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| DOI：10.1007/s11768-010-8038-x |
| Received:March 13, 2008Revised:November 13, 2008 |
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| Adaptive RBF neural network control of robot with actuator nonlinearities |
| Jinkun LIU,Yu LU |
| (School of School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics) |
| Abstract: |
| In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities
is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator.
Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis
function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an
adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming
training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the
closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole
control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a
general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness
of the designed scheme and the theoretical discussion. |
| Key words: Adaptive control RBF neural network Actuator nonlinearity Robot manipulator Deadzone |
