On almost sure and mean square convergence of P-type ILC under randomly varying iteration lengths

• Dong Shen ^[2] ; Wei Zhang ^[2] ; Youqing Wang ^[2] ; Chiang-Ju Chien ^[1]
  1. [1] Huafan University

     Huafan University

     Taiwán

     
  2. [2] Beijing University of Chemical Technology

     Beijing University of Chemical Technology

     China

     
• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 63, 2016, págs. 359-365
• Idioma: inglés
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• Resumen

  • This note proposes convergence analysis of iterative learning control (ILC) for discrete-time linear systems with randomly varying iteration lengths. No prior information is required on the probability distribution of randomly varying iteration lengths. The conventional P-type update law is adopted with Arimoto-like gain and/or causal gain. The convergence both in almost sure and mean square senses is proved by direct math calculating. Numerical simulations verifies the theoretical analysis.