3 accepted papers
We introduce $\mathcal{L}_1$-MBRL, a control-theoretic augmentation scheme for Model-Based Reinforcement Learning (MBRL) algorithms. Unlike model-free approaches, MBRL algorithms learn a model of the transition function using data and use it to design a control input. Our approach generates a series…
This paper introduces an LL1adaptive control aug-mentation for geometric tracking control of quadrotors. In the proposed design, the LL 1 augmentation handles nonlinear (time-and state-dependent) uncertainties in the quadrotor dynamics without assuming or enforcing parametric structures, while the b…
This letter presents an approach to guaranteed trajectory tracking for nonlinear control-affine systems subject to external disturbances based on robust control contraction metrics (CCM) that aims to minimize the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.