Velocity control for spherical robot using PI-fuzzy logic
— This paper presents the finding on designing the fuzzy logic controller and analysis of the step input test on the model and control design. The PI-type fuzzy logic controller (FLC) was designed to control the velocity of the rolling spherical robot. The spherical robot model was tested with s...
Main Authors: | , , |
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Format: | Conference or Workshop Item |
Language: | English English |
Published: |
Institute of Electrical and Electronics Engineers Inc.
2019
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Subjects: | |
Online Access: | http://irep.iium.edu.my/79633/ http://irep.iium.edu.my/79633/ http://irep.iium.edu.my/79633/ http://irep.iium.edu.my/79633/1/79633_Velocity%20Control%20for%20Spherical%20_complete.pdf http://irep.iium.edu.my/79633/2/79633_Velocity%20Control%20for%20Spherical%20_scopus.pdf |
Summary: | — This paper presents the finding on designing the
fuzzy logic controller and analysis of the step input test on the
model and control design. The PI-type fuzzy logic controller
(FLC) was designed to control the velocity of the rolling
spherical robot. The spherical robot model was tested with step
input signal to analysis the effectiveness of the designed Fuzzy
logic controller with 25-membership rule being constructed in
Fuzzy toolbox MATLAB using triangular membership
function and combination of gaussian and sigmoidal
membership function. The output scaling factor (output gain)
of FLC was tuned using Response Optimization toolbox and
Particle Swarm Optimization to improve the system
performance. Optimization using Matlab toolbox is done by
specified the desired step response characteristics while in
PSO, minimizing the Integral Absolute Error (IAE) is used as
on objective of the optimization. The combined membership
function shows better performance with less 8% overshoot, rise
time less than 2s and settle at less than 3s after the response
optimization process. Meanwhile, the PSO manage to tune the
gain to reduce the IAE but contain large overshoot and longer
settling time. |
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