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About 10,000 papers for “Iterative learning control”

Iterative Learning Control

P. L. Lewin · ePrints Soton (University of Southampton) · 2003

Iterative Learning Control (ILC) was first proposed in 1984 by Arimoto, Miyazaki and Kawamura (1) and allows process tools such as robot arms and production machinery to repeat actions or synchronise to differing demands in production processes, such as those in the food industry.

Cited by 0

Iterative Learning Control

Suguru Arimoto · 1996

Abstract A theory of iterative learning control for refinement of motions of robotic systems is presented, together with simulation results. It is shown that the passivity of such non-linear mechanical systems plays a key role in the ability to acquire a desired and skilled movement through repeated practice. This iterative learning scheme can be also applied to robot dynamics under holonomic tool tip constraint. When fluctuations and measurement noise exist, the introduction of a forgetting factor in the learning update law becomes crucial in the convergence of trajectory tracking errors.

Cited by 8

Iterative Learning Control

2010

Iterative learning control (ILC) is a performance-enhancing feedforward control scheme for systems that repeat the same trajectory or task. Before the start of each iteration of the trajectory, the designed ILC algorithm uses the error signal from the previous iteration(s) to generate an updated feedforward control signal. The learning process converges after anywhere from a few to tens of iterations, depending on the algorithm. In the literature it is commonly reported that ILC improves the performance of physical systems by several orders of magnitude, measured by root mean square (RMS) or maximum error, as compared to those systems’ feedback controllers.

Cited by 4

On iterative learning control

T. Sugie · 2004

We describe a new type of iterative learning control (ILC) which seeks the desired input in an appropriate finite dimensional input subspace. The method achieves perfect tracking for a class of uncertain systems without using time derivative of tracking error signals in the learning law. Its effectiveness is demonstrated by experiments. In addition, an application of the ILC method to continuous-time model identification is shown.

Cited by 3

On iterative learning control

Toshiharu Sugie · 2004

We describe a new type of iterative learning control (ILC) which seeks the desired input in an appropriate finite dimensional input subspace. The method achieves perfect tracking for a class of uncertain systems without using time derivative of tracking error signals in the learning law. Its effectiveness is demonstrated by experiments. In addition, an application of the ILC method to continuous-time model identification is shown.

Cited by 5

Adaptive iterative learning control

D.H. Owens · 1996

Describes the basic ideas underpinning the notion of iterative learning control (ILC) for systems where the experimental set-up permits the use of repetition to enable the controller to increase its accuracy in tracking of a specified set-point signal r(t). The resultant process of learning requires a new viewpoint on aspects of classical control design that presents new problems and new opportunities. The main issue addressed in the paper is the possibility of coping with plant uncertainty during the learning process by a combination of the process of iterative learning with adaptive control. The idea opens up a number of possibilities. The approach used here is that of universal adaptive stabilisation as it is a useful vehicle in demonstrating the essential feasibility of the ideas and in suggesting limiting factors.

Cited by 10

Hybrid iterative learning control

M. O. Tokhi, Abul Azad · Institution of Engineering and Technology eBooks · 2017

This chapter presents hybrid iterative learning control schemes with acceleration feedback for control of flexible manipulator systems. The learning schemes considered are PD-type, PI-type and PID-type. A collocated PD controller is considered for rigid-body motion control, and this is extended to incorporate noncollocated and iterative learning control with acceleration feedback. The control scheme is incorporated into a single-link flexible manipulator and a set of case study exercises assessing the performance of the approach is presented and discussed. The tests include time-domain and frequency-domain analyses with and without acceleration feedback in terms of amount of vibration reduction at resonance modes, robustness and input tracking.

Cited by 0

Iterative Model Learning and Dual Iterative Learning Control: A Unified Framework for Data-Driven Iterative Learning Control

Michael Meindl, Simon Bachhuber, Thomas Seel · 2024

Accurate reference tracking is essential in control tasks in a variety of applications, and, in repetitive systems, model-based Iterative Learning Control (ILC) is a standard solution that is underpinned by formal stability and convergence guarantees. To overcome the limitations of model-based ILC design, data-driven ILC methods have been introduced which lead to a patchwork of several, very different approaches that do not preserve the modularity and theoretical guarantees of the many, well-established model-based ILC methods. We, for the first time, propose a unifying framework for data-driven ILC that preserves the modularity and formally proven properties of model-based ILC. Specifically, we propose Iterative Model Learning (IML) and Dual Iterative Learning Control (DILC). The IML framework enables iterative learning of unknown dynamics in repetitive systems using input/output trajectory pairs. We formally prove the duality between IML and ILC, i.e., an IML system is equivalent to an ILC system with a trial-varying reference and trial-varying but known dynamics. We further provide generic conditions to guarantee the convergence of the model and prove the duality of monotonic convergence in ILC and IML. This duality allows arbitrary, established, model-based ILC methods to be effectively applied within the IML framework to learn models of unknown dynamics. Unlike existing data-driven ILC approaches which only combine specific model learning schemes with specific model-based ILC approaches, the proposed DILC framework combines IML with model-based ILC such that established, model-based ILC methods can be arbitrarily combined for simultaneous model and control learning with a formally proven separation principle. The proposed DILC is a unified framework for data-driven ILC that not only relieves model-based ILC of a priori model requirement but also preserves the modularity and theoretical guarantees of model-based ILC. Both IML and DILC are validated by extensive simulations and real-world experiments.

Cited by 0Open access