Citation

Huang, Hanyi and Arogbonlo, Adetokunbo and Yu, Samson and Kwek, Lee Chung and Lim, Chee Peng (2024) Adaptive neuro-fuzzy inference system based active force control with iterative learning for trajectory tracking of a biped robot. International Journal of Systems Science. pp. 1-18. ISSN 0020-7721

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Abstract

We investigate the integration of the active force control (AFC) scheme and the adaptive neuro-fuzzy inference system (ANFIS) as an intelligent controller algorithm to address trajectory-trackingproblems in robotic systems, The AFC-ANFIS model exploits iterative learning (IL) to improve thetracking performance based on its trained model. The ANFIS parameters are tuned using both parti-cle swarm optimisation (PSO) and beetle antennae search (BAS) algorithms. The simulation results oftwo different robots, i.e. a five-link biped robot and a PUMA 560 robot arm, indicate that the proposedAFC-ANFIS controller performs well for trajectory tracking and disturbances rejection. The AFC-ANFISperformance is evaluated and compared with those from other controllers using the average track-ing error (ATE) metric. The comparative results reveal that AFC-ANFIS offers a viable approach witha rapid training process to undertaking trajectory tracking and disturbance rejection tasks.

Item Type:	Article
Uncontrolled Keywords:	Neuro-fuzzy inference system (ANFIS
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Dec 2024 00:48
Last Modified:	03 Dec 2024 00:48
URII:	http://shdl.mmu.edu.my/id/eprint/13147

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