A Diffusion-based Generative Approach for Model-free Finite-time Control of Complex Systems
Keywords: Complex Network, Dynamic Control, Generative Model, Diffusion Model, AI for Science
TL;DR: We propose DIFOCON, a data-driven generative framework that leverages diffusion models to achieve finite-time optimal control of complex nonlinear systems without requiring prior knowledge of system parameters or dynamics.
Abstract: Complex systems with nonlinear dynamics pose significant challenges for finite-time optimal control, especially when accurate system models are unavailable. This paper introduces DIFOCON (DIffusion Finite-time Optimal CONtrol), a novel data-driven framework for finite-time optimal control that operates without prior knowledge of system parameters or dynamics. DIFOCON reformulates the control problem as a generative task, optimizing control signal trajectories to guide systems to target states within a finite time. Our approach utilizes a diffusion model with a dual-Unet architecture to capture nonlinear system dynamics and generate entire control sequences in a single step. Additionally, an inverse dynamics module is integrated to ensure that the generated control signals are appropriate for complex systems. To further enhance performance, we propose a retraining strategy that improves out-of-distribution generalization. Experiments on two nonlinear complex systems demonstrate DIFOCON's superior performance, reducing target loss by over 26.9\% and control energy by over 15.8\% compared to baselines while achieving up to 4 times faster convergence in practical steering tasks. The implementation of this work can be found at https://anonymous.4open.science/r/DIFOCON-C019/.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
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Submission Number: 7675
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