Stable Open Loop Control of Soft Robotic Manipulators

Abstract

Dynamic control of soft robotic manipulators is a challenging field still in its nascent stages. Modeling is still a major hurdle due to its high dimensional nonlinear dynamic properties. Even if accurate models of these high dimensional nonlinear systems are available, the computational burden of the models or sensory requirements poses problems for robust and stable control. This letter addresses these two problems by using a data-driven model that uses only mechanical feedback for stable control of the soft robotic manipulator. Using a learning-based open loop dynamic controller, we investigate the self-stabilizing behavior that can be obtained from the complex dynamics of a soft manipulator. Experimental findings illustrate the advantage of using open-loop dynamic controllers for accurate self-stabilizing control of soft robotic manipulators without any sensory feedback.