Efficient Method for Inverse Dynamics of Robot Manipulators by using Adaptive-network-based Fuzzy Inference System

Rain, Thu; Dovgal, V. M.; Soe, Yan Naing

doi:10.1109/icieam.2019.8743014

Cited by 2 publications

(1 citation statement)

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“…In [23], ANFIS is employed to perform an input-output mapping of the inverse dynamic model of a 5-DoF manipulator robot (Intelbot). The ANFIS system is trained using θ) and a payload index (β) to allow the robot to work with varying loads.…”

Section: Of 30mentioning

confidence: 99%

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

Kern,

Marrero,

Urrea

2023

Processes

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This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position error and its derivative as inputs, employing a set of 9 control knowledge rules. The second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller, trained to learn the inverse dynamic model of the robot through a structured dataset. The research emphasizes the importance of accurate parameter tuning and data acquisition to achieve optimal control system performance. Extensive experimentation was conducted to evaluate the controllers’ performance in trajectory tracking and their response against external disturbances, such as load variations. The controllers exhibited remarkable precision and proficiency in tracking reference trajectories, with minimal deviations, overshoots, or oscillations. A quantitative analysis using performance indices such as root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE) further confirmed the controllers’ effectiveness. Notably, the ANFIS Controller consistently outperformed the Fuzzy Logic Controller, demonstrating superior precision in trajectory tracking. The study underscored the importance of selecting the right control method and obtaining high-quality training data. Challenges in parameter tuning for Fuzzy Logic Controllers and potential time constraints in training ANFIS were discussed. The findings have significant implications for advancing robotic control systems, particularly in the era of Industry 4.0.

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Section: Of 30mentioning

confidence: 99%