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

Abstract

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.

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

“…The dynamic model of the 3-DoF manipulator is expressed by Equations (6) through (25) with the corresponding dynamical parameter values detailed in Table 3 . The mathematical model of the robot was adopted from [ 56 ]. …”

“…For a quantitative performance analysis, the metrics obtained with the spiking PID controller were compared with those of a conventional PID controller using identical gains. Additionally, comparisons were made with a previously developed fuzzy controller and an ANFIS (adaptive neuro-fuzzy inference system) controller designed for this plant, as detailed in [ 56 ]. The evaluation encompassed both Cartesian and joint trajectories, and the corresponding metrics are presented in Table 4 and Table 5 .…”

Automated Symbolic Processes for Dynamic Modeling of Redundant Manipulator Robots

“…Deep understanding of the dynamic characteristics of a manipulator robot is fundamental for practical robot applications. Many of these applications require, for example, effective trajectory tracking capacities that-to improve their performance-should consider a dynamic model of a manipulator robot [9][10][11]. Knowledge and modeling of a manipulator robot's dynamics are crucial for the optimal performance of its control strategies (based on the robot model), such as inverse dynamic control, calculated torque control, and model predictive control [12][13][14].…”