Comparison of Neural Network and Fuzzy Logic Control for Nonlinear Model of Two Link Rigid Manipulator

Singh, Neetu; Panjwani, Bharti

doi:10.14257/ijca.2014.7.4.38

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…To sum up, FLC shows better control than the NARMA-L2 method and PID with less MSE for the pitch angle. However, the PID controller shows a better response to the yaw angle in comparison to the FLC and NARMA-L2 These findings are in agreement with the study conducted by Singh & Panjwani [7] where it was indicated that the use of FL method had an improved effect on the nonlinear two links of a rigid manipulator.…”

Section: Resultssupporting

confidence: 90%

“…Singh and Panjwani [7] compared the effectiveness of fuzzy logic and NARMA-L2 controllers to control two links of a rigid manipulator. This application mimics the human upper arm which is known as a robotic arm.…”

Section: Related Workmentioning

confidence: 99%

“…As such, the study suggested using FL and NARMA-L2 because of their ability to deal with the nonlinearity and parametric uncertainty. Another reason that led to choosing NARMA-L2 is its capability in controlling the path of such nonlinear system by eliminating dynamic behaviour [7]. The overall findings suggested that FL controller had the ability to achieve better outcomes than the NARMA-L2 controller.…”

Section: Related Workmentioning

confidence: 99%

“…To this end, intelligent system methods including fuzzy logic control (FLC) and artificial neural network (ANN) can be considered as one of the optimal solutions for achieving this goal. This is due to their capability for controlling nonlinear systems [6,7]. In this research, the Quanser two degrees of freedom helicopter (2-DOF) model was chosen, providing the rotation around the pitch and yaw axis.…”

Section: Introductionmentioning

confidence: 99%

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Helicopter Control Using Fuzzy Logic and Narma-L2 Techniques

Al-Fallooji¹,

Abbod²

2020

IJISA

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Helicopter instability is one of the most limitations that should be addressed in a nonlinear application. Accordingly, researchers are invited to design a robust and reliable controller to obtain a stable system and enhance its overall performance. The present study focuses on the use of the intelligent system in controlling the pitch and yaw angles. This lead to controlling the elevation and the direction of the helicopter. Further to the application of the Linear Quadratic Regulator (LQR) controller, this research implemented the Proportional Integral Derivative (PID), Fuzzy Logic Control (FLC), and Artificial Neural Network (ANN). The results show that FLC achieved a good controllability for both angles, particularly for the pitch angle in comparison to the nonlinear auto regressive moving average (NARMA-L2). Moreover, NARMA-L2 requires further improvement by using, for example, the swarm optimization method to provide better controllability. The PID controller, on the other hand, had a greater capability in controlling the yaw angle in comparison to the other controllers implemented. Accordingly, it is suggested that the integration of PID and FLC may lead to more optimal outcomes.

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Section: Resultssupporting

confidence: 90%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Helicopter Control Using Fuzzy Logic and Narma-L2 Techniques

Al-Fallooji¹,

Abbod²

2020

IJISA

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Comparative performance analysis of intelligent controllers for solar tracking system

Singh

Chauhan²,

Sharma³

2022

Materials Today: Proceedings

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Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

Tolossa,

Gunasekaran,

Halder

et al. 2024

Robotica

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This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller’s robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes.

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Comparison of Neural Network and Fuzzy Logic Control for Nonlinear Model of Two Link Rigid Manipulator

Abstract: Abstract

Cited by 7 publications

References 11 publications

Helicopter Control Using Fuzzy Logic and Narma-L2 Techniques

Helicopter Control Using Fuzzy Logic and Narma-L2 Techniques

Comparative performance analysis of intelligent controllers for solar tracking system

Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

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