用模型预测控制优化假手手腕运动，更自然灵活。
Optimizing Prosthetic Wrist Movement: A Model Predictive Control Approach
- 采用模型预测控制算法，实时调节软体腕部运动。
- 仿真与实验验证显示动作更精准，提升操作灵巧度。
- 适合假肢研发、康复工程等领域的研究人员参考。
将先进控制策略融入假手对提升其适应性和性能至关重要。本研究提出一种模型预测控制（MPC）策略，用于调控由肌腱驱动的假手所连接的软体连续腕部结构，在降低计算开销的同时实现运动调节。通过欧拉-伯努利梁理论建模运动学，利用拉格朗日方法建立动力学模型。经仿真与实验验证，MPC能有效优化腕部屈伸运动，增强用户控制能力。结果表明该方法显著提升假手灵巧性，使动作更自然、直观。本研究为智能假肢系统的发展提供了可行方向，对机器人学与生物医学工程领域具有重要意义。
原文摘要 · Abstract (English)
The integration of advanced control strategies into prosthetic hands is essential to improve their adaptability and performance. In this study, we present an implementation of a Model Predictive Control (MPC) strategy to regulate the motions of a soft continuum wrist section attached to a tendon-driven prosthetic hand with less computational effort. MPC plays a crucial role in enhancing the functionality and responsiveness of prosthetic hands. By leveraging predictive modeling, this approach enables precise movement adjustments while accounting for dynamic user interactions. This advanced control strategy allows for the anticipation of future movements and adjustments based on the current state of the prosthetic device and the intentions of the user. Kinematic and dynamic modelings are performed using Euler-Bernoulli beam and Lagrange methods respectively. Through simulation and experimental validations, we demonstrate the effectiveness of MPC in optimizing wrist articulation and user control. Our findings suggest that this technique significantly improves the prosthetic hand dexterity, making movements more natural and intuitive. This research contributes to the field of robotics and biomedical engineering by offering a promising direction for intelligent prosthetic systems.
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