An improved adaptive switching control based on quasi-ARX neural network for nonlinear systems

Imam Sutrisno*, Chi Che, Jinglu Hu

*この研究の対応する著者

研究成果査読

4 被引用数 (Scopus)

抄録

In this paper, an improved switching mechanism based on quasi-linear auto regressive exogenous (quasi-ARX) neural network (QARXNN) is presented for the adaptive control of nonlinear systems. The proposed switching control is composed of a QARXNN-based prediction model and an improved switching mechanism using two new adaptive control laws, first is moving average filter law and second is new switching law. Since the control result of nonlinear predictor is better than the linear predictor in most of the time, the adaptive control with a simple switching mechanism has many useless switching during the processing. Hence, the improved smooth switching mechanism is proposed to replace the original switching mechanism; it can improve the performance by reducing the useless switching while guaranteeing stability of the system control. The simulations show that the efficiency of the proposed control method is satisfied in stability, improve control accuracy and robustness.

本文言語English
ページ(範囲)347-353
ページ数7
ジャーナルArtificial Life and Robotics
19
4
DOI
出版ステータスPublished - 2014 12 10

ASJC Scopus subject areas

  • 生化学、遺伝学、分子生物学(全般)
  • 人工知能

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