发表机构
Department of Mechanical Engineering, National Yang Ming Chiao Tung University（国立阳明交通大学机械工程学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究为固定翼无人机引入统一控制框架，采用三相策略，利用UKF融合视觉检测与惯性测量，结合带约束感知的NMPC及BPNG律，实现从目标检测到终端拦截的任务，仿真验证其能稳定跟踪并精确拦截，同时遵守相关约束。
AI 中文摘要
本研究为配备云台（PT）相机的固定翼无人机引入了一个统一控制框架，以执行从初始目标检测到精确终端交战的端到端任务。该系统采用三相策略：基于视觉的目标获取阶段、基于非线性模型预测控制（NMPC）的跟踪阶段和终端制导阶段。跟踪期间，框架使用无迹卡尔曼滤波器（UKF）融合基于YOLO的视觉检测和惯性测量，在未知动力学下实现稳健目标状态估计。为确保可靠视觉接触，引入带约束感知的NMPC策略，结合控制障碍函数（CBF）防止无人机自遮挡。满足终端交战条件时，系统无缝切换至基于四元数的偏置比例导航制导（BPNG）律，强制执行精确碰撞角约束。高保真仿真表明框架实现了稳定、稳健跟踪和精确终端拦截，同时严格遵守飞行器动态极限和相机视野约束。
英文摘要
This study introduces a unified control framework for fixed-wing unmanned aerial vehicles (UAVs) fitted with a pan-tilt (PT) camera, intended to perform an end-to-end mission spanning from initial target detection to accurate terminal engagement. The proposed system employs a three-phase strategy: a vision-based target acquisition phase, an NMPC-based tracking phase, and a terminal guidance phase. During tracking, the framework uses an Unscented Kalman Filter (UKF) to fuse YOLO-based visual detections with inertial measurements, enabling robust target state estimation under unknown dynamics. To ensure reliable visual contact, we introduce a constraint-aware Nonlinear Model Predictive Control (NMPC) strategy that incorporates Control Barrier Functions (CBFs) to explicitly prevent UAV self-occlusion -- a common limitation in fixed-wing tracking. Upon satisfying terminal engagement conditions, the system seamlessly transitions control to a quaternion-based Biased Proportional Navigation Guidance (BPNG) law, enforcing precise impact angle constraints. High-fidelity simulations demonstrate that the framework achieves stable, robust tracking and accurate terminal interception while strictly respecting the vehicle's dynamic limits and camera field-of-view constraints.
