基于序列凸规划的非线性模型预测控制用于无人机间对接
Nonlinear Model Predictive Control via Sequential Convex Programming for Drone-to-Drone Docking
- Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究提出基于序列凸规划的非线性模型预测控制框架，用于无人机间对接，在高保真仿真中验证其可应对扰动与估计不确定性，实现可靠安全的对接。
中文摘要 AI 辅助
在扰动驱动的目标运动下，多旋翼飞行器的自主空中对接构成了一个受约束的非线性轨迹优化挑战。本研究将对接任务表述为基于降阶非线性模型的有限时域最优控制问题，该模型加入了扰动状态。所得到的问题在后退时域框架内采用序列凸规划（SCP）求解，以生成动态可行的对接轨迹。研究中融入了带噪声测量的状态估计，以实现鲁棒的相对运动预测，同时在高保真刚体MuJoCo仿真环境中验证了轨迹执行效果。对静止和匀速目标运动下的所提框架进行评估，结果显示其能可靠收敛至对接界面，同时满足几何捕获约束。定量来看，该方法可维持可忽略的对接锥违反情况，且终端状态误差在规定容差内；当对接锥半角低至10度时，可实现一致、安全的对接性能；在标准偏差达0.5的风扰动水平下仍能稳健运行，同时保持有界的接近速度和稳定的控制输入。这些结果证明了基于SCP的轨迹优化框架在估计不确定性下对扰动鲁棒的空中对接中的有效性。
英文摘要
Autonomous mid-air docking of multi-rotor vehicles under disturbance-driven target motion poses a constrained non-linear trajectory optimization challenge. This work formulates the docking task as a finite-horizon optimal control problem based on a reduced-order nonlinear model augmented with disturbance states. The resulting problem is solved using sequential convex programming within a receding-horizon framework to generate dynamically feasible docking trajectories. State estimation with noisy measurements is incorporated to enable robust relative motion prediction, while trajectory execution is validated in a high-fidelity rigid-body MuJoCo simulation environment. The proposed framework is evaluated for stationary and constant-velocity target motions, demonstrating reliable convergence to the docking interface while satisfying geometric capture constraints. Quantitatively, the method maintains negligible docking-cone violations and terminal state errors within prescribed tolerances, and achieves consistent, safe docking performance for cone half-angles as low as 10 degrees. Robust operation is observed for wind disturbance levels up to a standard deviation of 0.5, while preserving bounded approach velocities and stable control effort. These results demonstrate the effectiveness of the SCP-based trajectory optimization framework for disturbance-robust aerial docking under estimation uncertainty.
