Effects of Kinematics Design on Tracking Performance of Model-Based Adaptive Control

In this paper, the relationships between the kinematics design and tracking performance of the model-based adaptive control are studied. For this purpose, the position tracking error convergences of three serial manipulators with joint types of RR, RP and PP are considered. The physical parameters and desired trajectories of these manipulators are assumed same for the proper comparison. Since the model-based adaptive control can completely account for nonlinear structure of robot dynamics, it has been preferred as control method.

[1]  Clément Gosselin,et al.  A Global Performance Index for the Kinematic Optimization of Robotic Manipulators , 1991 .

[2]  Zhang Yi,et al.  Advances in Neural Networks - ISNN 2005, Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part II , 2005, ISNN.

[3]  Madan M. Gupta,et al.  An adaptive switching learning control method for trajectory tracking of robot manipulators , 2006 .

[4]  Rafael Kelly,et al.  PD Control with Desired Gravity Compensation of Robotic Manipulators , 1997, Int. J. Robotics Res..

[5]  Wen-Jun Zhang,et al.  Trajectory tracking control of robot manipulators using a neural-network-based torque compensator , 1998 .

[6]  Francis L. Merat,et al.  Introduction to robotics: Mechanics and control , 1987, IEEE J. Robotics Autom..

[7]  Rafael Kelly,et al.  A tuning procedure for stable PID control of robot manipulators , 1995, Robotica.

[8]  René V. Mayorga,et al.  A kinematics performance index based on the rate of change of a standard isotropy condition for robot design optimization , 2005, Robotics Auton. Syst..

[9]  S. Shankar Sastry,et al.  Adaptive Control of Mechanical Manipulators , 1987, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[10]  Y. Zhigang,et al.  The establishment and reasoning of knowledge base system for mechanism kinematic schemes , 2004 .

[11]  Pingyuan Cui,et al.  Sliding Mode Control for Uncertain Nonlinear Systems Using RBF Neural Networks , 2005, ISNN.

[12]  Nabil Derbel,et al.  Fuzzy control of robot manipulators , 2002 .

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

[14]  Peng-Yung Woo,et al.  Global stability analysis for some trajectory-tracking control schemes of robotic manipulators , 2001, J. Field Robotics.

[15]  Zafer Bingul,et al.  Comparative study of performance indices for fundamental robot manipulators , 2006, Robotics Auton. Syst..