Combined $H _{\infty}$-Feedback Control and Iterative Learning Control Design With Application to Nanopositioning Systems

Helfrich, B.E.; Lee, Chibum; Bristow, Douglas A.; Xiao, Xiang; Dong, Jingyan; Alleyne, Andrew G.; Salapaka, Srinivasa M.; Ferreira, Placid M.

doi:10.1109/tcst.2009.2018835

Cited by 77 publications

(33 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Q filter was also chosen to be a non-causal zero phase filter with a cutoff frequency of two times 0.000223 Hz. This satisfies the stability condition in (9). Additionally, the reference modifier T z,rm was limited between -1 and 1.…”

Section: Office Building Temperature Controlmentioning

confidence: 87%

“…This means that we only need nearly periodic disturbances, in the sense that they are allowed to change, but on average they will show periodicity. The authors in [9] have provided a way to calculate the Repeatable-to-Nonrepeatable Ratio (RNR), which can be used to determine if the repeatable part of the tracking error or disturbance is larger than the nonrepeatable part for each frequency. A ratio above zero means that there is potential for applying learning type control.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Learning/repetitive control for building systems with nearly periodic disturbances

Vinther

Chandan

Alleyne

2013

2013 European Control Conference (ECC)

View full text Add to dashboard Cite

Abstract-In this paper, learning/repetitive control is proposed for improvement of existing feedback control loops for temperature regulation in buildings. A single zone office building is used as an example, with real weather data for Phoenix Arizona and realistic occupancy load schedules. Simulations have shown a decrease in the average set point tracking error of more than 50%, even without additional energy consumption. This can be achieved in situations where the load disturbances have enough repeatability and a repeatable-to-nonrepeatable ratio can be computed to determine if learning should be used and at which frequencies. Furthermore, the increased tightness in reference tracking could be used to lower energy consumption by moving the reference set point closer to the boundaries of the allowable temperature range.

show abstract

Section: Office Building Temperature Controlmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Learning/repetitive control for building systems with nearly periodic disturbances

Vinther

Chandan

Alleyne

2013

2013 European Control Conference (ECC)

View full text Add to dashboard Cite

show abstract

“…Different types of controllers were developed, such as a proportional-integral-derivate (PID)-type ILC applied for improved robotic operations [3,7]. Also, ILC was employed for dynamical system synchronization based on classical linear system theory [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Chaotic Synchronizing Systems with Zero Time Delay and Free Couple via Iterative Learning Control

Cheng

Chao

2018

Applied Sciences

View full text Add to dashboard Cite

This study aims to orchestrate a less restrictive learning controller by using the iteration-varying function, the so-called iterative learning controller (ILC), to synchronize two nonlinear systems with free time delay and couple free. The mathematical theories are proven rigorously and controllers are developed for system synchronization, and then an example is forged to demonstrate the effectiveness of synchronization by the designed ILC. The ILC is designed with a feed-forward based by the error dynamics between the two considered nonlinear drive and response systems. The stability of the synchronization facilitated by the designed ILC is ensured by rendering the convergence of an error dynamics that satisfied the Lyapunov function. The Lorenz system within a drive-response system is considered as one system that drives another for the demonstration of the effectiveness of the designed ILC to achieve synchronization and verified initial conditions. Simulations are conducted for the controlled Lorenz system, and the results validated well the expected capability of the designed ILC for synchronization and matched the proposed mathematical theory.

show abstract

“…Some advanced control theories including the robust control [19] , adaptive control [20] , iterative learning control [21] and neural networks control [22] , were presented to control precision positioning systems, which show appropriate level of performance. However, their applications are limited in the fast and precision wire clamp control because of the computationally expensive calculations [23] .…”

Section: Introductionmentioning

confidence: 99%

Development of a high speed and precision wire clamp with both position and force regulations

Liang

Wang

Tian

et al. 2017

Robotics and Computer-Integrated Manufacturing

View full text Add to dashboard Cite

Development of a high speed and precision wire clamp with both position and force regulations. . Permanent WRAP URL:http://wrap.warwick.ac.uk/94028 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.

show abstract

Combined $H _{\infty}$-Feedback Control and Iterative Learning Control Design With Application to Nanopositioning Systems

Cited by 77 publications

References 26 publications

Learning/repetitive control for building systems with nearly periodic disturbances

Learning/repetitive control for building systems with nearly periodic disturbances

Chaotic Synchronizing Systems with Zero Time Delay and Free Couple via Iterative Learning Control

Development of a high speed and precision wire clamp with both position and force regulations

Contact Info

Product

Resources

About