A Repetitive Segmented Training Neural Network Controller with Applications to Robot Visual Servoing

P. Jiang and Y.Q. Chen


Neural networks, iterative learning control, nonlinear control, visual


The authors design a neural network controller for a nonlinear system with uncertainties that are invariant or repetitive over repeatedly executed tasks. The training of the neural networks is carried out iteratively as the task repeats. The desired trajectory is segmented, and for each segment a local neural network is constructed to keep the training errors within a permitted region. Meanwhile, the training is segment-wise progressively from the starting segment to the ending one. The accurate tracking of the whole desired trajectory is thus accomplished in a step-by-step or segment-bysegment manner, which means that the training of the second segment starts after the first segment tracking has reached a desired accuracy level. To guarantee the uniform boundedness of the pointwise training, a projection-type learning update law and deadzone technique are proposed. As an application example, a robot visual servoing control problem with an uncalibrated camera is considered. The effectiveness of the proposed neural network controller with repetitive segmented training is demonstrated by simulations of a typical robot visual servoing task, robot movement learning from demonstration.

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