On Using Recurrent Neural Network Models To Develop Direct Robust Adaptive Regulators For Unknown Systems

摘要

A direct nonlinear adaptive controller, to solve the regulation problem for unknown dynamical systems that are modeled by recurrent neural networks is discussed. The behavior of the closed-loop system is analyzed for the case in which the true system differs from the recurrent neural network due to the presence of a modeling error term. Generally, our adaptive regulator can guarantee uniform ultimate boundedness of the state and boundedness of all signals in the closed loop. Furthermore, the magnitude of the growth of the modeling error is considered unknown.