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Computer Science > Computer Vision and Pattern Recognition
Title: Manifold-Guided Lyapunov Control with Diffusion Models
(Submitted on 26 Mar 2024)
Abstract: This paper presents a novel approach to generating stabilizing controllers for a large class of dynamical systems using diffusion models. The core objective is to develop stabilizing control functions by identifying the closest asymptotically stable vector field relative to a predetermined manifold and adjusting the control function based on this finding. To achieve this, we employ a diffusion model trained on pairs consisting of asymptotically stable vector fields and their corresponding Lyapunov functions. Our numerical results demonstrate that this pre-trained model can achieve stabilization over previously unseen systems efficiently and rapidly, showcasing the potential of our approach in fast zero-shot control and generalizability.
Submission history
From: Amartya Mukherjee [view email][v1] Tue, 26 Mar 2024 13:33:16 GMT (2920kb,D)
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