Control Lyapunov Functions for Underactuated Soft Robots

2026-03-05

AI Summary

This work addresses the challenge of achieving stable task-space control in underactuated soft robots under actuator constraints by proposing a unified control framework. It introduces, for the first time, a rapidly exponentially stabilizing control Lyapunov function formulated as a convex inequality and embedded directly into the optimization constraints, jointly enforcing underactuated whole-body dynamics and input bounds. The approach guarantees Lyapunov stability while enabling high-precision task-space regulation and trajectory tracking. Experimental validation across multiple underactuated soft robotic platforms demonstrates its effectiveness, showing significant improvements over baseline methods in both tracking accuracy and convergence performance.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• underactuated
• soft robots
• task-space control
• actuator limits
• stability guarantees

Innovation

• Control Lyapunov Function
• underactuated soft robots
• bounded inputs
• task-space control
• convex constraint