Multi-objective iterative learning control using convex optimization

DOI10.1016/j.ejcon.2016.10.001zbMath1403.93200OpenAlexW2537531135MaRDI QIDQ1662993

Jan Swevers, Tong Duy Son, Goele Pipeleers

Publication date: 21 August 2018

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

Full work available at URL: https://doi.org/10.1016/j.ejcon.2016.10.001

zbMATH Keywords

convex optimizationiterative learning control\(\mathcal H_\infty\) controlmulti-objective control

Mathematics Subject Classification ID

Convex programming (90C25) Sensitivity (robustness) (93B35) Design techniques (robust design, computer-aided design, etc.) (93B51) Multivariable systems, multidimensional control systems (93C35) Stochastic learning and adaptive control (93E35)

Related Items (5)

Optimal input filtering for networked iterative learning control systems with packet dropouts and channel noises in both sides ⋮ Optimal input filters for iterative learning control systems with additive noises, random delays, and data dropouts in both channels ⋮ Optimal preview repetitive control with application to permanent magnet synchronous motor drive system ⋮ An intelligent parameter determination approach in iterative learning control ⋮ An optimal filter for updated input of iterative learning controllers with multiplicative and additive noises

Uses Software

• CVX

Cites Work

• Unnamed Item
• Linear computational complexity robust ILC for lifted systems
• Unified Analysis of Iterative Learning and Repetitive Controllers in Trial Domain
• Frequency domain system identification with missing data
• Iterative learning control and repetitive control for engineering practice
• Synthesis of robust multivariable iterative learning controllers with application to a wafer stage motion system
• Monotonic Convergence of Iterative Learning Control for Uncertain Systems Using a Time-Varying Filter
• Robust iterative learning control design is straightforward for uncertain LTI systems satisfying the robust performance condition
• Experimental evaluation of iterative learning control algorithms for non-minimum phase plants
• On the design of ILC algorithms using optimization

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