Adaptive neural network predictive control for nonlinear pure feedback systems with input delay

Jing Na*, Xuemei Ren, Cong Shang, Yu Guo

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

79 Citations (Scopus)

Abstract

This paper presents an adaptive neural control design for nonlinear pure-feedback systems with an input time-delay. Novel state variables and the corresponding transform are introduced, such that the state-feedback control of a pure-feedback system can be viewed as the output-feedback control of a canonical system. An adaptive predictor incorporated with a high-order neural network (HONN) observer is proposed to obtain the future system states predictions, which are used in the control design to circumvent the input delay and nonlinearities. The proposed predictor, observer and controller are all online implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed. The conventional backstepping design and analysis for pure-feedback systems are avoided, which renders the developed scheme simpler in its synthesis and application. Practical guidelines on the control implementation and the parameter design are provided. Simulation on a continuous stirred tank reactor (CSTR) and practical experiments on a three-tank liquid level process control system are included to verify the reliability and effectiveness.

Original languageEnglish
Pages (from-to)194-206
Number of pages13
JournalJournal of Process Control
Volume22
Issue number1
DOIs
Publication statusPublished - 1 Jan 2012

Keywords

  • Neural networks
  • Nonlinear predictor
  • Process control
  • Pure-feedback systems
  • Time-delay

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