为欠驱动软体机器人设计了带稳定性保证的任务空间控制方法
Control Lyapunov Functions for Underactuated Soft Robots
- 通过凸约束优化实现快速指数稳定的控制李雅普诺夫函数
- 在输入受限条件下保持任务空间精度与稳定收敛，优于基线方法
- 适用于从简单手指到高度欠驱动螺旋机器人的多种平台
软体及软硬混合机器人本质上是欠驱动的，且受执行器能力限制，难以实现具有稳定性保证的任务空间控制。现有非线性控制策略（如基于PD控制的方法）通常假设完全驱动且无执行器限制，不适用于此类系统。本文提出一种通用控制框架，用于欠驱动软体机器人在输入受限条件下的任务空间调节与跟踪。该方法将快速指数稳定的控制李雅普诺夫函数作为凸不等式约束，同时满足欠驱动整体动力学和执行器边界。我们在多个逐步增加欠驱动程度的仿真平台上验证：一个简单的双连杆腱驱动“手指”、一个修剪后的螺旋形机械臂，以及一个高度欠驱动的螺旋机器人。与文献中的多种基线方法对比，结果表明该方法在输入受限下实现了更高的任务空间精度和一致的李雅普诺夫收敛，显著提升了稳态点与轨迹跟踪性能。
原文摘要 · Abstract (English)
Soft and soft-rigid hybrid robots are inherently underactuated and operate under tight actuator limits, making task-space control with stability guarantees challenging. Common nonlinear strategies for soft robots (e.g., those based on PD control) often rely on the assumption of full actuation with no actuator limits. This paper presents a general control framework for task-space regulation and tracking of underactuated soft robots under bounded inputs. The method enforces a rapidly exponentially stabilizing control Lyapunov function as a convex inequality constraint while simultaneously satisfying underactuated full-body dynamics and actuator bounds. We validate the approach in simulation on several platforms spanning increasing underactuation: a simple two link tendon-driven "finger", a trimmed helicoid manipulator, and a highly underactuated spiral robot. We compare against a number of baseline methods from the literature. Results show improved task-space accuracy and consistent Lyapunov convergence under input limits, achieving superior set-point and trajectory-tracking performance.
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