解决无人机视觉伺服中失控与稳定性问题，提升视觉导航鲁棒性。
Terminal Constraint Model Predictive Control for Image-Based Visual Servoing of UAVs with Kalman Filter-Based Moment Loss Compensation
- 用终端约束模型预测控制，确保在极限条件下仍稳定收敛。
- 结合卡尔曼滤波预测图像矩变化，应对短暂视觉丢失。
- 适合高速飞行中依赖视觉的无人机系统开发与应用。
基于图像的视觉伺服（IBVS）通过直接调节图像空间误差，为无人机提供高效的视觉引导控制范式。然而，传统IBVS控制器面临两大关键问题：接近目标时因输入与状态约束导致闭环稳定性丧失；剧烈运动下基于矩的视觉特征间歇性丢失引发控制失效。本文提出一种融合终端约束模型预测控制（TC-MPC）与卡尔曼滤波（KF）状态预测机制的IBVS框架。TC-MPC显式引入终端状态约束与终端代价项，嵌入到IBVS误差动态中，保证递归可行性、改善收敛行为，并在控制与状态约束下维持闭环稳定。同时，卡尔曼滤波预测短时视觉退化期间图像矩的时序演化，使控制器在部分矩测量缺失时仍保持控制连续性。所提方法通过实时无人机视觉伺服实验验证。
原文摘要 · Abstract (English)
Image-Based Visual Servoing (IBVS) provides an efficient vision-guided control paradigm for unmanned aerial vehicles (UAVs) by directly regulating image-space errors. However, conventional IBVS controllers are vulnerable to two critical issues: loss of closed-loop stability near the target due to input and state constraints, and control failure caused by intermittent loss of moment-based visual features under aggressive motion. To address these challenges, this paper proposes a terminal-constraint model predictive control (TC-MPC) framework for IBVS, integrated with a Kalman filter (KF)-based state-prediction mechanism. The TC-MPC explicitly incorporates terminal-state constraints and a terminal cost into the IBVS error dynamics, ensuring recursive feasibility, improved convergence behavior, and closed-loop stability under control and state constraints. In parallel, the Kalman filter predicts the temporal evolution of image moments during short-term visual degradation, enabling the controller to preserve control continuity when moment measurements are partially unavailable. The proposed approach is validated through real-time UAV visual servoing experiments.
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