Highlights. •. A data-driven ILC is proposed for nonlinear systems with varying trial lengths without using any mechanistic model.
This work considers randomly varying trial lengths of a nonlinear and non-affine repetitive system and proposes a data-driven nonlinear iterative learning ...
This brief is concerned with iterative learning control (ILC) of constrained multi-input multi-output (MIMO) nonlinear systems under the state alignment ...
A data-driven ILC is proposed for nonlinear systems with varying trial lengths without using any mechanistic model. The learning gain can be tuned using I/O ...
This paper presents an iterative learning control (ILC) method for nonlinear systems where the trial lengths could be randomly varying in the iteration ...
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Aiming for this issue, a nonlinear active disturbance rejection control (NADRC)-based control strategy is proposed for robotic manipulators. In this controller, ...
This paper proposes adaptive iterative learning control (ILC) schemes for continuous-time parametric nonlinear systems with iteration lengths that randomly vary ...
The book focuses on the synthesis and analysis of ILC for both linear and nonlinear systems with iteration-varying trial lengths, and proposes various novel techniques to deal with the precise tracking problem under non-repeatable trial lengths, such as moving window, switching system, and searching-based moving average operator.
In this work, a nonlinear uncertain system's iterative learning control (ILC) with randomly varying iteration lengths is discussed. Based on existing adaptive
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This book studies a novel data-driven framework for the design and analysis of optimal iterative learning control for nonlinear discrete-time systems.