Work

Event-triggered neural adaptive cooperative control of multiple trains based on minimum learning parameters

Year: 2026

Type: article

Abstract: This paper investigates the challenges of event-triggered trajectory tracking control for multiple high-speed train nonlinear systems subject to model uncertainty. First, a neural network-based minimum learning parameter (MLP) method is proposed for multi-train nonlinear systems with model uncertainty and integrated into the controller design, eliminating conventional neural network weight selection. Second, to reduce the communication resource burden, a fixed-threshold event-triggered mechanism is developed. Finally, an event-triggered adaptive tracking control approach based on the MLP method is presented within the sliding mode control framework. Theoretical analysis demonstrates that all closed-loop control signals are uniformly ultimately bounded and that Zeno behaviour is avoided. Simulation results verify the effectiveness of the proposed control scheme.

Source: Automatika

DOI: 10.1080/00051144.2026.2716986

Authors: Yang Liu, Yehong Chen, Hao Qu, Tingting Zhang, Zhiyong Tan

Institutions: Dalian Jiaotong University, CRRC (China)

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $850

Funders: Department of Science and Technology of Liaoning Province, Scientific Research Fund of Liaoning Provincial Education Department, Key Research and Development Program of Liaoning Province