Adaptive Dynamic Sliding Mode Control of Soft Continuum Manipulators

Soft robots are made of compliant materials and perform tasks that are challenging for rigid robots. However, their continuum nature makes it difficult to develop model-based control strategies. This work presents a robust model-based control scheme for soft continuum robots. Our dynamic model is based on the Euler-Lagrange approach, but it uses a more accurate description of the robot's inertia and does not include oversimplified assumptions. Based on this model, we introduce an adaptive sliding mode control scheme, which is robust against model parameter uncertainties and unknown input disturbances. We perform a series of experiments with a physical soft continuum arm to evaluate the effectiveness of our controller at tracking task-space trajectory under different payloads. The tracking performance of the controller is around 38 % more accurate than that of a state-of-the-art controller, i.e., the inverse dynamics method. Moreover, the proposed model-based control design is flexible and can be generalized to any continuum robotic arm with an arbitrary number of segments. With this control strategy, soft robotic object manipulation can become more accurate while remaining robust to disturbances.