Adaptive neural predictive control schemes for unknown nonlinear systems

Wei Wu, Jun Xian Chang, Chia Ju Wu

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Adaptive neural predictive control strategies for general nonlinear systems are proposed. The network weight update rule with discrete-time learning procedures which executes the minimal error between the feedforward neural network (INN) model output and plant output is obtained. The one-step-ahead neural predictive control combined with the 'dual' optimization algorithm serves as a rapid, reliable adaptation mechanism and guarantees the stable output regulation of a class of uncertain nonlinear systems. In principle, the off-line training algorithm on neural networks is reduced, and the state/parameter estimation design is obviated. Through closed-loop simulation demonstrations, the proposed control schemes have been successfully applied to two reactor system examples.

Original languageEnglish
Pages (from-to)107-117
Number of pages11
JournalJournal of the Chinese Institute of Chemical Engineers
Issue number2
Publication statusPublished - 2005 Mar 1

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)


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