Adaptive Dynamic Sliding Mode Control of Soft Continuum Manipulators

Abstract

Soft robots are made of compliant and deformable materials and can perform tasks challenging for conventional rigid robots. The inherent compliance of soft robots makes them more suitable and adaptable for interactions with humans and the environment. However, this preeminence comes at a cost: their continuum nature makes it challenging to develop robust model-based control strategies. Specifically, an adaptive control approach addressing this challenge has not yet been applied to physical soft robotic arms. This work presents a reformulation of dynamics for a soft continuum manipulator using the Euler-Lagrange method. The proposed model eliminates the simplifying assumption made in previous works and provides a more accurate description of the robot's inertia. Based on our model, we introduce a task-space adaptive control scheme. This controller is robust against model parameter uncertainties and unknown input disturbances. The controller is implemented on a physical soft continuum arm. A series of experiments were carried out to validate the effectiveness of the controller in task-space trajectory tracking under different payloads. The controller outperforms the state-of-the-art method both in terms of accuracy and robustness. Moreover, the proposed model-based control design is flexible and can be generalized to any continuum robotic arm with an arbitrary number of continuum segments.

A Unified and Modular Model Predictive Control Framework for Soft Continuum Manipulators under Internal and External Constraints

“…Various model-based control methods have been already applied to soft robotic arms, such as the cascaded curvature controller based on Inverse Kinematics [3], the Curvature Dynamic Control [4] and the Sliding Mode Control (SMC) [5]. All those methods have shown promising results, but they suffer some issues.…”