High-precision contour control of industrial robot arm by neural network compensation with learning uncertainties

Uncertainties are the main reasons of deterioration of contour control of industrial articulated robot arms. In this paper, a high-precision contour control method was proposed to overcome some main uncertainties, such as system delay dynamics, interference between robot links, friction, and so on. Firstly each considered factor of uncertainty was introduced briefly. Then according to the model of an industrial articulated robot arm, the construction of a Gaussian neural network controller, with consideration of system delay dynamics, interference between robot links and friction, was explained in detail. Finally, through experiments and simulation, the effectiveness of the proposed method was verified. Furthermore, based on the results, it was shown that the Gaussian neural network controller can be also adapted for the various kinds of frictions and high-speed motion of an industrial articulated robot arm.