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An explicit model predictive control framework based on physics-informed neural networks
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DOI：10.1016/j.jprocont.2026.103634.png)
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Abstract
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• A PINN-based framework for explicit model predictive control of nonlinear systems. • Loss function enforces physical consistency, tracking, smooth control, and constraints. • Eliminates online optimization for fast real-time control of nonlinear systems. • Validated on SISO and MIMO water tank systems.
Keywords:
Explicit control
Model predictive control
Physics-informed neural networks
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