An improved model free adaptive control algorithm

  • Authors:

  • Xu Aidong;Zheng Yangbo;Song Yan;Liu Mingzhe

  • Affiliations:

  • Shenyang Institute of Automation, The Chinese Academy of Sciences, Shenyang, China;Shenyang Institute of Automation, The Chinese Academy of Sciences, Shenyang, China and Graduate School of Chinese Academy of Sciences, Beijing, China;Shenyang Institute of Automation, The Chinese Academy of Sciences, Shenyang, China;Shenyang Institute of Automation, The Chinese Academy of Sciences, Shenyang, China

  • Venue:
  • ICNC'09 Proceedings of the 5th international conference on Natural computation
  • Year:
  • 2009

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Abstract

Generally the application of traditional adaptive control algorithm relies on the mathematic model of system. But mathematic models of some dynamic systems are difficult to establish. According to this actual problem and the existing structure of algorithm, an improved Model Free Adaptive control algorithm based on neural network is put forward in this paper. Within corresponding controller, equivalent proportion link is used to enhance the flexibility of adjustable parameters and speed, in addition, the estimate of sensitivity of process is used to update the values of the weighting factors, which can improve the actual application effect of neural network. In the end, simulation result shows that this algorithm can get the good performance in the aspect of stability, speed and adaptability.