用神经网络+滑模控制，让假手手腕更快更省力地运动
A Hybrid Model-based and Data-based Approach Developed for a Prosthetic Hand Wrist
- 用神经网络算弯曲角度，滑模控制器调腱绳力
- 仿真与实验均显示响应快、计算负担小
- 适合想提升假手灵活性的研究者或工程师
将先进控制算法融入假手可显著提升其模仿人手复杂动作的能力。本文针对名为‘PRISMA HAND II’的腱驱动软连续腕部，提出一种融合人工神经网络（ANN）与滑模控制（SMC）的模型-数据混合控制器。基于分段恒曲率（PCC）假设建立腕部运动学与动力学模型，利用ANN计算弯曲角度，再由SMC调节腱绳张力，实现快速动态响应并降低计算开销。论文对比了该控制器与其他控制策略在相同腕部上的表现，并通过仿真与实物实验验证了其有效性。
原文摘要 · Abstract (English)
The incorporation of advanced control algorithms into prosthetic hands significantly enhances their ability to replicate the intricate motions of a human hand. This work introduces a model-based controller that combines an Artificial Neural Network (ANN) approach with a Sliding Mode Controller (SMC) designed for a tendon-driven soft continuum wrist integrated into a prosthetic hand known as "PRISMA HAND II". Our research focuses on developing a controller that provides a fast dynamic response with reduced computational effort during wrist motions. The proposed controller consists of an ANN for computing bending angles together with an SMC to regulate tendon forces. Kinematic and dynamic models of the wrist are formulated using the Piece-wise Constant Curvature (PCC) hypothesis. The performance of the proposed controller is compared with other control strategies developed for the same wrist. Simulation studies and experimental validations of the fabricated wrist using the controller are included in the paper.
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