Open AccessRobust adaptive dynamic surface control of uncertain non‐linear systems with output constraints
Author(s) -
Zhang Zhikai,
Duan Guangren,
Hou Mingzhe
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.0456
Subject(s) - control theory (sociology) , parametric statistics , transformation (genetics) , bounded function , linear system , computer science , stability (learning theory) , scheme (mathematics) , adaptive control , mathematical optimization , robust control , function (biology) , control (management) , control system , mathematics , engineering , mathematical analysis , artificial intelligence , electrical engineering , biochemistry , statistics , chemistry , machine learning , evolutionary biology , biology , gene
This study presents a novel constraints transformation‐based robust adaptive dynamic surface control (DSC) scheme for tracking control of a class of uncertain non‐linear systems with output constraints. The considered systems are in semi‐strict feedback form that contain not only parametric uncertainties, but also general non‐linear function uncertainties as well as unknown input gains. In the proposed approach, a constraints transformation technique is firstly introduced to convert the original constrained system into an equivalent one without constraints. By ensuring the stability of the transformed system, it suffices to guarantee the constraints are not transgressed. To attain this, a decoupled DSC design methodology is then developed to stabilise the transformed system and a systematic design procedure is established. Through rigorous analysis, it is shown that the proposed control scheme can achieve output tracking objective and guarantee that all the closed‐loop signals remain bounded while simultaneously preventing the prescribed output constraints from being violated. Simulation studies illustrate the efficiency and feasibility of the proposed approach.