Abstract
The sensors of many autonomous systems provide high-dimensional and information-rich measurements. The state of the system is a part of this information, however it is challenging to extract it from such measurements. An autonomous system cannot use traditional feedback control algorithms without knowledge of the state. We propose computational algorithms for the analysis and synthesis of classifier-enabled control architectures. We show how to train classifiers based on criteria that relate to both learning from data and properties of the resulting closed-loop system. The approach to deriving these algorithms involves modeling the resulting closed-loop system as a piecewise affine differential inclusion. The training method is based on the projected gradient descent algorithm. An application of this method to a navigation problem for a mobile robot demonstrates the capabilities of this approach.
| Original language | English |
|---|---|
| Article number | 109509 |
| Journal | Automatica |
| Volume | 128 |
| DOIs | |
| State | Published - Jun 2021 |
Bibliographical note
Funding Information:This research was partially supported by the National Science Foundation Grants 1652113 , CNS-1836900 , and 1646522 , and the University of Kentucky. The material in this paper was partially presented at the 2019 American Control Conference (ACC), July 10–12, 2019, Philadelphia, PA, USA. This paper was recommended for publication in revised form by Associate Editor Adrian George Wills under the direction of Editor Torsten Söderström.
Publisher Copyright:
© 2021 Elsevier Ltd
Keywords
- Classifiers
- Feedback control
- Lyapunov-based methods
- Machine leaning
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering