Tracking Control of Robotic Manipulators based on the All-Coefficient Adaptive Control Method

A multi-variable Golden-Section adaptive controller is proposed for the tracking control of robotic manipulators with unknown dynamics. With a small sample time, the unknown dynamics of the robotic manipulator are denoted equivalently by a characteristic model of a 2-order multi variable time-varying difference equation. The coefficients of the characteristic model change slowly with time and some of their valuable characteristic relationships emerge. Based on the characteristic model, an adaptive algorithm with a simple form for the control of robotic manipulators is presented, which combines the multi-variable Golden-Section adaptive control law with the weighted least squares estimation method. Moreover, a compensation neural network law is incorporated into the designed controller to reduce the influence of the coefficients estimation error on the control performance. The results of the simulations indicate that the developed control scheme is effective in robotic manipulator control.

[1]  Andrew H. Jazwinski,et al.  Adaptive filtering , 1969, Autom..

[2]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[3]  Charles P. Neuman,et al.  Discrete dynamic robot models , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

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

[5]  Lakmal Seneviratne,et al.  Adaptive Control Of Robot Manipulators , 1992, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  A. Zagorianos,et al.  ON LINE DISCRETE-TIME CONTROL OF INDUSTRIAL ROBOTS , 1992 .

[7]  Spyros G. Tzafestas,et al.  On line discrete-time control of industrial robots , 1991, Robotics Auton. Syst..

[8]  F. Lewis,et al.  Discrete-time neural net controller for a class of nonlinear dynamical systems , 1996, IEEE Trans. Autom. Control..

[9]  Shuzhi Sam Ge,et al.  Advanced control techniques of robotic manipulators , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[10]  Fuchun Sun,et al.  Neural adaptive tracking controller for robot manipulators with unknown dynamics , 2000 .

[11]  Ivica Kostanic,et al.  Principles of Neurocomputing for Science and Engineering , 2000 .

[12]  Wu Hongxin.  Study on the Stability of Multivariable All-Coefficient Adaptive Control System , 2000 .

[13]  Khashayar Khorasani,et al.  Experimental results on discrete-time nonlinear adaptive tracking control of a flexible-link manipulator , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[14]  Hideki Hashimoto,et al.  Position Control of Mobile Robot for Human-Following in Intelligent Space with Distributed Sensors , 2006 .

[15]  Yangmin Li,et al.  Dynamic Modeling and Adaptive Neural-Fuzzy Control for Nonholonomic Mobile Manipulators Moving on a Slope , 2006 .

[16]  Yingchun Wang,et al.  Intelligent control based on intelligent characteristic model and its application , 2007, Science in China Series F: Information Sciences.