Decentralized robust adaptive neural dynamic surface control for multi-machine excitation systems with static var compensator

Xiuyu Zhang, Shuran Wang, Guoqiang Zhu, Jia Ma, Xiaoming Li, Xinkai Chen

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Focusing on solving the control problem of the multimachine excitation systems with static var compensator (SVC), this paper proposes a decentralized neural adaptive dynamic surface control (DNADSC) scheme, where the radial basis function neural networks are used to approximate the unknown nonlinear dynamics of the subsystems and compensate the unknown nonlinear interactions. The main advantages of the proposed DNADSC scheme are summarized as follows: (1) the strong nonlinearities and complexities are mitigated when the SVC equipment are introduced to the multimachine excitation systems and the explosion of complexity problem of the backstepping method is overcome by combining the dynamic surface control method with neural networks (NNs) approximators; 2) the tracking error of the power angle can be kept in the prespecified performance curve by introducing the error transformed function; (3) instead of estimating the weighted vector itself, the norm of the weighted vector of the NNs are estimated, leading to the reduction of the computational burden. It is proved that all the signals in the multimachine excitation system with SVC are semiglobally uniformly ultimately bounded.

Original languageEnglish
Pages (from-to)92-113
Number of pages22
JournalInternational Journal of Adaptive Control and Signal Processing
Volume33
Issue number1
DOIs
Publication statusPublished - 2019 Jan

Keywords

  • adaptive control
  • decentralized control
  • error transformation function
  • multimachine excitation systems with SVC

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Electrical and Electronic Engineering

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