Computationally Inexpensive Robust Adaptive Control of Nonlinear Systems

Yongcheng ZHOU, Mingliang ZHOU, Kai ZHAO, Long CHEN

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review


For the parametric strict-feedback nonlinear systems in the literature, as the number/dimension of the system parameters increases, a large number of adaptive laws must be designed, which sharply consumes the available computational resource. Hence, reducing the number of parameters to be estimated is one of the effective ways for handling such problem. In this paper, a single-parameter-estimation based robust adaptive control scheme is proposed for a class of parametric strict-feedback nonlinear systems. By giving the concept of the maximum value of the norm of the system parameters and estimating such scalar constant (rather than the system parameters themselves), the developed control ensures that only one parameter needs to be updated online, making the control implementation inexpensive in computation. A simulation example is presented to demonstrate the effectiveness of the proposed scheme.

Original languageEnglish
Title of host publicationProceedings of the 39th Chinese Control Conference, CCC 2020
EditorsJun FU, Jian SUN
Number of pages5
ISBN (Electronic)9789881563903
Publication statusPublished - Jul 2020
Externally publishedYes
Event39th Chinese Control Conference, CCC 2020 - Shenyang, China
Duration: 27 Jul 202029 Jul 2020

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927


Conference39th Chinese Control Conference, CCC 2020

Bibliographical note

Publisher Copyright:
© 2020 Technical Committee on Control Theory, Chinese Association of Automation.


  • Backstepping
  • Nonlinear systems
  • One parameter estimation
  • Robust adaptive control


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