Modular Adaptive Safety-Critical Control

This paper presents an adaptive control approach for uncertain nonlinear systems subject to safety constraints that allows for modularity in the selection of the parameter estimation algorithm. Such modularity is achieved by unifying the concepts of input-to-state stability (ISS) and input-to-state...

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Bibliographic Details
Published in:Proceedings of the American Control Conference pp. 2969 - 2974
Main Authors: Cohen, Max H., Belta, Calin
Format: Conference Proceeding
Language:English
Published: American Automatic Control Council 31.05.2023
Subjects:
Adaptive control
Estimation
Machine learning
Machine learning algorithms
Nonlinear systems
Parameter estimation
Safety
ISSN:2378-5861
Online Access:Get full text
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Summary:This paper presents an adaptive control approach for uncertain nonlinear systems subject to safety constraints that allows for modularity in the selection of the parameter estimation algorithm. Such modularity is achieved by unifying the concepts of input-to-state stability (ISS) and input-to-state safety (ISSf) via control Lyapunov functions (CLFs) and control barrier functions (CBFs), respectively. In particular, we propose a class of exponential ISS-CLFs and ISSf high order CBFs that can be combined with a general class of parameter estimation algorithms akin to those found in the literature on concurrent learning adaptive control. We demonstrate that the unification of ISS and ISSf in an adaptive control setting allows for maintaining a single set of parameter estimates for both the CLF and CBF that can be generated by a class of update laws satisfying a few general properties. The modularity of our approach is demonstrated via numerical examples by comparing performance in terms of stability and safety across different parameter estimation algorithms.
ISSN:2378-5861
DOI:10.23919/ACC55779.2023.10156396