Work

Robust Model Predictive Control With Neural Ordinary Differential Equations for Nonlinear Systems

Year: 2025

Type: article

Abstract: ABSTRACT This article presents a model predictive control (MPC) strategy that leverages neural ordinary differential equations (NODEs) to address the persistent issue of model mismatch in nonlinear systems. A residual model based on NODEs is employed to capture the differences between the actual system dynamics and conventional models. Simultaneously, an approximate model with an uncertainty upper bound, derived from first principles, is introduced. These two models are integrated into a learning‐based MPC framework, effectively decoupling system safety and performance, leading to more reliable control. To enhance model and controller adaptability, we propose an online dynamic learning framework that continuously refines the accuracy of the dynamic model. Compared to traditional learning‐based MPC, the proposed algorithm significantly reduces memory usage. Furthermore, using the theory of invariant sets, we establish the recursive feasibility and stability of the algorithm. The method's effectiveness is demonstrated through addressing nonlinear problems involving modeling errors and stochastic disturbances.

Source: Optimal Control Applications and Methods

DOI: 10.1002/oca.3287

Authors: Xuyu Shen, Zhenzhong Chu, Fei Meng, Xuanyu Hu, Yukun Dang

Institution: University of Shanghai for Science and Technology

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, National Natural Science Foundation of China-Shandong Joint Fund

Awards: G4850547208, G97621961