NDLI: Adapting intelligence in robot using fuzzy logic

Content Provider	ACM Digital Library
Author	Sood, Vaishali
Abstract	Research in robot motion control offers research opportunities that will emulate human decision making capabilities to perfection in years to come. Autonomous robots roles are increasing in different aspects of engineering and everyday life. This paper describes an autonomous robot motion control system based on fuzzy logic Proportional Integral Derivative (PID) controller. Fuzzy rules are embedded in the controller to tune the gain parameters of PID and to make them helpful in real time applications. This paper discusses the design aspects of fuzzy PID controller for mobile robot that decrease rise time, remove steady sate error quickly and avoids overshoot. The performance of robot design has been verified with rule based evaluation using Matlab and results obtained have been found to be robust. Overall, the performances criteria in terms of its response towards rise time, steady sate error and overshoot have been found to be good.
Starting Page	158
Ending Page	161
Page Count	4
File Format	PDF
ISBN	9781450306355
DOI	10.1145/2007052.2007084
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-07-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Fuzzy logic Rise time Steady state error Pid controller Overshoot Robot design Artificial intelligence Robotics
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Adapting intelligence in robot using fuzzy logic