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

A model with multiple inputs and multiple outputs is considered to simulate two links rigid manipulator. Its mathematical model is obtained by using Euler’s Lagrange method. A new intelligent scheme based on fixed stabilization technique is proposed in this paper for controlling the system. Comparison of Neural Network and Fuzzy Logic controller designed by utilizing this technique is also presented. The control law is determined such that the system output follows the reference trajectory. Controller design and simulation is done in MATLAB & Simulink. Simulation results validate the proposed controllers design and their comparison shows that Fuzzy Logic controller outperforms Neural Network controller.

[1]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Fuzzy Logic in Control Systems : Fuzzy Logic , 2022 .

[3]  Leila Fallah Araghi Neural Network Controller for Two links- Robotic Manipulator Control with Different Load , 2009 .

[4]  Wahyudi,et al.  Real time implementation of NARMA L2 feedback linearization and smoothed NARMA L2 controls of a single link manipulator , 2008, 2008 International Conference on Computer and Communication Engineering.

[5]  Sreenatha G. Anavatti,et al.  Fuzzy + PID Controller for Robot Manipulator , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).

[6]  Myung Jin Chung,et al.  A robust fuzzy logic controller for robot manipulators with uncertainties , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[7]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[8]  Peng-Yung Woo,et al.  Fuzzy logic control of robot manipulator , 1993, Proceedings of IEEE International Conference on Control and Applications.

[9]  Martin T. Hagan,et al.  Neural networks for control , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[10]  Yun Li,et al.  PID control system analysis, design, and technology , 2005, IEEE Transactions on Control Systems Technology.

[11]  M. Sheppard,et al.  Design and implementation of a stable fuzzy model reference learning controller applied to a rigid-link manipulator , 2004, 2004 IEEE International Conference on Industrial Technology, 2004. IEEE ICIT '04..