用自适应权重的非线性模型预测控制，提升双臂机器人抓取高速移动物体的稳定性与效率。

Catching fast-moving objects serves as a benchmark for robotic agility, posing significant coordination challenges for cooperative manipulator systems holding a catcher, particularly due to inherent closed-chain constraints. This paper presents a nonlinear model predictive control (MPC)-based motion planner that bridges high-level interception planning with real-time joint space control, enabling dynamic object interception for systems comprising two cooperating arms. We introduce an Adaptive- Terminal (AT) MPC formulation featuring cost shaping, which contrasts with a simpler Primitive-Terminal (PT) approach relying heavily on terminal penalties for rapid convergence. The proposed AT formulation is shown to effectively mitigate issues related to actuator power limit violations frequently encountered with the PT strategy, yielding trajectories and significantly reduced control effort. Experimental results on a robotic platform with two cooperative arms, demonstrating excellent real time performance, with an average planner cycle computation time of approximately 19 ms-less than half the 40 ms system sampling time. These results indicate that the AT formulation achieves significantly improved motion quality and robustness with minimal computational overhead compared to the PT baseline, making it well-suited for dynamic, cooperative interception tasks.