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

Hazem Nounou, Kevin M. Passino

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

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
Volume18
Issue number3
DOIs
Publication statusPublished - Apr 2005
Externally publishedYes

Fingerprint

Nonlinear systems
Tuning
Controllers
Continuous time systems
Control nonlinearities
Fuzzy systems
Power control
Rocks
Polynomials
Neural networks
Feedback

Keywords

  • Adaptation gain
  • Adaptive control
  • Fuzzy/neural control

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

Cite this

Stable auto-tuning of hybrid adaptive fuzzy/neural controllers for nonlinear systems. / Nounou, Hazem; Passino, Kevin M.

In: Engineering Applications of Artificial Intelligence, Vol. 18, No. 3, 04.2005, p. 317-334.

Research output: Contribution to journalArticle

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