Model Free Adaptive Control for Robotic Manipulator Trajectory Tracking

This paper considers the problem of trajectory tracking of robotic manipulator system by using model free adaptive control (MFAC). The dynamic linearization technique is first introduced, and then the controller can be designed only by I/O data of the robotic manipulator system via MFAC approach, not includes any explicit model information. With some given conditions for controller parameters, the stability of MFAC for robotic manipulator system can be given. It is shown that the tracking error of robotic manipulator can converge to zero and the better tracking performance can be obtained. Simulation result for two-link robot manipulator further given to valid the effective of the proposed method.

[1]  Leila Notash,et al.  Adaptive sliding mode control with uncertainty estimator for robot manipulators , 2010 .

[2]  Z. Hou,et al.  The model-free learning adaptive control of a class of SISO nonlinear systems , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[3]  Shangtai Jin,et al.  A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems , 2011, IEEE Transactions on Control Systems Technology.

[4]  Shuzhi Sam Ge,et al.  Adaptive neural network control of robot manipulators in task space , 1997, IEEE Trans. Ind. Electron..

[5]  Long Cheng,et al.  Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model , 2009, Autom..

[6]  Genzhong Wu,et al.  Adaptive Iterative Learning Control for Robot Manipulators , 2012 .

[7]  Xuhui Bu,et al.  Robust model free adaptive control with measurement disturbance , 2012 .

[8]  Paolo Rocco,et al.  Revising the Robust-Control Design for Rigid Robot Manipulators , 2010 .

[9]  Jose Alvarez-Ramirez,et al.  On the PID tracking control of robot manipulators , 2001 .

[10]  Chintae Choi,et al.  Practical Nonsingular Terminal Sliding-Mode Control of Robot Manipulators for High-Accuracy Tracking Control , 2009, IEEE Transactions on Industrial Electronics.

[11]  Amar Goléa,et al.  Observer-based adaptive control of robot manipulators: Fuzzy systems approach , 2008, Appl. Soft Comput..

[12]  Xuhui Bu,et al.  Model free adaptive control with data dropouts , 2011, Expert Syst. Appl..

[13]  B. Siciliano,et al.  An asymptotically stable joint PD controller for robot arms with flexible links under gravity , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[14]  T. S. Chandar,et al.  Robust control of robot manipulators based on uncertainty and disturbance estimation , 2013 .

[15]  Maruthi R. Akella,et al.  Non-certainty equivalent adaptive control for robot manipulator systems , 2009, Syst. Control. Lett..

[16]  Paolo Rocco,et al.  Revising the Robust-Control Design for Rigid Robot Manipulators , 2007, IEEE Transactions on Robotics.

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

[18]  Caixia Liu,et al.  The Research of PLC and Touch Screen in the Erosion of Coating of Wind Turbine Blade , 2015 .

[19]  Jin S. Lee,et al.  Control of Flexible Joint Robot System by Backstepping Design Approach , 1999, Intell. Autom. Soft Comput..

[20]  Tie Zhang,et al.  Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics , 2013 .

[21]  Xuhui Bu,et al.  Model-Free Adaptive Control Algorithm with Data Dropout Compensation , 2012 .

[22]  Bu Xuhui,et al.  The robustness of model-free adaptive control with disturbance suppression , 2011 .

[23]  Shangtai Jin,et al.  Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems , 2011, IEEE Transactions on Neural Networks.

[24]  Chiang-Ju Chien,et al.  A One-Parameter Structure for Adaptive Iterative Learning Control of Robot Manipulators , 2007, 2007 IEEE 22nd International Symposium on Intelligent Control.