Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
- Creators
- Tsukamoto, Hiroyasu
-
Chung, Soon-Jo
Abstract
This letter presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM). The NCM uses a deep long short-term memory recurrent neural network for a global approximation of an optimal contraction metric, the existence of which is a necessary and sufficient condition for exponential stability of nonlinear systems. The optimality stems from the fact that the contraction metrics sampled offline are the solutions of a convex optimization problem to minimize an upper bound of the steady-state Euclidean distance between perturbed and unperturbed system trajectories. We demonstrate how to exploit NCMs to design an online optimal estimator and controller for nonlinear systems with bounded disturbances utilizing their duality. The performance of our framework is illustrated through Lorenz oscillator state estimation and spacecraft optimal motion planning problems.
Additional Information
© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. Manuscript received March 17, 2020; revised May 15, 2020; accepted June 4, 2020. Date of publication June 11, 2020; date of current version June 24, 2020. This work was supported in part by the Jet Propulsion Laboratory, California Institute of Technology and in part by the Raytheon Company. Recommended by Senior Editor G. Cherubini.Attached Files
Published - 09115010.pdf
Submitted - 2006.04361.pdf
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Additional details
- Eprint ID
- 104021
- Resolver ID
- CaltechAUTHORS:20200624-155134352
- JPL/Caltech
- Raytheon Company
- Created
-
2020-06-25Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Caltech groups
- GALCIT, Center for Autonomous Systems and Technologies (CAST)