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Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

Published online by Cambridge University Press:  19 September 2024

Tesfaye Deme Tolossa ,
Manavaalan Gunasekaran ,
Kaushik Halder ,
Hitendra Kumar Verma ,
Shyam Sundar Parswal ,
Nishant Jorwal ,
Felix Orlando Maria Joseph Open the ORCID record for Felix Orlando Maria Joseph [Opens in a new window]  and
Yogesh Vijay Hote
Tesfaye Deme Tolossa
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Manavaalan Gunasekaran
Affiliation:
Department of Electrical and Electronics Engineering, CIT, Combature, Tamil Nadu, India
Kaushik Halder
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Hitendra Kumar Verma
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Shyam Sundar Parswal
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Nishant Jorwal
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Felix Orlando Maria Joseph*
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Yogesh Vijay Hote
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
*
Corresponding author: Felix Orlando Maria Joseph; Email: [email protected]
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Abstract

This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller’s robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes.

Keywords

wheeled mobile robotmembership functionparticle swarm optimizationfuzzy logic controllerpath trackingexperimental evaluation
Type
Research Article
Information
Robotica , First View , pp. 1 - 24
Copyright
© The Author(s), 2024. Published by Cambridge University Press.

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