Intervalized iterative learning control for monotonic convergence in the sense of sup-norm

DOI10.1080/00207170500291937zbMath1078.93033OpenAlexW2002404497MaRDI QIDQ5704604

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Publication date: 15 November 2005

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

Full work available at URL: https://doi.org/10.1080/00207170500291937

Mathematics Subject Classification ID

Design techniques (robust design, computer-aided design, etc.) (93B51) Multivariable systems, multidimensional control systems (93C35)

Related Items (11)

Networked iterative learning control for discrete-time systems with stochastic packet dropouts in input and output channels ⋮ Data-driven networked optimal iterative learning control for discrete linear time-varying systems with one-operation Bernoulli-type communication delays ⋮ Lebesgue‐p NORM Convergence OF Fractional‐Order PID‐Type Iterative Learning Control for Linear Systems ⋮ Input–output‐driven gain‐adaptive iterative learning control for linear discrete‐time‐invariant systems ⋮ Equivalence and convergence of two iterative learning control schemes with state feedback ⋮ Markov parameters identification and adaptive iterative learning control for linear discrete-time MIMO systems with higher-order relative degree ⋮ Rectified fractional order iterative learning control for linear system with initial state shift ⋮ Lebesgue-\(p\) norm convergence analysis of PD\(^\alpha\)-type iterative learning control for fractional-order nonlinear systems ⋮ Convergence Properties of Iterative Learning Control Processes in the Sense of the Lebesgue‐P Norm ⋮ Robust design of feedback feed-forward iterative learning control based on 2D system theory for linear uncertain systems ⋮ Iterative learning control for a class of uncertain nonlinear systems with current state feedback

Cites Work

• A generalized iterative learning controller against initial state error
• A study on the robustness of a PID-type iterative learning controller against initial state error
• Stochastic P-type/D-type iterative learning control algorithms
• Discrete-time inverse model-based iterative learning control: stability, monotonicity and robustness

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