Initial-Rectification Neuro-Adaptive Iterative Learning Control for Robot Manipulators With Input Deadzone and Nonzero Initial Errors

An initial-rectification adaptive iterative learning control scheme is proposed to solve the angle tracking problem of robot manipulators with input deadzone under nonzero initial errors. Lyapunov approach utilized design controller. First, auxiliary reference signal constructed overcome obstacle caused by errors during ILC design. Second, strategy and robust are adopted for dealing nonlinearity. In addition, neural network applied approximate uncertainties. The stability closed-loop robotic system rigorously proven theoretical analysis. end, numerical simulation results provided verify effectiveness scheme.