Fuzzy control for nonlinear systems via neural-network-based approach

Feng Hsiag Hsiao, Wei Ling Chiang, Cheng Wu Chen

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


The stabilization problem is considered in this study for a nonlinear system. It is shown that the stability analysis of nonlinear systems can be reduced into linear matrix inequality (LMI) problems. First, the neural-network (NN) model is employed to approximate a nonlinear system via the back propagation algorithm. Then, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. In terms of Lyapunov’s direct method, a sufficient condition is provided to guarantee the stability of nonlinear systems. Based on this criterion, a model based fuzzy controller is then designed to stabilize the nonlinear system and the H control performance is achieved at the same time. Finally, two examples with numerical simulations are given to illustrate the control methodology.

Original languageEnglish
Pages (from-to)145-152
Number of pages8
JournalInternational Journal of Computational Methods in Engineering Science and Mechanics
Issue number3
StatePublished - 2005


  • Fuzzy control
  • Neural network


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