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Provably Robust Learning-Based Approach for High-Accuracy Tracking Control of Lagrangian Systems
Inverse dynamics control and feedforward linearization techniques are typically used to convert the complex nonlinear dynamics of Lagrangian systems to a set of decoupled double integrators, and then a standard, outer-loop controller can be used to calculate the commanded acceleration for the linearized system.
An Inversion-Based Learning Approach for Improving Impromptu Trajectory Tracking of Robots with Non-Minimum Phase Dynamics
This paper presents a learning-based approach for impromptu trajectory tracking for non-minimum phase systems, i. e., systems with unstable inverse dynamics.
Multi-Robot Transfer Learning: A Dynamical System Perspective
In this paper, we investigate, through a theoretical study of single-input single-output (SISO) systems, the properties of such optimal transfer maps.
Deep Neural Networks for Improved, Impromptu Trajectory Tracking of Quadrotors
Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making.
