Dexterous hands must simultaneously track precise finger trajectories and maintain safe, compliant contact -- objectives in tension for any fixed-gain controller. We present an actuator-agnostic Impedance Model Predictive Control (Impedance MPC) framework for dexterous fingers, instantiating the constant-$A_d$ offset-free architecture established for physical human-robot interaction (pHRI); its stability, recursive-feasibility, and input-to-state-stability guarantees are inherited by preserving the architectural assumptions. An algebraic feedforward reduces the tendon transmission -- hydraulic, cable, pneumatic, twisted-string, or series-elastic -- to a constant-coefficient double integrator, so the QP cost inverse is precomputed offline and a 10-step receding-horizon quadratic program runs at 500\,Hz while enforcing hard constraints on contact force (ISO/TS 15066), actuation limits, and jerk. An encoder-only augmented-Kalman disturbance state drives steady-state error to zero under any constant contact load. On a hydraulically actuated finger -- the worked example platform, adding pressure and cavitation constraints -- the 500\,Hz Kalman MPC attains 0.5\,mrad RMS, 0.1\,mrad steady-state, and 6.6\,mrad peak deflection under 1.5\,Nm contact: 183$\times$, 1500$\times$, and 23$\times$ better than classical impedance. The realized first-move stiffness (18$\to$323\,Nm/rad with update rate) is independently verified. The architecture scales to a 16-DOF LEAP Hand MuJoCo simulation, recovering from 2.5\,N grasp-load disturbances within 0.7\,s.

翻译：灵巧手必须同时跟踪精确的手指轨迹并维持安全柔顺的接触——这对于任何固定增益控制器而言是相互矛盾的目标。我们提出了一种适用于灵巧手指的、与执行器无关的阻抗模型预测控制（Impedance MPC）框架，其具体实现了为人机物理交互（pHRI）建立的恒定$A_d$无偏移架构；通过保留架构假设，该框架继承了稳定性、递归可行性和输入-状态稳定性保证。代数前馈将腱驱动传递——包括液压、线缆、气动、扭绳或串联弹性系统——简化为常系数双积分器，因此二次规划（QP）代价函数的逆矩阵可离线预计算，一个10步滚动时域二次规划以500 Hz频率运行，同时强制执行接触力（ISO/TS 15066）、驱动极限和加加速度的硬约束。仅基于编码器的增广卡尔曼扰动状态可在任何恒定接触负载下将稳态误差驱动至零。在一个液压驱动手指（作为示例验证平台，增加了压力和空化约束）上，500 Hz的卡尔曼MPC在1.5 Nm接触负载下实现了0.5 mrad均方根误差、0.1 mrad稳态误差和6.6 mrad峰值偏转：分别比经典阻抗控制提升了183倍、1500倍和23倍。首次动作刚度（随更新速率从18 Nm/rad变化至323 Nm/rad）得到了独立验证。该架构可扩展至16自由度LEAP Hand MuJoCo仿真，能在0.7秒内从2.5 N抓取负载扰动中恢复。