吉林大学学报(工学版) ›› 2010, Vol. 40 ›› Issue (04): 1054-1058.

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Clonal selection algorithm based nonlinear optimal iterative learning control

LI Heng-jie1|HAO Xiao-hong1|ZENG Xian-qiang2   

  1. 1.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China；2.Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730050, China
  • Received:2008-10-20 Online:2010-07-01 Published:2010-07-01

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Abstract:

In order to realize effective tracking of output of nonlinear systems with constraint and model uncertainty in specified time domain, improved clonal selection algorithms are employed to solve optimization problems in iterative learning control. A clonal selection algorithm based nonlinear optimal iterative learning control is proposed. After each iteration, a clonal selection algorithm is employed to search optimal input for next iteration, and another clonal selection algorithm is used to update the plant model. Simulations show that the proposed method converges faster than GA-ILC, and is able to deal with constraint on input and model uncertainty. Satisfactory tracking performance can be obtained after a few iterations.

Key words: artificial intelligence, iterative learning control, optimization, clonal selection algorithm, non-linear system

CLC Number: 

  • TP181
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