Work

Hierarchical optimization-based online learning control for nonlinear systems with unknown disturbances

Year: 2025

Type: article

Abstract: A hierarchical optimization-based online learning control scheme is presented to exploit the learning control performance of neural networks (NNs) for nonlinear systems with unknown disturbances. First, leveraging the equivalent-input-disturbance (EID) concept and using only the system output, an NN-assisted control structure is devised. Then, a two-stage learning control framework is developed. Specifically, the first rough stage is for fast dynamic disturbance-rejection control with exponential convergence performance and then the second refined stage is to further refine the performance by online optimization. Further, the closed-loop performance with integrated disturbance-rejection performance is analyzed and a performance-oriented design algorithm is presented. Finally, superiorities of the developed scheme are shown by comparisons of rotary inverted pendulum experiments with other methods.

Source: Automatica

DOI: 10.1016/j.automatica.2025.112744

Authors: Pan Yu, Qiang Wu, Kang-Zhi Liu

Institutions: Beijing University of Technology, Chiba University

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,760

Funders: National Natural Science Foundation of China, Beijing Municipal Education Commission, Natural Science Foundation of Beijing Municipality, Beijing Association for Science and Technology