Adaptive neural predictive control schemes for unknown nonlinear systems

Wei Wu, Jun Xian Chang, Chia Ju Wu

研究成果: Article同行評審

3 引文 斯高帕斯(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.

頁(從 - 到)107-117
期刊Journal of the Chinese Institute of Chemical Engineers
出版狀態Published - 2005 3月 1

All Science Journal Classification (ASJC) codes

  • 化學 (全部)
  • 化學工程 (全部)


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