Convergence analysis of feedback-based iterative learning control with input saturation

• Autores: Gijo Sebastian, Ying Tang, Denny Oetomo
• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Nº. 101, 2019, págs. 44-52
• Idioma: inglés
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• Resumen

  • The use of feedback-based iterative learning control (ILC) has been widely reported in the literature to reduce large transient errors in the iteration-domain. Using both feedback and feed-forward (ILC) would result in large control input. However, due to the existence of hardware limitation, input constraints exist. Such a hard constraint might lead to unacceptable performances such as unstable trajectories in the time-domain and steady-state error in the iteration-domain. In this paper, a feedback-based ILC design is proposed for a class of linear-time-invariant system in the presence of input constraints. The proposed control scheme consists of two parts: one (feedback) deals with the performance in time domain while the other (ILC) ensures ensure the perfect tracking performance. With the help of composite energy function, it is shown that the proposed algorithm can ensure the perfect tracking performance in the presence of hard input constraints under appropriate assumptions. Simulation results support the theoretical findings.