  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.
