Work

Event-triggered adaptive neural network asymptotic tracking control of intelligent vehicles with composite learning

Year: 2025

Type: article

Abstract: Benefitted from composite learning, the proposed scheme takes on the better neural learning and less control conservativeness than the existing adaptive asymptotic control approaches.• This paper opens the avenue to address the high-precision tracking of nonlinear intelligent vehicles by guaranteeing the asymptotic stability.• Taking the advantage of ETC, the proposed scheme separately reduces the communication burden in the different controller-to-actuator channels compared with the continuous control work.

Source: Advanced Equipment

DOI: 10.55092/ae20250002

Authors: Yingjie Deng, Fangcheng Liu, Songtao Wang, Yifei Xu, Tao Ni, Dingxuan Zhao

Institution: Yanshan University

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funder: National Natural Science Foundation of China

Awards: G2064793505, G5290673701, G7955524146