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This paper investigates the formation tracking control problem for multiple unmanned surface vehicles (USVs) under prescribed performance constraints in the presence of model uncertainties and unknown disturbances. A decentralized formation control strategy is developed based on a modified active disturbance rejection control (ADRC) framework, where a model-compensation extended state observer (ESO) is designed to estimate the total disturbance and enhance robustness. To avoid the \"explosion of complexity\", a tracking differentiator (TD) is employed to approximate virtual control derivatives, while a universal barrier function (UBF) is incorporated into the Lyapunov-based synthesis to guarantee both transient and steady-state performance bounds. Rigorous Lyapunov analysis proves that all closed-loop signals remain uniformly ultimately bounded, prescribed performance constraints are strictly satisfied, and inter-agent collision avoidance and communication connectivity are maintained. Comprehensive simulations further demonstrate significant performance advantages over representative baseline methods. In particular, the proposed controller achieves a 57.4% reduction in IAE and a 42.6% reduction in RMSE compared with a PID controller, and a further 49.4% and 36.7% reduction relative to a backstepping controller. These quantitative results confirm the superior accuracy and robustness of the proposed approach.

This paper presents a robust and adaptive visual servoing-based landing control method for unmanned aerial vehicles (UAVs) equipped with a three-axis gimbal camera. To address the limitations of fixed-camera configurations, the proposed approach integrates pixel-level field-of-view (FOV) constraints and leverages the gimbal’s agility for enhanced visual tracking. The landing task is formulated as a constrained image-based control problem, where tracking errors of image features are rigorously bounded using prescribed performance functions. A velocity observer is incorporated to estimate the time-varying motion of the landing platform in real time, enabling accurate autonomous landing without relying on external communication or infrastructure. Lyapunov-based stability analysis confirms the theoretical soundness of the control strategy. Simulation results validate the effectiveness and robustness of the proposed method, demonstrating improved accuracy, adaptability, and practical applicability in UAV landing scenarios.

This paper investigates the formation tracking control problem for multiple unmanned surface vehicles (USVs) under prescribed performance constraints in the presence of model uncertainties and unknown disturbances. A decentralized formation control strategy is developed based on a modified active disturbance rejection control (ADRC) framework, where a model-compensation extended state observer (ESO) is designed to estimate the total disturbance and enhance robustness. To avoid the “explosion of complexity”, a tracking differentiator (TD) is employed to approximate virtual control derivatives, while a universal barrier function (UBF) is incorporated into the Lyapunov-based synthesis to guarantee both transient and steady-state performance bounds. Rigorous Lyapunov analysis proves that all closed-loop signals remain uniformly ultimately bounded, prescribed performance constraints are strictly satisfied, and inter-agent collision avoidance and communication connectivity are maintained. Comprehensive simulations further demonstrate significant performance advantages over representative baseline methods. In particular, the proposed controller achieves a 57.4% reduction in IAE and a 42.6% reduction in RMSE compared with a PID controller, and a further 49.4% and 36.7% reduction relative to a backstepping controller. These quantitative results confirm the superior accuracy and robustness of the proposed approach.