The conventional limitations of the robotic actuation mechanisms have led to many researchers needing to explore novel biomimetic motor mechanisms as the antagonistic human motor system. In this way, it is of interest to understand the inherent adaptive stiffness, or compliance, and modulation, in different alternative actuation architectures such as the antagonistic bi-articular (AbA) system. These novel AbA actuation mechanisms are characterized by resembling the efficient tendon and muscle build-up over our skeletal structure. In this paper, we propose a Cartesian neuro-controller for a robot manipulator actuated by a simplified adaptive viscoelastic linear AbA system. It is shown that the adaptive closed-loop system enforces terminal attractors, induced by a continuous model-free sliding mode control, simultaneously with a learning algorithm to compensate parametric uncertainties of AbA system through a low dimensional neural network. Numerical simulation results exhibit the feasibility of this approach.

中文翻译：

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基于终端吸引子的机械臂拮抗双关节肌肉驱动神经控制器

机器人驱动机制的传统局限性导致许多研究人员需要探索新的仿生运动机制作为对抗性人类运动系统。通过这种方式，了解不同替代驱动架构（例如拮抗双关节 (AbA) 系统）中的固有自适应刚度、顺应性和调制是很有趣的。这些新颖的 AbA 驱动机制的特点是类似于我们骨骼结构上有效的肌腱和肌肉堆积。在本文中，我们为由简化的自适应粘弹性线性 AbA 系统驱动的机器人操纵器提出了笛卡尔神经控制器。结果表明，自适应闭环系统强制终端吸引子，由连续无模型滑模控制引起，同时与学习算法通过低维神经网络补偿 AbA 系统的参数不确定性。数值模拟结果证明了该方法的可行性。