planning, perception modules, and defect detection algorithms. Key limitations identified include the fragmentation of core technical modules, unresolved bottlenecks in dynamic environments, challenges in weak-texture and all-weather perception, and a lack of mature integrated systems with practical validation. The study concludes by advocating for future research in multi-robot heterogeneous collaborative systems, intelligent dynamic task scheduling, large model-based airworthiness assessment, and the expansion of inspection scenarios, all aimed at achieving fully autonomous and reliable operations.