Abstract
In this paper a multilayer neural-net (NN) controller is applied for tracking control of robotic manipulator, which is a nonlinear object having unknown and changeable parameters. Dynamics equations of a rigid manipulator are presented. The NN controller is used for compensating manipulator nonlinearities. The controller is realized in a form of a multilayer NN, which is nonlinear in the weights. The standard delta rule using backpropagation tuning is inadequate, so a term correcting the delta rule as well as a robustifying term is added. The presented control law and tuning algorithm are derived from the Lyapunov’s direct method. Results of the experiment are presented in this paper.
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