Using stochastic programming to train neural network approximation of nonlinear MPC laws

DOI10.1016/j.automatica.2022.110665zbMath1505.93056OpenAlexW4307241003MaRDI QIDQ2097845

Yun Li, Yankai Cao, Kaixun Hua

Publication date: 14 November 2022

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2022.110665

zbMATH Keywords

stochastic optimizationnonlinear systemsparallel computationmodel predictive controlpolicy learningdeep neural networks

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Stochastic programming (90C15) Nonlinear systems in control theory (93C10) Model predictive control (93B45)

Uses Software

• Ipopt

Cites Work

• Clustering-based preconditioning for stochastic programs
• A receding-horizon regulator for nonlinear systems and a neural approximation
• Research on probabilistic methods for control system design
• Randomized methods for design of uncertain systems: sample complexity and sequential algorithms
• On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming
• Embedded Online Optimization for Model Predictive Control at Megahertz Rates
• An Accelerated Dual Gradient-Projection Algorithm for Embedded Linear Model Predictive Control
• Introduction to Stochastic Programming
• Statistical Learning Theory: Models, Concepts, and Results
• Randomized Strategies for Probabilistic Solutions of Uncertain Feasibility and Optimization Problems
• Model predictive control based on linear programming - the explicit solution
• Ultra-Fast Stabilizing Model Predictive Control via Canonical Piecewise Affine Approximations
• The explicit linear quadratic regulator for constrained systems

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