Advanced Robotics, ( ISI ), Volume (32), No (5), Year (2018-1) , Pages (231-241)

Title : ( Statistically optimized FOPID for output force control of SEAs )

Authors: Alireza Akbarzadeh Tootoonchi , Somayye Noruzi , Iman Kardan ,

Citation: BibTeX | EndNote


Fractional order PID (FOPID) controllers have recently found an increasing application in different fields of control. Comparing to traditional PID algorithms, FOPID controllers provide more flexibility and better performances. The simple and non-model-based structure of FOPID controllers has boosted their usage in real-world applications. However, due to having two more control parameters than regular PID controllers and the non-linear structure of FOPID controllers, the tuning procedure of these controllers is still a challenge. The authors of the present paper have recently proposed a Taguchi-based gain tuning algorithm for tuning of control parameters of FOPID controller. The present paper is an experimental evaluation of the proposed method. A custom made SEA, FUMLSEA, is used as the test bed in this study. Deriving a dynamic model of the FUM-LSEA, feed-forward terms are added to the controller to compensate for disturbances from motions of the output block. Optimal gains and orders of the controller are obtained through a set of experiments suggested by the Taguchi method. The Taguchi optimized controller is also compared to a Ziegler–Nichols tuned controller. The experimental results indicate 45% improvements in force tracking error.


, SEA, FOPID, Taguchi, tuning, ANOVA
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author = {Akbarzadeh Tootoonchi, Alireza and Noruzi, Somayye and Kardan, Iman},
title = {Statistically optimized FOPID for output force control of SEAs},
journal = {Advanced Robotics},
year = {2018},
volume = {32},
number = {5},
month = {January},
issn = {0169-1864},
pages = {231--241},
numpages = {10},
keywords = {SEA; FOPID; Taguchi; tuning; ANOVA},


%0 Journal Article
%T Statistically optimized FOPID for output force control of SEAs
%A Akbarzadeh Tootoonchi, Alireza
%A Noruzi, Somayye
%A Kardan, Iman
%J Advanced Robotics
%@ 0169-1864
%D 2018