Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Adaptive Neural Control for Output-Constrained Pure-Feedback Systems

출력 제약된 Pure-Feedback 시스템의 적응 신경망 제어

  • Kim, Bong Su (School of Electrical and Electronics Engineering, Chung-Ang University) ;
  • Yoo, Sung Jin (School of Electrical and Electronics Engineering, Chung-Ang University)
  • 김봉수 (중앙대학교 전자전기공학부) ;
  • 유성진 (중앙대학교 전자전기공학부)
  • Received : 2013.10.09
  • Accepted : 2013.11.18
  • Published : 2014.01.01
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Abstract

This paper investigates an adaptive approximation design problem for the tracking control of output-constrained non-affine pure-feedback systems. To satisfy the desired performance without constraint violation, we employ a barrier Lyapunov function which grows to infinity whenever its argument approaches some limits. The main difficulty in dealing with pure-feedback systems considering output constraints is that the system has a non-affine appearance of the constrained variable to be used as a virtual control. To overcome this difficulty, the implicit function theorem and mean value theorem are exploited to assert the existence of the desired virtual and actual controls. The function approximation technique based on adaptive neural networks is used to estimate the desired control inputs. It is shown that all signals in the closed-loop system are uniformly ultimately bounded.

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References

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