Model-free kinematic control for robotic systems

We propose a new model-free approach for kinematic robot control, where both the model and its parameters are partially unknown, which is inspired by the model-free plant tuning framework. The proposed method only relies on the assumption that the relationship between control inputs and outputs is a smooth and static unknown function, whose partial derivatives have lower and upper bounds that are approximately known. Notably, our approach does not require a learning phase, and it is flexible enough to be applied to a wide class of robotic structures. To showcase the methodology, two distinct types of robotic challenges are considered: the control of a family of cable-driven parallel robots and the control of a tendon-driven soft robot. We devise a task-independent approach to synthesize controllers that enable robots to achieve their goals, with minimum prior knowledge of the nonlinear system. Experimental results are presented for the cable-driven parallel robot, while simulations are conducted for the soft robot case.