Adaptive Robust Controller for handling Unknown Uncertainty of Robotic Manipulators

TL;DR

This paper introduces an adaptive robust control scheme for robotic manipulators that compensates for unknown uncertainties without prior bounds, ensuring precise trajectory tracking with less prior knowledge.

Contribution

A novel adaptive robust feedback linearization method that does not require prior uncertainty bounds, with proven convergence and effective performance in simulations.

Findings

Performance comparable to uncertainty-aware methods

Requires less prior knowledge about uncertainties

Effective in simulated robotic manipulator tasks

Abstract

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.