Fixed-point computing with variable fraction length for motion profile generation

S. M. Dehghan; M. Mansourian

doi:10.1109/PEDSTC.2016.7556842

Fixed-point computing with variable fraction length for motion profile generation

S. M. Dehghan, M. Mansourian

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

2 Citations (Scopus)

Abstract

This paper proposes a new fixed-point computing method for motion profile generation. When parameters have wide ranges, floating-point computing usually is used for accurate computation; however, this method increases execution time. In the proposed method, by considering input data, optimum fraction lengths are determined for different parameters. In this way, the fixed-point computing offers an accurate computation in a minimum execution time. Experimental results show the performance of the proposed method.

Original language	English
Title of host publication	7th Power Electronics, Drive Systems and Technologies Conference, PEDSTC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	82-86
Number of pages	5
ISBN (Electronic)	9781509003754
ISBN (Print)	9781509003761
DOIs	https://doi.org/10.1109/PEDSTC.2016.7556842
Publication status	Published - 1 Sept 2016
Externally published	Yes
Event	7th Power Electronics, Drive Systems and Technologies Conference - Tehran, Iran, Islamic Republic of Duration: 16 Feb 2016 → 18 Feb 2016 Conference number: 7

Conference

Conference	7th Power Electronics, Drive Systems and Technologies Conference
Abbreviated title	PEDSTC 2016
Country/Territory	Iran, Islamic Republic of
City	Tehran
Period	16/02/16 → 18/02/16

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10.1109/PEDSTC.2016.7556842Licence: Unspecified

Cite this

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title = "Fixed-point computing with variable fraction length for motion profile generation",

abstract = "This paper proposes a new fixed-point computing method for motion profile generation. When parameters have wide ranges, floating-point computing usually is used for accurate computation; however, this method increases execution time. In the proposed method, by considering input data, optimum fraction lengths are determined for different parameters. In this way, the fixed-point computing offers an accurate computation in a minimum execution time. Experimental results show the performance of the proposed method.",

keywords = "fixed-point, floating-point, Motion control, motion profile",

author = "Dehghan, {S. M.} and M. Mansourian",

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note = "7th Power Electronics, Drive Systems and Technologies Conference, PEDSTC 2016 ; Conference date: 16-02-2016 Through 18-02-2016",

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Dehghan, SM & Mansourian, M 2016, Fixed-point computing with variable fraction length for motion profile generation. in 7th Power Electronics, Drive Systems and Technologies Conference, PEDSTC 2016., 7556842, Institute of Electrical and Electronics Engineers Inc., pp. 82-86, 7th Power Electronics, Drive Systems and Technologies Conference, Tehran, Iran, Islamic Republic of, 16/02/16. https://doi.org/10.1109/PEDSTC.2016.7556842

Fixed-point computing with variable fraction length for motion profile generation. / Dehghan, S. M.; Mansourian, M.
7th Power Electronics, Drive Systems and Technologies Conference, PEDSTC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 82-86 7556842.

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

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AU - Mansourian, M.

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PY - 2016/9/1

Y1 - 2016/9/1

N2 - This paper proposes a new fixed-point computing method for motion profile generation. When parameters have wide ranges, floating-point computing usually is used for accurate computation; however, this method increases execution time. In the proposed method, by considering input data, optimum fraction lengths are determined for different parameters. In this way, the fixed-point computing offers an accurate computation in a minimum execution time. Experimental results show the performance of the proposed method.

AB - This paper proposes a new fixed-point computing method for motion profile generation. When parameters have wide ranges, floating-point computing usually is used for accurate computation; however, this method increases execution time. In the proposed method, by considering input data, optimum fraction lengths are determined for different parameters. In this way, the fixed-point computing offers an accurate computation in a minimum execution time. Experimental results show the performance of the proposed method.

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KW - floating-point

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Y2 - 16 February 2016 through 18 February 2016

ER -

Fixed-point computing with variable fraction length for motion profile generation

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