  The ability to achieve precise and smooth trajectory tracking is crucial for
ensuring the successful execution of various tasks involving robotic
manipulators. State-of-the-art techniques require accurate mathematical models
of the robot dynamics, and robustness to model uncertainties is achieved by
relying on precise bounds on the model mismatch. In this paper, we propose a
novel adaptive robust feedback linearization scheme able to compensate for
model uncertainties without any a-priori knowledge on them, and we provide a
theoretical proof of convergence under mild assumptions. We evaluate the method
on a simulated RR robot. First, we consider a nominal model with known model
mismatch, which allows us to compare our strategy with state-of-the-art
uncertainty-aware methods. Second, we implement the proposed control law in
combination with a learned model, for which uncertainty bounds are not
available. Results show that our method leads to performance comparable to
uncertainty-aware methods while requiring less prior knowledge.
