Work

A novel observer-based neural-network finite-time output control for high-order uncertain nonlinear systems

Year: 2024

Type: article

Abstract: Due to the difficulty encountered in dealing with unstructured system dynamics with unmeasured system state variables, this paper presents a novel observer-based neural network finite-time output control strategy for general high-order nonlinear systems (HNSs). The suggested technique is performed based on the backstepping-like control (BSC) scheme with a hybrid nonlinear disturbance-state observer and norm estimation-based radial basis function neural network (RBFNN). This helps not only reduce the number of estimated parameters but also relax the restriction of using inequality when exploiting the norm estimation concept in a conventional way; thus, retaining the same properties of the original system. Therefore, an observer-based finite-time output feedback control is established to deal with the unstructured dynamical behaviors and satisfying the output tracking regulation with the semi-global practically finite-time stability (SGPFS) guaranteed for the closed-loop system. The effectiveness and workability of the proposed algorithm is verified by a numerical simulation on a specific practical application.

Source: Applied Mathematics and Computation

DOI: 10.1016/j.amc.2024.128699

Authors: Hoai Vu Anh Truong, Van Du Phan, Duc Thien Tran, Kyoung Kwan Ahn

Institutions: Pohang University of Science and Technology, Vinh University, Ho Chi Minh City University of Technology and Engineering, University of Ulsan

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: $3,370

Funders: Ministry of Education - Singapore, Ministry of Education, National Research Foundation of Korea