无需建模，通过张力优化实现空间连续机械臂的高精度实时控制。
Model-Less Feedback Control of Space-based Continuum Manipulators using Backbone Tension Optimization
- 用经验初始化雅可比矩阵，在线通过凸优化更新，避免依赖动力学模型。
- 在圆形、五边形、方形轨迹上实现亚毫米级稳态精度，无需参数标定。
- 适用于空间受限环境中的高可靠性连续机械臂控制，适合航天任务应用。
连续机械臂在狭小且障碍物密集的环境中具有固有的灵活性和安全的几何顺应性。然而，其无限维的主干变形、未建模的内部摩擦以及配置相关的刚度，从根本上限制了基于模型的运动学公式可靠性，导致雅可比预测不准确、人为奇点和不稳定的驱动行为。为克服这些局限，本文提出一个完整的无模型控制框架：通过经验初始化的雅可比矩阵，结合在线微分凸更新，绕过运动学建模。末端运动通过实时二次规划生成，计算执行器增量，同时避免腱松弛并满足几何约束。本文引入主干张力优化项以调节轴向载荷并抑制共激活压缩。该框架在圆形、五边形和方形轨迹上进行了验证，展现出平滑收敛、稳定张力演化和亚毫米级稳态精度，且无需任何模型校准或参数识别。结果表明，该控制器是受限环境下模型依赖型连续操作的一种可扩展替代方案。
原文摘要 · Abstract (English)
Continuum manipulators offer intrinsic dexterity and safe geometric compliance for navigation within confined and obstacle-rich environments. However, their infinite-dimensional backbone deformation, unmodeled internal friction, and configuration-dependent stiffness fundamentally limit the reliability of model-based kinematic formulations, resulting in inaccurate Jacobian predictions, artificial singularities, and unstable actuation behavior. Motivated by these limitations, this work presents a complete model-less control framework that bypasses kinematic modeling by using an empirically initialized Jacobian refined online through differential convex updates. Tip motion is generated via a real-time quadratic program that computes actuator increments while enforcing tendon slack avoidance and geometric limits. A backbone tension optimization term is introduced in this paper to regulate axial loading and suppress co-activation compression. The framework is validated across circular, pentagonal, and square trajectories, demonstrating smooth convergence, stable tension evolution, and sub-millimeter steady-state accuracy without any model calibration or parameter identification. These results establish the proposed controller as a scalable alternative to model-dependent continuum manipulation in a constrained environment.
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