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

• URL: http://arxiv.org/abs/2606.20197v2
• Date: Mon, 22 Jun 2026 09:17:46 GMT
• Title: Stable Transformer-Actor-Critic Model Predictive Control: A Contraction Analysis Approach
• Abstract summary: Actor-Critic Predictive Control (MPC) effectively addresses complex, non-reaching control problems.<n>We introduce a novel Transformer-Actor-Critic MPC architecture with formal robustness architecture.
• Score: 1.7625675658723843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 ($δ$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.

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