Hybrid intelligent output-feedback control for trajectory tracking of uncertain nonlinear multivariable dynamical systems

Yi Hsing Chien, Wei Yen Wang, I. Hsum Li, Kuang Yow Lian, Tsu Tian Lee

Research output: Contribution to journalArticle

7 Citations (Scopus)


Output-feedback control for trajectory tracking is an important research topic of various engineering systems. In this paper, a novel online hybrid direct/indirect adaptive Petri fuzzy neural network (PFNN) controller with stare observer for uncertain nonlinear multivariable dynamical systems using generalized projection-update laws is presented. This new approach consists of control objectives determination, approximator configuration design, system dynamics modeling, online control algorithm development, and system stability analysis. According to the importance and viability of plant knowledge and control knowledge, a weighting factor is utilized to sum together the direct and indirect adaptive PFNN controllers. Therefore, the controller design methodology is more flexible during the design process. Besides, an improved generalized projection-update law is utilized to tune the adjustable parameters to prevent parameter drift. To illustrate the effectiveness of the proposed online hybrid PFNN controller and observer-design methodology, numerical simulation results for inverted pendulum systems and rigid robot manipulators are given in this paper.

Original languageEnglish
Pages (from-to)141-153
Number of pages13
JournalInternational Journal of Fuzzy Systems
Issue number1
Publication statusPublished - 2012 Mar



  • Output-feedback control
  • Trajectory tracking
  • Uncertain nonlinear systems

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Computational Theory and Mathematics
  • Artificial Intelligence

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