A Design High Impact Lyapunov Fuzzy PD-Plus-Gravity Controller with Application to Rigid Manipulator

The control problem for manipulators is to determine the joint inputs required to case the end- effector execute the commanded motion. The nonminimum phase characteristic of a rigid manipulator makes the design of stable controller that ensure stringent tracking requirements a highly nontrivial and challenging problem. A useful controller in the computed torque family is the PD-plus-gravity controller. Methodology. To compensate the dynamic parameters, fuzzy logic methodology is used and applied parallel to this method. when the arm is at rest, the only nonzero terms in the dynamic is the gravity. Proposed method can cancels the effects of the terms of gravity. In this case inorder to decrease the error and satteling time, higher gain controller is design and applied to nonlinear system.

[1]  Farzin Piltan,et al.  Adaptive MIMO Fuzzy Compensate Fuzzy Sliding Mode Algorithm: Applied to Second Order Nonlinear System , 2011 .

[2]  Bruno,et al.  Springer Handbook of Robotics || Rehabilitation and Health Care Robotics , 2008 .

[3]  Thomas R. Kurfess,et al.  Robotics And Automation Handbook , 2019 .

[4]  Fan-Tien Cheng,et al.  Study and resolution of singularities for a 6-DOF PUMA manipulator , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[5]  Farzin Piltan,et al.  Performance-Based Adaptive Gradient Descent Optimal Coefficient Fuzzy Sliding Mode Methodology , 2012 .

[6]  Farzin Piltan,et al.  Design Error-based Linear Model-free Evaluation Performance Computed Torque Controller , 2012 .

[7]  Farzin Piltan,et al.  Artificial Control of PUMA Robot Manipulator: A-Review of Fuzzy Inference Engine And Application to Classical Controller. , 2011 .

[8]  Mark W. Spong,et al.  Robot dynamics and control , 1989 .

[9]  Farzin Piltan,et al.  Design of Model Free Adaptive Fuzzy Computed Torque Controller: Applied to Nonlinear Second Order System , 2011 .

[10]  Abdul Rahman Ramli,et al.  Design On-Line Tunable Gain Artificial Nonlinear Controller , 2011 .

[11]  Farzin Piltan,et al.  Design Artificial Nonlinear Controller Based on Computed Torque like Controller with Tunable Gain , 2011 .

[12]  Farzin Piltan,et al.  Novel Nonlinear Controller Applied to Robot Manipulator : Design New Feedback Linearization Fuzzy Controller With Minimum Rule Base Tuning Method , 2012 .

[13]  A. Vivas,et al.  Predictive functional control of a PUMA robot , 2005 .

[14]  Farzin Piltan,et al.  GDO Artificial Intelligence-Based Switching PID Baseline Feedback Linearization Method: Controlled PUMA Workspace , 2012 .

[15]  Duy Nguyen-Tuong,et al.  Computed torque control with nonparametric regression models , 2008, 2008 American Control Conference.

[16]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .