A fuzzy logic controller for a three links robotic manipulator

Abdul-Sadah, Ali Muhssen; Raheem, Kamal M. H.; Altufaili, Mohammed Mahdi Salih

doi:10.1063/5.0066871

Cited by 60 publications

(3 citation statements)

References 16 publications

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“…Contrary to traditional control methods, fuzzy logic-based schemes provide a more effective method for the analysis and control of nonlinear, time-varying systems that are relatively complex and difficult to model mathematically [34]. The Fuzzy Logic Controller allows to express the general characteristics of a non-linear system through linguistic expressions by the creation of IF-THEN rules [35].…”

Section: Fuzzy Logic Controllermentioning

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: Fuzzy Logic Controllermentioning

confidence: 99%

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

Kern,

Marrero,

Urrea

2023

Processes

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“…Fuzzy input, represented by an error in the fuzzy logic controller, regulates the performance of a system. The fuzzy controller uses variable input-output information to control the joint speed and position of the robot mechanisms [8,14]. Using the advantageous features of the fuzzy controller scheme, it is also possible to control systems with a wider range of reference and interference signals [15].…”

Section: Introductionmentioning

confidence: 99%

Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

Ersin,

Yaz

2024

Journal of Sensors

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In this study, a wearable exoskeleton arm was designed and controlled with different control methods to help people with muscle disorders in their arms and support treatment. The developed robot arm was transferred to Simulink software with the Simmechanics application. Two electromyography (EMG) muscle sensors and the ADXL335 position and acceleration sensors attach to the human arm’s biceps and triceps muscle areas. As the human moved the arm, data were obtained from the EMG muscle sensors and the ADXL335 position and acceleration sensor. The received data were first trained with the fuzzy logic algorithm. The same data were then trained with machine learning algorithms in Simulink software. It has been determined that the best result is the quadratic support vector machine (SVM) algorithm. The fuzzy logic algorithm trained with the PID controller block and the received sensor data have been added to the degrees of freedom regions that will enable rotation in the block diagram of the previously exported system. Later, the fuzzy logic block was removed and the machine learning algorithm, the quadratic SVM algorithm, was added. The designed system was operated with two different control systems, and the control algorithm closest to the human arm movement was determined. In addition, each part of the system, whose design was prepared, was removed and assembled separately with a 3D printer. ESP32 microcontroller development board was used to control the system, and it was run in real-time with EMG muscle sensors and position sensors.

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“…The results of the study illustrated that fractional operators increase the degree of freedom and robustness of the SRFOFPID controller. A fuzzy logic controller for a 3-DOF robot manipulator was presented in [9]. The simulation results demonstrated that PID produces better transient parameters.…”

Section: Introductionmentioning

confidence: 99%

Fractional Order Fuzzy Like PID Controller Design for Three Links Rigid Robot Manipulator

2022

IJCCCE

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Four Fractional/Integer Order Fuzzy Proportional Integral Derivative controller structures are designed in this study to successfully control a nonlinear, coupled, multi-input, multi-output, three-link rigid robotic manipulator system. The performance of Fractional Order Fuzzy Proportional Integral Derivative and Integer Order Fuzzy Proportional Integral Derivative controllers is evaluated for reference trajectory tracking, changing beginning circumstances, disturbance rejection, and model uncertainty. These controllers' parameters are tuned using a meta-heuristic optimization approach called the most valuable player algorithm for the objective function, which is defined as the integral of the time-squared error. Simulation results show that the suggested Fractional Order Fuzzy Proportional Integral Derivative controllers outperform Integer Order Fuzzy Proportional Integral Derivative controllers for tracking performance, stability, and robustness for all structures. Fractional Order Fuzzy Proportional Derivative Fractional Order Proportional Integral Derivative controller is the best one for trajectory tracking, disturbances rejection, and parameter variation with the least integral of time square error equal to 2.7420×10-6, 3.4×10-3 and 2.0108×10-4 respectively and the response of the angular position for all links for trajectory tracking has minimum settling time which is equal to 0.0290 s for the first link, 0.0160 s for the second link and 0.0050 s for the third link. When the initial condition is changed, the One Block Fractional Order Fuzzy Proportional Integral Derivative controller is the best one, since the integral of time square error is minimum and equal to 1.6253×10-4. Index Terms— Fractional order controller, Fuzzy logic, Most valuable player algorithm, PID controller, Robotic manipulator.

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A fuzzy logic controller for a three links robotic manipulator

Cited by 60 publications

References 16 publications

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

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

Implementation and Comparison of Wearable Exoskeleton Arm Design with Fuzzy Logic and Machine Learning Control

Fractional Order Fuzzy Like PID Controller Design for Three Links Rigid Robot Manipulator

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