Stable Transformer-Actor-Critic Model Predictive Control: A Contraction Analysis Approach

Abstract:Actor-Critic Model Predictive Control (MPC) effectively addresses complex, non-convex control problems, but guaranteeing the closed-loop stability of sequence-based learning models within these pipelines remains challenging. This paper introduces a novel Transformer-Actor-Critic MPC architecture with formal robustness guarantees. First, we prove that Transformer networks can satisfy global incremental Input-to-State Stability ($\delta$ISS). We then leverage Riemannian contraction theory to analyze the interconnected dynamics between the physical plant and the predictive neural network. Finally, we integrate these theoretical bounds as a training regularizer to yield a certifiably robust policy. The framework is validated on a nonlinear 3D drone model executing target-reaching and obstacle-avoidance maneuvers.

Robotics (cs.RO)

arXiv:2606.20197 [cs.RO]

(or arXiv:2606.20197v3 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.20197 Focus to learn more arXiv-issued DOI via DataCite

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