K-filter observer based optimal robust guarantee cost control for uncertain nonlinear servo system with immeasurable states

Minlin Wang, Xueming Dong, Xuemei Ren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a K-filter observer based optimal robust guarantee cost controller is proposed for uncertain nonlinear servo system with immeasurable states to achieve the precision load tracking. By using a K-filter observer to estimate both the system states and the nonlinearities, an adaptive dynamic surface control is designed to make the system output track a given reference command. However, since the parameter uncertainties may influence the system tracking performance, an optimal robust guarantee cost control is developed to combine with the adaptive dynamic surface control to stabilize the uncertain system and guarantee the system cost function less than or equal to a given upper bound. The Lyapunov theory proves the stability of the closed-loop control system. Simulation results based on a servo system are conducted to illustrate the efficiency of the proposed control scheme.

Original languageEnglish
Title of host publicationProceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages420-425
Number of pages6
ISBN (Electronic)9781728114545
DOIs
Publication statusPublished - May 2019
Externally publishedYes
Event8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019 - Dali, China
Duration: 24 May 201927 May 2019

Publication series

NameProceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019

Conference

Conference8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019
Country/TerritoryChina
CityDali
Period24/05/1927/05/19

Keywords

  • Adaptive dynamic surface control
  • K-filter observer
  • Optimal robust guarantee cost control
  • Uncertain nonlinear servo system

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