Grey-box modelling and fuzzy logic control of a Leader-Follower robot manipulator system: A hybrid Grey Wolf-Whale Optimisation approach
This study presents the development of a grey-box modelling approach and fuzzy logic control for real time trajectory control of an experimental four degree-of-freedom Leader–Follower Robot (LFR) manipulator system using a hybrid optimisation algorithm, known as Grey Wolf Optimiser (GWO) - Whale Op...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
ISA - Instrumentation, Systems, and Automation Society
2022
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/102719/1/ZaharuddinMohamed2022_GreyBoxModellingandFuzzyLogicControl.pdf http://eprints.utm.my/id/eprint/102719/ http://dx.doi.org/10.1016/j.isatra.2022.02.023 |
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| Summary: | This study presents the development of a grey-box modelling approach and fuzzy logic control for real time trajectory control of an experimental four degree-of-freedom Leader–Follower Robot (LFR) manipulator system using a hybrid optimisation algorithm, known as Grey Wolf Optimiser (GWO) - Whale Optimisation Algorithm (WOA). The approach has advantages in achieving an accurate model of the LFR manipulator system, and together with a better trajectory tracking performance. In the first instance, the white box model is formed by modelling the dynamics of the follower manipulator using the Euler–Lagrange formulation. This white-box model is then improved upon by re-tuning the model's parameters using GWO-WOA and experimental data from the real LFR manipulator system, thus forming the grey-box model. A minimum improvement of 73.9% is achieved by the grey-box model in comparison to the white-box model. In the latter part of this investigation, the developed grey-box model is used for the design, tuning and real-time implementation of a fuzzy PD＋I controller on the experimental LFR manipulator system. A 78% improvement in the total mean squared error is realised after tuning the membership functions of the fuzzy logic controller using GWO-WOA. Experimental results show that the approach significantly improves the trajectory tracking performance of the LFR manipulator system in terms of mean squared error, steady state error and time delay. |
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