drone landing control

This work addresses the challenges of autonomous drone recovery on offshore platforms subjected to wave-induced stochastic motion, time-varying attitudes, and uncertain touchdown conditions. A hierarchical control framework is proposed that decouples high-level vertical landing decisions from low-level flight stabilization. The approach integrates reinforcement learning with conventional control: the upper layer employs a temporal-aware reinforcement learning policy, operating on compact relative observations, to generate a reference vertical velocity; the lower layer ensures precise trajectory tracking. The framework achieves smooth landings without requiring explicit switching logic and demonstrates strong generalization and robustness to unseen wave disturbances in simulation, validating its effectiveness for autonomous maritime drone recovery.

To address insufficient coordination agility of UAV swarms executing time-sensitive tasks in dynamic environments, this paper proposes a distributed game-theoretic coordination framework that tightly integrates lightweight one-dimensional convex game optimization with model predictive control (MPC). Each agent solves, online and solely based on local information, a cost function incorporating temporal and task constraints; the existence and exponential stability of the Nash equilibrium are rigorously proven. The key innovation lies in the first tight coupling of one-dimensional game theory with MPC—achieving low communication overhead and strong robustness against non-ideal conditions such as intermittent communication and tracking errors, while preserving global coordination. Simulations demonstrate a 37% improvement in task success rate and a reduction in response latency to 83 ms. Real-world motion-capture experiments validate centimeter-level cooperative precision under dynamic disturbances up to 3 m/s.

This work addresses the challenge of real-time trajectory planning and tracking for fixed-wing UAVs under wind disturbances, dynamical constraints, and time-varying curvature limits. We propose an online adaptive replanning method grounded in differential flatness. Leveraging the differential flatness property of coordinated flight dynamics, we formulate a compact state representation and generate dynamically feasible trajectories via nonlinear optimization; continuous in-flight replanning ensures adherence to evolving curvature constraints. To our knowledge, this is the first approach that tightly integrates differential-flat modeling with online curvature adaptation, significantly enhancing real-time responsiveness and trajectory fidelity for small-scale fixed-wing platforms operating in uncertain, high-wind environments. Comprehensive simulations and real-world flight experiments validate the method’s capability to robustly synthesize and accurately track dynamically feasible trajectories under complex, time-varying constraints.

This paper addresses the joint optimization of thrust-module spatial configuration and controller design for rigidly coupled multi-UAV cooperative payload transportation, aiming to enhance flight precision and disturbance rejection robustness during payload carriage. Method: We propose an H₂-norm-based robustness metric—first applied to configuration optimization—and develop a unified UAV–payload rigid-body dynamical model. Integrating hierarchical control architecture with optimal control theory, we formulate a co-design framework jointly optimizing module placement and control law. An iterative numerical optimization algorithm is designed to solve for the disturbance-rejection-optimal configuration. Results: Experimental validation demonstrates that the optimized configuration significantly outperforms suboptimal alternatives in disturbance suppression, with measured performance closely matching theoretical predictions—confirming the efficacy and accuracy of the proposed co-design methodology.

This work addresses the challenge of unmanned aerial vehicle (UAV) landing on a heaving maritime platform, where large impact forces and post-impact bouncing—caused by relative vertical motion—often lead to landing failure. To mitigate this, the authors propose a model predictive control (MPC) framework that explicitly incorporates impact dynamics by embedding a rigid-body collision model, based on Newton’s restitution law, into the MPC formulation as a linear complementarity problem (LCP). This novel integration enables explicit prediction of the discontinuous post-collision velocities and active suppression of rebound. Simulation and experimental results demonstrate that the proposed approach significantly reduces the relative velocity prior to touchdown and decreases post-landing deviation by 86.2% compared to conventional tracking MPC, thereby substantially enhancing landing stability and success rate.

This work addresses the challenge of achieving timed, robust dynamic landing of multirotor unmanned aerial vehicles on moving platforms, particularly under time-varying perception quality that compromises landing accuracy and consistency. To this end, the authors propose a fixed-time touchdown cooperative control framework that integrates an adaptive unscented Kalman filter (UKF) to online estimate and update noise statistics, thereby enhancing state estimation robustness. The approach further combines nonlinear model predictive control (NMPC) with a real-time minimum-jerk trajectory planner to guarantee precise touchdown at a predetermined time during the terminal phase, while generating bounded thrust and torque commands under standard tracking assumptions. Simulation and hardware-in-the-loop experiments demonstrate that the proposed method significantly outperforms conventional EKF/UKF-based approaches, achieving superior performance in platform velocity prediction and landing repeatability.

研究解决了无人机在高速敏捷编队飞行时保持阵型的难题，通过提出一种结合模型预测轮廓控制的新方法，并经仿真和实验验证。

This work addresses the challenges of inaccurate 4D trajectory prediction and unstable formation flight in drone swarms operating in complex low-altitude environments, where nonlinear dynamics and stringent real-time requirements pose significant difficulties. To this end, the authors propose a unified closed-loop control framework that integrates an efficient dimension-decoupled trajectory predictor, a diffusion-model-based residual dynamics refinement module to capture temporal dynamic uncertainties, and an uncertainty-aware distributed nonlinear model predictive controller (DNMPC) for robust formation stabilization. Evaluated on a newly constructed synchronized multi-scenario 4D drone swarm dataset, the method achieves an average tracking error below 0.07 meters in complex urban and industrial settings—outperforming existing approaches by 10–15%—while maintaining a real-time inference rate of 34 FPS (latency < 30 ms).

This work addresses the performance gap observed when deploying multi-waypoint drone controllers from simulation to real-world platforms under environmental disturbances. To bridge this sim-to-real discrepancy, the authors propose a hierarchical terminal control architecture that decouples smooth approach trajectory generation, continuous disturbance compensation, and supervised near-target regulation, while separating the core controller structure from platform-specific tuning. The system is validated through a three-stage pipeline—from PyBullet and PX4/Gazebo simulations to physical deployment on a Tello drone—achieving a mean late-phase position error of 0.024 meters under stochastic wind disturbances. Integrating a cascaded flight control stack, Vicon-based high-precision motion capture, and dual evaluation criteria (Strict/Grace), the framework substantially enhances cross-scenario robustness and transfer reliability.

This work addresses the challenge of reference trajectory tracking for quadrotors navigating laterally constrained paths, such as those defined by obstacle boundaries. A novel two-layer control architecture is proposed: at the high level, a Dubins airplane model smooths and decouples the reference trajectory through dimensionality reduction, enabling either offline or receding-horizon optimization; at the low level, a geometric tracking controller based on the full quadrotor dynamics ensures high-precision trajectory following. By synergistically integrating the spatial modeling capability of the Dubins model with geometric control strategies, the approach significantly enhances tracking performance in complex, constrained environments while maintaining computational efficiency. This makes it particularly suitable for autonomous quadrotor flight in narrow or structured spaces.