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.

翻译：实现精确且平滑的轨迹跟踪能力对于确保涉及机器人操作臂的各种任务成功执行至关重要。现有先进技术需要精确的机器人动力学数学模型，其模型不确定性的鲁棒性依赖于对模型失配的精确边界设定。本文提出一种新颖的自适应鲁棒反馈线性化方案，该方案能够在没有任何先验知识的情况下补偿模型不确定性，并在温和假设条件下提供了收敛性的理论证明。我们在模拟RR机器人上评估了该方法。首先，我们考虑具有已知模型失配的标称模型，这使我们能够将所提策略与当前先进的不确定性感知方法进行比较。其次，我们将所提出的控制律与学习模型结合实施，该学习模型不具备可用的不确定性边界。结果表明，我们的方法在需要较少先验知识的同时，能够达到与不确定性感知方法相当的性能水平。