Aug 2026· Proceedings in Applied Mathematics and Mechanics· Vol 26· 0 citations· 10 references
Abstract
To efficiently compute optimal compressor actions in gas networks, we investigate port‐Hamiltonian models consisting of linear and a nonlinear model assumptions. The control actions are derived via adjoint‐based gradients that incorporate the constraints of the underlying optimization problem. We then present results from the implementation of a model predictive control (MPC) strategy. We compare the results of the optimization on different models and focus on the computational efficiency. These actions are then validated and periodically updated based on a physically detailed nonlinear model, which captures the detailed system dynamics. This design is applied to a daily demand profile in a network with multiple consumers and sources.
This article addresses the control of large-scale district heating networks (DHNs). Traditional nonlinear model predictive control (MPC) suffers from computational intractability due to the nonconvex optimization of complex thermal-hydraulic dynamics. We present scalable predictive control strategies based on the Koopm...
Max Sibeijn, Mohammad Khosravi, S. Pequito et al.· IEEE Transactions on Control...· 0 citations
We provide a framework for robust mean field control problems that describe multi-dimensional optimal liquidation problems under uncertainty from both the underlying stochastic process and the deterministic model parameters. The verification results are established with Hamilton-Jacobi-Bellman-Isaacs (HJBI) equations w...
Huafu Liao, Shuhui Liu, Chen-Chen Mou et al.· 0 citations
This study develops a computational framework for modeling complex population dynamics through advanced algorithmic optimization and numerical simulation techniques. We implement fourth-order Runge-Kutta algorithms to solve nonlinear differential equation systems representing multi-level population networks with three...
Yi-Fei Song, Xin-Y. Fang, Yu-Tong Zhang et al.· International Conference on...· 0 citations
The application and performance of Nonlinear Model Predictive Control (NMPC) are critically limited by the computational efficiency of solving nonlinear combinatorial optimization problems. To address this challenge, this study proposes an efficient optimization strategy that employs a neural network to solve the const...
