EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems
Robotics
2023-03-06 v1 Systems and Control
Systems and Control
Abstract
This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation.
Cite
@article{arxiv.2303.01705,
title = {EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems},
author = {Andre Coelho and Alin Albu-Schaeffer and Arne Sachtler and Hrishik Mishra and Davide Bicego and Christian Ott and Antonio Franchi},
journal= {arXiv preprint arXiv:2303.01705},
year = {2023}
}