The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Direct Adaptive Neural Control of Perturbed Strict-feedback Nonlinear Systems

섭동 순궤환 비선형 계통의 신경망 직접 적응 제어기

  • 박장현 (목포대학교 전기제어신소재공학부) ;
  • 김성환 (목포대학교 전기제어신소재공학부) ;
  • 유영재 (목포대학교 전기제어신소재공학부)
  • Published : 2009.09.01
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Abstract

An adaptive neural controller for perturbed strict-feedback nonlinear system is proposed. All the previous adaptive neural (or fuzzy) controllers are based on the backstepping scheme where the universal approximators are employed in every design steps. These schemes involve virtual controls and their time derivatives that make the stability analysis and implementation of the controller very complex. This fact is called 'explosion of complexty ' since the complexity grows exponentially as the system dynamic order increases. The proposed adaptive neural control scheme adopt the backstepping design procedure only for determining ideal control law and employ only one neural network to approximate the finally selected ideal controller, which makes the controller design procedure and stability analysis considerably simple compared to the previously proposed controllers. It is shown that all the time-varing signals containing tracking error are stable in the Lyapunov viewpoint.

Keywords

References

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