Analysis of nonlinear discrete-time systems with higher-order iterative learning control

DOI10.1023/A:1017912319985zbMath0999.93037OpenAlexW2154916250MaRDI QIDQ5957809

Sun, Mingxuan, Wang, Danwei

Publication date: 2 December 2002

Published in: Dynamics and Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1017912319985

zbMATH Keywords

nonlinear discrete-time systemshigher-order iterative learning controlinitial shifts

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Control/observation systems with incomplete information (93C41) Design techniques (robust design, computer-aided design, etc.) (93B51) Multivariable systems, multidimensional control systems (93C35) Discrete-time control/observation systems (93C55)

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Necessary and sufficient stability condition of LTV iterative learning control systems using a 2-D approach ⋮ Iterative learning control for discrete nonlinear systems with randomly iteration varying lengths ⋮ Pulse compensation for PD-type iterative learning control against initial state shift ⋮ Varying-order iterative learning control against perturbed initial conditions ⋮ Feedback higher-order iterative learning control for nonlinear systems with non-uniform iteration lengths and random initial state shifts ⋮ Robust iterative learning control for linear continuous systems with vector relative degree under varying input trail lengths and random initial state shifts ⋮ Convergence analysis for iterative learning control of impulsive linear discrete delay systems ⋮ Iterative learning control for linear discrete delay systems via discrete matrix delayed exponential function approach ⋮ Convergence Properties of Iterative Learning Control Processes in the Sense of the Lebesgue‐P Norm ⋮ A stochastic iterative learning control algorithm with application to an induction motor ⋮ Iterative learning identification for a class of parabolic distributed parameter systems ⋮ Robust networked ILC for switched nonlinear discrete systems with non-repetitive uncertainties and random data dropouts

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