Linear and Nonlinear Iterative Learning Control

By Jian-xin Xu, Ying Tan. 2003

445 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/930dc498c106014f244801206435b0ac3a637cc3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Z. Hou
2. Jian-xin Xu
3. Zhuo Wang
4. S. Jin
5. Eugenio Schuster
6. Chao Xu
7. Reza Arastoo
8. Jessica Chani-Cahuana

Citations in the corpus

• From model-based control to data-driven control: Survey, classification and perspective (2013, methodology): “Several previous studies provided a comprehensive and systematic summary of the recent ILC research [33,96,130,149,150].”
• A survey on iterative learning control for nonlinear systems (2011, methodology): “…knowledge on the system (1) required for ILC design in (3) is also very minor, which is the knowledge on J such that the gain matrix (cid:2) can be chosen to ensure For instance, when the system (1) is single input and single output, we only need to know the upper bound of J (Xu and Tan 2003).”
• A survey of iterative learning control (2006, background): “Some researchers consider algorithms with linear time-varying (LTV) functions [42], [46], [47], nonlinear functions [37], and iteration-varying functions [1], [48].”
• Iterative Learning Control for RF Power Amplifier Linearization (2016, methodology): “7) Solving this [22], the optimal learning gain γ ∗ is given by γ ∗ = 2 Jmin + Jmax .”
• Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems (2011, methodology): “For a repeatable control task on a finite time interval, ILC is most suitable control method with a data-driven feature [6]–[8].”
• Sliding-Mode Control With Soft Computing: A Survey (2009, unknown): “, [12].”
• A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems (2011, unknown): “For a repeatable control task on a finite time interval, the ILC approach is also a typical data-driven control approach [7]–[10].”
• On Iterative Learning Control of parabolic distributed parameter systems (2009, methodology): “, [3], [4], [5], [6], [7] and references therein).”
• Provably safe and robust learning-based model predictive control (2011, unknown): “This has driven research in adaptive [64,65,55,6,60] and learning-based [74,3,70,1,47] control.”