Discrete-time Adaptive ILC for Non-parametric Uncertain Nonlinear Systems with Iteration-Varying Trajectory and Random Initial Condition

DOI10.1002/asjc.569zbMath1327.93243OpenAlexW2104607335MaRDI QIDQ3454411

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Publication date: 24 November 2015

Published in: Asian Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/asjc.569

zbMATH Keywords

adaptive iterative learning controlnonlinear discrete-time systemrandom initial conditionnon-parametric uncertaintiesiteration-varying target trajectories

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Nonlinear systems in control theory (93C10) Adaptive control/observation systems (93C40) Discrete-time control/observation systems (93C55) Stochastic systems in control theory (general) (93E03)

Related Items (6)

Adaptive iteration learning control with iteration‐varying event‐triggered mechanism for discrete‐time nonlinear systems with random initial states ⋮ On Indirect Model Reference Adaptive Learning Control ⋮ A parametric learning and identification based robust iterative learning control for time varying delay systems ⋮ Multiple model ILC for continuous-time nonlinear systems ⋮ Unfalsifying Pole Locations Using a Fading Memory Cost Function ⋮ Adaptive Terminal ILC for Iteration-varying Target Points

Cites Work

• Adaptive iterative learning control for robot manipulators
• Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition
• How much uncertainty can be dealt with by feedback?
• A framework for analysis of observer-based ILC
• An ILC scheme for a class of nonlinear continuous-time systems with time-iteration-varying parameters subject to second-order internal model
• On-off iterative adaptive controller for low-power micro-robotic step regulation
• Non-linear iterative learning by an adaptive Lyapunov technique
• Robust learning control for robotic manipulators with an extension to a class of non-linear systems
• A composite energy function-based learning control approach for nonlinear systems with time-varying parametric uncertainties
• Sampled-data iterative learning control for nonlinear systems with arbitrary relative degree

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