Neural network system for online controller adaptation and its application to underwater robot

Abstract

Describes a neural network system which executes identification of robot dynamics and controller adaptation in parallel with robot control. The system consists of two parts: real-world part and imaginary-world part. The real-world part is a feedback control system for the actual robot. In the imaginary-world part, the model of robot and the controller are adjusted continuously in order to deal with the change of dynamic property caused by disturbance and so on. The system is designed to be suitable for a computer system with parallel processing ability. In the paper, adaptability of the controller system is investigated by heading keeping and path following experiments on the condition that unknown disturbances are given to the robot.