DNN-Based Adaptive Optimal Learning Controller for Uncertain Robot Systems

동적 신경망에 기초한 불확실한 로봇 시스템의 적응 최적 학습제어기

This paper presents an adaptive optimal learning controller for uncertian robot systems which makes use fo simple DNN(dynamic neural network) units to estimate uncertain parameters and learn the unknown desired optimal input. With the aid of a lyapunov function, it is shown that all that error signals in the system are bounded and the robot trajectory converges to the desired one globally exponentially. The effectiveness of the proposed controller is hsown by applying the controller to a 2-DOF robot manipulator.