Virtual unmodeled dynamics modeling for nonlinear multivariable adaptive control with decoupling design

Yajun Zhang, Tianyou Chai, Dianhui Wang, Xinkai Chen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

For a class of complex industrial processes with nonlinear, strongly coupled multivariable properties, a new multivariable decoupling design framework which based on the concepts of virtual unmodeled dynamics (VUD) and lower order linear models is proposed in this paper. First, a selftuning multivariable decoupling controller is constructed based on a lower order model. Then based on the compensator of the VUD, a nonlinear multivariable decoupling controller is designed, where a decomposition estimation algorithm is employed for modeling the VUD. In our proposed scheme, it solves the problem that the current input signal is embedded in the VUD and the true input data vector used by the learner model is difficult to be obtained in time. The linear and nonlinear decoupling controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features. Finally, the stability and convergence of the proposed algorithm is analyzed. Experimental tests on a heavily coupled nonlinear twin-tank system are carried out to demonstrate the effectiveness and the practicability of the proposed method.

Original languageEnglish
Article number7571110
Pages (from-to)342-353
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume48
Issue number3
DOIs
Publication statusPublished - 2018 Mar 1

Keywords

  • Adaptive neural fuzzy inference system (ANFIS)-based data modeling
  • Decoupling
  • Multivariable and nonlinear systems
  • Switching control
  • Virtual unmodeled dynamics (VUD)

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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