Details

Autor(en) / Beteiligte

• Guo, Zirong
• Meng, Deyuan
• Zhang, Jingyao
• Du, Junping
• Jia, Yingmin
• Zhang, Weicun

Titel

Robust Convergence of High-Order Adaptive Iterative Learning Control Against Iteration-Varying Uncertainties

Ist Teil von

• Proceedings of 2019 Chinese Intelligent Systems Conference, 2019, Vol.592, p.591-598

Ort / Verlag

Singapore: Springer Singapore Pte. Limited

Erscheinungsjahr

2019

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In this paper, adaptive iterative learning control (AILC) algorithms using a high-order law for linear time-varying (LTV) systems with iteration-varying uncertainties are proposed. Sufficient conditions are derived to guarantee the robust convergence of tracking error such that the bounded-input-bounded-output stability of LTV systems can be achieved. Extensions of established AILC results to nonlinear systems are further developed. Numerical simulations are implemented to validate the effectiveness of the theoretical results.