Accelerated norm-optimal iterative learning control algorithms using successive projection

DOI10.1080/00207170802512824zbMath1194.93123OpenAlexW2091686438MaRDI QIDQ5323230

Bing Chu, David H. Owens

Publication date: 23 July 2009

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

Full work available at URL: https://doi.org/10.1080/00207170802512824

zbMATH Keywords

projection methodsoptimisationiterative learning controlnorm-optimisation

Mathematics Subject Classification ID

Related Items (10)

Iterative learning control for constrained linear systems ⋮ An improvement on the transient response of tracking for the sampled-data system based on an improved PD-type iterative learning control ⋮ Linearly monotonic convergence of nonlinear <scp>parameter‐optimal</scp> iterative learning control to linear discrete‐time‐invariant systems ⋮ Input–output‐driven gain‐adaptive iterative learning control for linear discrete‐time‐invariant systems ⋮ Machine learning based iterative learning control for non‐repetitive time‐varying systems ⋮ Optimal iterative learning control design for continuous-time systems with nonidentical trial lengths using alternating projections between multiple sets ⋮ Method to improve convergence performance of iterative learning control systems with bounded noise ⋮ An inverse-model approach to multivariable norm optimal iterative learning control with auxiliary optimisation ⋮ Multivariable norm optimal iterative learning control with auxiliary optimisation ⋮ Iterative learning control for intermittently sampled data: monotonic convergence, design, and applications

Cites Work

• Iterative learning control. Robustness and monotonic convergence for interval systems.
• Basis functions and parameter optimisation in high-order iterative learning control
• Higher-order iterative learning control algorithm
• Iterative solution of constrained differential/algebraic systems
• Predictive optimal iterative learning control
• Iterative learning control for discrete-time systems with exponential rate of convergence
• Iterative learning control using optimal feedback and feedforward actions
• An algebraic approach to iterative learning control

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