Resource-efficient ILC for LTI/LTV systems through LQ tracking and stable inversion: Enabling large feedforward tasks on a position-dependent printer

Zundert, Jcd Jurgen van; Bolder, JJ Joost; Koekebakker, Sjirk; Oomen, Tom

doi:10.1016/j.mechatronics.2016.07.001

Cited by 48 publications

(48 citation statements)

References 47 publications

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“…Many related challenges, e.g., for specific application areas, have not been addressed in the present paper, including (4) (113)- (116) . In addition, numerical aspects are highly challenging for complex systems, in control (117) , system identification (79)- (82) , and learning (95) . In addition, resource-efficient approaches for ILC based on sparse optimization are investigated in (118) .…”

Section: Discussionmentioning

confidence: 99%

“…18. An LTV approach, see (95) , can effectively deal with position-dependent behavior. Initial results towards an LPV approach are reported in (96) , whereas an LPTV approach is pursued in (97) .…”

Section: Ilc For Position-dependent Systemsmentioning

confidence: 99%

“…Linearization of the model at a different location leads to a convergent ILC (+) without the need for a Q-filter, hence leading to improved performance. An LTV design (95) leads to superior performance: it achieves high performance and fast convergence …”

Section: Ilc For Position-dependent Systemsmentioning

confidence: 99%

“…1(a), see (102) . The theoretical framework is further extended towards input shaping (103) and rational feedforward structures in (104)- (106) , which have as key advantage that these can exactly compensate non-minimum phase dynamics, see (107) for a detailed exposition, and also (95) (108) (109) for further details and applications. Finally, a strongly related approach that further connects to system identification in Sec.…”

Section: Flexible Tasksmentioning

confidence: 99%

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Advanced Motion Control for Precision Mechatronics: Control, Identification, and Learning of Complex Systems

Oomen

2018

IEEJ Journal IA

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Manufacturing equipment and scientific instruments, including wafer scanners, printers, microscopes, and medical imaging scanners, require accurate and fast motions. An increase in such requirements necessitates enhanced control performance. The aim of this paper is to identify several challenges for advanced motion control originating from these increasing accuracy, speed, and cost requirements. For instance, flexible mechanics must be explicitly addressed through overactuation, oversensing, inferential control, and position-dependent control. This in turn requires suitable models of appropriate complexity. One of the main advantages of such motion systems is the fact that experimenting and collecting large amounts of accurate data is inexpensive, paving the way for identifying and learning of models and controllers from experimental data. Several ongoing developments are outlined that constitute part of an overall framework for control, identification, and learning of complex motion systems. In turn, this may pave the way for new mechatronic design principles, leading to fast lightweight machines where spatio-temporal flexible mechanics are explicitly compensated through advanced motion control.

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Section: Discussionmentioning

confidence: 99%

Section: Ilc For Position-dependent Systemsmentioning

confidence: 99%

Section: Ilc For Position-dependent Systemsmentioning

confidence: 99%

Section: Flexible Tasksmentioning

confidence: 99%

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Advanced Motion Control for Precision Mechatronics: Control, Identification, and Learning of Complex Systems

Oomen

2018

IEEJ Journal IA

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“…For example, ZPETC is often used for the design of ILC filters, but requires an additional robustness filter at the cost of performance [2,18]. Alternatively, infinite preview [19] or H ∞ based [11,12] techniques can be used. However, [11,12] lack the use of preview, and are recently extended in [20] towards preview/fixed lag smoothing situations.…”

Section: Introductionmentioning

confidence: 99%

On inversion-based approaches for feedforward and ILC

2018

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Iterative learning control with discrete‐time nonlinear nonminimum phase models via stable inversion

Spiegel

Strijbosch

Oomen

et al. 2021

Intl J Robust & Nonlinear

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Output reference tracking can be improved by iteratively learning from past data to inform the design of feedforward control inputs for subsequent tracking attempts. This process is called iterative learning control (ILC). This article develops a method to apply ILC to systems with nonlinear discrete-time dynamical models with unstable inverses (i.e. discrete-time nonlinear non-minimum phase models). This class of systems includes piezoactuators, electric power converters, and manipulators with flexible links, which may be found in nanopositioning stages, rolling mills, and robotic arms, respectively. As these devices may be required to execute fine transient reference tracking tasks repetitively in contexts such as manufacturing, they may benefit from ILC. Specifically, this article facilitates ILC of such systems by presenting a new ILC synthesis framework that allows combination of the principles of Newton's root finding algorithm with stable inversion, a technique for generating stable trajectories from unstable models. The new framework, called Invert-Linearize ILC (ILILC), is validated in simulation on a cart-and-pendulum system with model error, process noise, and measurement noise. Where preexisting Newton-based ILC diverges, ILILC with stable inversion converges, and does so in less than one third the number of trials necessary for the convergence of a gradient-descent-based ILC technique used as a benchmark.

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Resource-efficient ILC for LTI/LTV systems through LQ tracking and stable inversion: Enabling large feedforward tasks on a position-dependent printer

Cited by 48 publications

References 47 publications

Advanced Motion Control for Precision Mechatronics: Control, Identification, and Learning of Complex Systems

Advanced Motion Control for Precision Mechatronics: Control, Identification, and Learning of Complex Systems

On inversion-based approaches for feedforward and ILC

Iterative learning control with discrete‐time nonlinear nonminimum phase models via stable inversion

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