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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2503.00529 |
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- In this paper, we propose a method to solve nonlinear optimal control problems (OCPs) with constrained control input in real-time using neural networks (NNs). We introduce what we have termed co-state Neural Network (CoNN) that learns the mapping from any given state value to its corresponding optimal co-state trajectory based on the Pontryagin's Minimum (Maximum) Principle (PMP). In essence, the CoNN parameterizes the Two-Point Boundary Value Problem (TPBVP) that results from the PMP for various initial states. The CoNN is trained using data generated from numerical solutions of TPBVPs for unconstrained OCPs to learn the mapping from a state to its corresponding optimal co-state trajectory. For better generalizability, the CoNN is also trained to respect the first-order optimality conditions (system dynamics). The control input constraints are satisfied by solving a quadratic program (QP) given the predicted optimal co-states. We demonstrate the effectiveness of our CoNN-based controller in a feedback scheme for numerical examples with both unconstrained and constrained control input. We also verify that the controller can handle unknown disturbances effectively.