Stable auto-tuning of hybrid adaptive fuzzy/neural controllers for nonlinear systems

Hazem N. Nounou, Kevin M. Passino

Research output: Contribution to journalArticle

10 Citations (Scopus)


In direct adaptive control, the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. In the indirect case, however, we approximate parts of the plant dynamics that are used by a feedback controller to cancel the system nonlinearities. In both cases, "approximators" such as linear mappings, polynomials, fuzzy systems, or neural networks can be used as either the parameterized nonlinear controller or identifier model. In this paper, we present an algorithm to tune the adaptation gain for a gradient-based hybrid update law used for a class of nonlinear continuous-time systems in both direct and indirect cases. In our proposed algorithm, the adaptation gain is obtained by minimizing the instantaneous control energy. Finally, we will demonstrate the performance of the algorithm via a wing rock regulation example.

Original languageEnglish
Pages (from-to)317-334
Number of pages18
JournalEngineering Applications of Artificial Intelligence
Issue number3
Publication statusPublished - 1 Apr 2005


  • Adaptation gain
  • Adaptive control
  • Fuzzy/neural control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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