Neural net robot controller with guaranteed tracking performance

A neural net (NN) controller for a general serial-link robot arm is developed. The NN has two layers so that linearity in the parameters holds, but the “net functional reconstruction error” is taken as nonzero. The structure of the NN controller is derived using a filtered error/passivity approach. It is shown that standard backpropagation, when used for real time closed-loop control, can yield unbounded NN weights if (1) the net cannot exactly reconstruct a certain required control function, or (2) there are bounded unknown disturbances in the robot dynamics. An online weight tuning algorithm including a correction term to backpropagation guarantees tracking as well as bounded weights. The notions of a passive NN and a robust NN are introduced