A multirate sampling structure for adaptive robot control using a neurocompensator

A novel multirate sampling structure is developed for adaptive control of robot manipulators. This control structure has the implementation advantage that the parameter adaptation in a control action is independent of the feedforward torque computation of the same control action. A fast sampling rate can be achieved by applying this structure. The parameter adaptation element in this structure is realized by a neurocompensator which is implemented using the ADALINE algorithm. Instead of the normal delta-learning rule used in ADALINE, a special learning rule is derived from the Lyapunov method to adjust the weights of the neurocompensator. Both system stability and error convergence can then be guaranteed. Simulation studies on a two-link manipulator show that the control system maintains very good trajectory tracking performance even in the presence of large parameter uncertainty and external disturbance. The satisfactory control performance of this approach is also demonstrated by experimental results for a one-link robot.

[1]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[2]  Frank L. Lewis,et al.  Control of Robot Manipulators , 1993 .

[3]  Duc Truong Pham,et al.  Identification of linear and nonlinear dynamic systems using recurrent neural networks , 1993, Artif. Intell. Eng..

[4]  Tarun Khanna,et al.  Foundations of neural networks , 1990 .

[5]  Marcelo H. Ang,et al.  Performance of a neuro-model-based robot controller: adaptability and noise rejection , 1992 .

[6]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[7]  Graham C. Goodwin,et al.  Adaptive computed torque control for rigid link manipulators , 1986, 1986 25th IEEE Conference on Decision and Control.

[8]  John J. Craig Zhu,et al.  Introduction to robotics mechanics and control , 1991 .

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

[10]  Bernard C. Jiang,et al.  Using neural networks for robot positioning control , 1993 .

[11]  Weiping Li,et al.  Indirect adaptive robot control , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

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

[13]  Weiping Li,et al.  Composite adaptive control of robot manipulators , 1989, Autom..

[14]  Steven Dubowsky,et al.  The application of model-referenced adaptive control to robotic manipulators , 1979 .

[15]  Alan S. Morris,et al.  Neuro-adaptive control of robotic manipulators , 1993, Robotica.

[16]  J. Slotine,et al.  On the Adaptive Control of Robot Manipulators , 1987 .

[17]  Roberto Horowitz,et al.  Stability analysis of an adaptive controller for robotic manipulators , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[18]  Masayoshi Tomizuka,et al.  An adaptive control scheme for mechanical manipulators. Compensation of nonlinearity and decoupling control , 1986 .

[19]  Edgar Sanchez-Sinencio,et al.  Artificial Neural Networks: Paradigms, Applications, and Hardware Implementations , 1994 .

[20]  Robert J. Schilling,et al.  Fundamentals of robotics - analysis and control , 1990 .

[21]  B. Anderson Exponential stability of linear equations arising in adaptive identification , 1977 .

[22]  Peter H. Meckl,et al.  An analytical comparison of a neural network and a model-based adaptive controller , 1993, IEEE Trans. Neural Networks.