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Supervisory intelligent control system design for forward DC–DC converters

Supervisory intelligent control system design for forward DC–DC converters

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  • Author(s): C.-F. Hsu 1 ; C.-M. Lin 2 ; K.-H. Cheng 3
    • View affiliations
    • Affiliations: 1: Department of Electrical and Control Engineering, National Chiao-Tung University, Taiwan, Republic of China
      2: Department of Electrical Engineering, Yuan-Ze University, Taiwan, Republic of China
      3: Department of Electrical Engineering, Chang Gung University, Taiwan, Republic of China
  • Source: Volume 153, Issue 5, September 2006, p. 691 – 701
    DOI:  10.1049/ip-epa:20050376 , Print ISSN 1350-2352, Online ISSN 1359-7043
© The Institution of Engineering and Technology
Published

A supervisory intelligent control system is developed. The supervisory intelligent control system is comprised of a neural controller and a supervisory controller. The neural controller is investigated to mimic an ideal controller and the supervisory controller is designed to compensate for the approximation error between the neural controller and the ideal controller. In the proposed control scheme, an online parameter training methodology is developed based on the gradient descent method and the Lyapunov stability theorem, so that the control system can guarantee system stability. Finally, to investigate the effectiveness of the proposed control scheme, it is applied to control a forward DC–DC converter. A comparison between a PI controller, a fuzzy controller, a fuzzy neural network controller and the supervisory intelligent controller is made. Experimental results show that the proposed control system can achieve favourable regulation performances even for different input voltages and under load resistance variations.

Inspec keywords: learning (artificial intelligence); neurocontrollers; approximation theory; Lyapunov methods; DC-DC power convertors; error compensation; fuzzy control; control system synthesis; intelligent control

Other keywords: supervisory intelligent control system design; online parameter training; approximation error compensation; fuzzy controller; gradient descent method; PI controller; DC-DC converter; neural controller; Lyapunov stability theorem

Subjects: Neurocontrol; Control system analysis and synthesis methods; Fuzzy control; Power electronics, supply and supervisory circuits; Control of electric power systems; Stability in control theory; Learning in AI (theory); Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis)

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