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Decentralized robust adaptive neural dynamic surface control for multi‐machine excitation systems with static var compensator
Author(s) -
Zhang Xiuyu,
Wang Shuran,
Zhu Guoqiang,
Ma Jia,
Li Xiaoming,
Chen Xinkai
Publication year - 2019
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2953
Subject(s) - control theory (sociology) , backstepping , artificial neural network , nonlinear system , tracking error , electric power system , computer science , bounded function , adaptive control , mathematics , power (physics) , control (management) , artificial intelligence , mathematical analysis , physics , quantum mechanics
Summary 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.