Neuro-Fuzzy System for 3-DOF Parallel Robot Manipulator

Planar Parallel manipulators (PPMs) are widely used these days, as they have many advantages compared to their serial counterparts. However, their inverse and direct kinematics are hard to obtain, due to the complexity of the manipulators’ behavior. Therefore, this paper provides a comparative analysis for two methods that were used to obtain the inverse kinematics of a 3-RRR manipulator. Instead of the conventional algebraic and graphical methods used for attaining the mathematical models for such manipulators, an adaptive neuro-fuzzy inference structure (AFNIS) model was alternatively employed. It is then compared with a traditional neural network (NN) model for the same manipulator in order to ascertain which model is better in angles prediction, training time and overall performance. The data points used for both training the models and testing their performance are acquired from motion studies in SolidWorks.

[1]  Sundarapandian Vaidyanathan,et al.  Fractional Order Control and Synchronization of Chaotic Systems , 2017, Studies in Computational Intelligence.

[2]  M. Omizo,et al.  Modeling , 1983, Encyclopedic Dictionary of Archaeology.

[3]  Badler,et al.  Techniques for Generating the Goal-Directed Motion of Articulated Structures , 1982, IEEE Computer Graphics and Applications.

[4]  Bijan Shirinzadeh,et al.  Direct Kinematics and Analytical Solution to 3RRR Parallel Planar Mechanisms , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[5]  C. S. George Lee,et al.  Robot Arm Kinematics, Dynamics, and Control , 1982, Computer.

[6]  Quanmin Zhu,et al.  Complex System Modelling and Control Through Intelligent Soft Computations , 2016, Studies in Fuzziness and Soft Computing.

[7]  Yong-Lin Kuo,et al.  Experimental and Numerical Study on the Semi-Closed Loop Control of a Planar Parallel Robot Manipulator , 2014 .

[8]  Ahmad Taher Azar,et al.  Path Planning Control for 3-Omni Fighting Robot Using PID and Fuzzy Logic Controller , 2019, AMLTA.

[9]  Peter Corke.  Robot Arm Kinematics , 2011 .

[10]  Quanmin Zhu,et al.  Control design approaches for parallel robot manipulators: a review , 2017, Int. J. Model. Identif. Control..

[11]  Li-Chun T. Wang,et al.  Numerical direct kinematic analysis of fully parallel linearly actuated platform type manipulators , 2002, J. Field Robotics.

[12]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[13]  Wisama Khalil,et al.  Modeling, Identification and Control of Robots , 2003 .

[14]  M. Spong,et al.  Robot Modeling and Control , 2005 .

[15]  Rabie A. Ramadan,et al.  A Fuzzy Approach of Sensitivity for Multiple Colonies on Ant Colony Optimization , 2016, SOFA.

[16]  Nguyen Truong Thinh,et al.  Solving inverse kinematics of delta robot using ANFIS , 2015, 2015 15th International Conference on Control, Automation and Systems (ICCAS).

[17]  Ahmad Taher Azar,et al.  A Novel Actuator Fault-tolerant Control Strategy of DFIG-based Wind Turbines Using Takagi-Sugeno Multiple Models , 2018 .

[18]  Dervis Karaboga,et al.  Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey , 2018, Artificial Intelligence Review.

[19]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[20]  Wyatt S. Newman.  Robot Arm Kinematics , 2017 .

[21]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[22]  Larbi El Bakkali,et al.  Analysis and Optimum Kinematic Design of a Parallel Robot , 2017 .