Barrier-Riccati Synthesis for Nonlinear Safe Control with Expanded Region of Attraction

Fuente: arXiv
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Main Authors: Almubarak, Hassan, AL-Sunni, Maitham F., Dubbin, Justin T., Sadegh, Nader, Dolan, John M., Theodorou, Evangelos A.
Format: Preprint
Published: 2025
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author Almubarak, Hassan
AL-Sunni, Maitham F.
Dubbin, Justin T.
Sadegh, Nader
Dolan, John M.
Theodorou, Evangelos A.
author_facet Almubarak, Hassan
AL-Sunni, Maitham F.
Dubbin, Justin T.
Sadegh, Nader
Dolan, John M.
Theodorou, Evangelos A.
contents We present a Riccati-based framework for safety-critical nonlinear control that integrates the barrier states (BaS) methodology with the State-Dependent Riccati Equation (SDRE) approach. The BaS formulation embeds safety constraints into the system dynamics via auxiliary states, enabling safety to be treated as a control objective. To overcome the limited region of attraction in linear BaS controllers, we extend the framework to nonlinear systems using SDRE synthesis applied to the barrier-augmented dynamics and derive a matrix inequality condition that certifies forward invariance of a large region of attraction and guarantees asymptotic safe stabilization. The resulting controller is computed online via pointwise Riccati solutions. We validate the method on an unstable constrained system and cluttered quadrotor navigation tasks, demonstrating improved constraint handling, scalability, and robustness near safety boundaries. This framework offers a principled and computationally tractable solution for synthesizing nonlinear safe feedback in safety-critical environments.
format Preprint
id arxiv_https___arxiv_org_abs_2504_15453
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Barrier-Riccati Synthesis for Nonlinear Safe Control with Expanded Region of Attraction
Almubarak, Hassan
AL-Sunni, Maitham F.
Dubbin, Justin T.
Sadegh, Nader
Dolan, John M.
Theodorou, Evangelos A.
Systems and Control
Robotics
We present a Riccati-based framework for safety-critical nonlinear control that integrates the barrier states (BaS) methodology with the State-Dependent Riccati Equation (SDRE) approach. The BaS formulation embeds safety constraints into the system dynamics via auxiliary states, enabling safety to be treated as a control objective. To overcome the limited region of attraction in linear BaS controllers, we extend the framework to nonlinear systems using SDRE synthesis applied to the barrier-augmented dynamics and derive a matrix inequality condition that certifies forward invariance of a large region of attraction and guarantees asymptotic safe stabilization. The resulting controller is computed online via pointwise Riccati solutions. We validate the method on an unstable constrained system and cluttered quadrotor navigation tasks, demonstrating improved constraint handling, scalability, and robustness near safety boundaries. This framework offers a principled and computationally tractable solution for synthesizing nonlinear safe feedback in safety-critical environments.
title Barrier-Riccati Synthesis for Nonlinear Safe Control with Expanded Region of Attraction
topic Systems and Control
Robotics
url https://arxiv.org/abs/2504.15453