Can I read Implicit Regularization and Momentum Algorithms in Nonlinearly Parameterized Adaptive Control and Prediction on EtoBox?

Implicit Regularization and Momentum Algorithms in Nonlinearly Parameterized Adaptive Control and Prediction by Boffi, Nicholas M.; Slotine, Jean-Jacques E. is a scholarly article available to read on EtoBox.

What is Implicit Regularization and Momentum Algorithms in Nonlinearly Parameterized Adaptive Control and Prediction about?

Stable concurrent learning and control of dynamical systems is the subject of adaptive control. Despite being an established field with many practical applications and a rich theory, much of the development in adaptive control for nonlinear systems revolves around a few key algorithms. By exploiting strong connections between classical adaptive nonlinear control techniques and recent progress in optimization and machine learning, we show that there exists considerable untapped potential in algorithm development for both adaptive nonlinear control and adaptive dynamics prediction. We begin by introducing first-order adaptation laws inspired by natural gradient descent and mirror descent. We prove that when there are multiple dynamics consistent with the data, these non-Euclidean adaptation laws implicitly regularize the learned model. Local geometry imposed during learning thus may be used to select parameter vectors -- out of the many that will achieve perfect tracking or prediction -- for desired properties such as sparsity. We apply this result to regularized dynamics predictor and observer design, and as concrete examples, we consider Hamiltonian systems, Lagrangian systems, and

Author

Boffi, Nicholas M.; Slotine, Jean-Jacques E.

Published

2019

Language

EN