Globally stable neural robot control capable of payload adaptation

A set of four separate three-layer-perceptrons (3LP) learns matrix components representing mass-coupling, coriolis, viscose, and static friction forces in an inverse robot model as a function of the robot´s current position and payload. Based on training with point-to-point trajectories between random start- and goal-points that are executed with various load masses, the inverse model gradually acquires high precision over the entire robot working range. A controller using the 3LP-networks inside the feedback loop is shown to be globally L_∞-stable. The stability criterion is based on guaranteed model error bounds for the complete continuous working range and for all load masses in a certain range. Results of the stability analysis and of load-adaptive control are demonstrated for a realistically simulated planar 4-joint-machine.