Data-Driven Economic Predictive Control for Nonlinear Systems
Research Paper#Control Systems, Machine Learning, Optimization🔬 Research|Analyzed: Jan 3, 2026 19:08•
Published: Dec 29, 2025 03:25
•1 min read
•ArXivAnalysis
This paper presents a novel data-driven control approach for optimizing economic performance in nonlinear systems, addressing the challenges of nonlinearity and constraints. The use of neural networks for lifting and convex optimization for control is a promising combination. The application to industrial case studies strengthens the practical relevance of the work.
Key Takeaways
- •Proposes a data-enabled economic predictive control method for nonlinear systems.
- •Uses neural networks for lifting to approximate nonlinearities.
- •Formulates the control problem as a convex optimization problem.
- •Demonstrates effectiveness through industrial case studies (water treatment, carbon capture).
Reference / Citation
View Original"The online control problem is formulated as a convex optimization problem, despite the nonlinearity of the system dynamics and the original economic cost function."