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.

翻译：将先进控制算法融入假肢手可显著增强其复现人手精细运动的能力。本研究提出一种基于模型的控制器，该控制器将人工神经网络方法与滑模控制器相结合，专为集成于名为"PRISMA HAND II"假肢手的肌腱驱动软体连续体手腕而设计。我们的研究重点在于开发一种能在手腕运动过程中以较低计算量实现快速动态响应的控制器。所提出的控制器包含用于计算弯曲角度的人工神经网络以及用于调节肌腱力的滑模控制器。基于分段常曲率假设建立了手腕的运动学和动力学模型。本文将该控制器的性能与针对同一手腕开发的其他控制策略进行了对比，并包含了采用该控制器的制造手腕的仿真研究及实验验证。