Adaptive recursive control of a soil testing load frame

Thomas Milthorp; Victor Becerra; William Holderbaum; Benjamin Hutt

doi:10.1109/RAMECH.2011.6070464

Adaptive recursive control of a soil testing load frame

Thomas Milthorp, Victor Becerra, William Holderbaum, Benjamin Hutt

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Recursive Learning Control (RLC) has the potential to significantly reduce the tracking error in many repetitive trajectory applications. This paper presents an application of RLC to a soil testing load frame where non-adaptive techniques struggle with the highly nonlinear nature of soil. The main purpose of the controller is to apply a sinusoidal force reference trajectory on a soil sample with a high degree of accuracy and repeatability. The controller uses a feedforward control structure, recursive least squares adaptation algorithm and RLC to compensate for periodic errors. Tracking error is reduced and stability is maintained across various soil sample responses.

Original language	English
Title of host publication	2011 IEEE 5th International Conference on Robotics, Automation and Mechatronics (RAM)
Place of Publication	Piscataway
Publisher	IEEE/ IAPR
Pages	102-107
ISBN (Print)	9781612842523
DOIs	https://doi.org/10.1109/RAMECH.2011.6070464
Publication status	Published - 1 Sept 2011

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10.1109/RAMECH.2011.6070464

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title = "Adaptive recursive control of a soil testing load frame",

abstract = "Recursive Learning Control (RLC) has the potential to significantly reduce the tracking error in many repetitive trajectory applications. This paper presents an application of RLC to a soil testing load frame where non-adaptive techniques struggle with the highly nonlinear nature of soil. The main purpose of the controller is to apply a sinusoidal force reference trajectory on a soil sample with a high degree of accuracy and repeatability. The controller uses a feedforward control structure, recursive least squares adaptation algorithm and RLC to compensate for periodic errors. Tracking error is reduced and stability is maintained across various soil sample responses.",

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AU - Milthorp, Thomas

AU - Becerra, Victor

AU - Holderbaum, William

AU - Hutt, Benjamin

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PY - 2011/9/1

Y1 - 2011/9/1

N2 - Recursive Learning Control (RLC) has the potential to significantly reduce the tracking error in many repetitive trajectory applications. This paper presents an application of RLC to a soil testing load frame where non-adaptive techniques struggle with the highly nonlinear nature of soil. The main purpose of the controller is to apply a sinusoidal force reference trajectory on a soil sample with a high degree of accuracy and repeatability. The controller uses a feedforward control structure, recursive least squares adaptation algorithm and RLC to compensate for periodic errors. Tracking error is reduced and stability is maintained across various soil sample responses.

AB - Recursive Learning Control (RLC) has the potential to significantly reduce the tracking error in many repetitive trajectory applications. This paper presents an application of RLC to a soil testing load frame where non-adaptive techniques struggle with the highly nonlinear nature of soil. The main purpose of the controller is to apply a sinusoidal force reference trajectory on a soil sample with a high degree of accuracy and repeatability. The controller uses a feedforward control structure, recursive least squares adaptation algorithm and RLC to compensate for periodic errors. Tracking error is reduced and stability is maintained across various soil sample responses.

U2 - 10.1109/RAMECH.2011.6070464

DO - 10.1109/RAMECH.2011.6070464

M3 - Conference contribution

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EP - 107

BT - 2011 IEEE 5th International Conference on Robotics, Automation and Mechatronics (RAM)

PB - IEEE/ IAPR

CY - Piscataway

ER -

Adaptive recursive control of a soil testing load frame

Abstract

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