In this paper, stable indirect adaptive control with recurrent neural networks is presented for multi-input multi-output (MIMO) square non linear plants with unknown dynamics. The control scheme is made of a neural model and a neural controller based on fully connected RTRL networks. On-line weights updating law, closed loop performance, and boundedness of the neural network weights are derived...

In this paper a new approach to a neural network-based model reference adaptive intelligent controller is proposed. In this scheme, the intelligent supervisory loop is incorporated into the conventional model reference adaptive controller framework by utilizing an online growing multilayer back propagation neural network structure in parallel with it. The idea is to control the plant by convent...

IdentiÞcation of nonlinear stochastic processes via differential neural networks is discussed. A new ”dead-zone” type learning law for the weight dynamics is suggested. By a stochastic Lyapunov-like analysis the stability conditions for the identiÞcation error as well as for the neural network weights are established. The adaptive trajectory tracking using the obtained neural network model is r...

A back-propagation neural network is applied to a nonlinear self-tuning tracking problem. Traditional self-tuning adaptive control techniques can only deal with linear systems or some special nonlinear systems. The emerging back-propagation neural networks have the capability to learn arbitrary nonlinearity and show great potential for adaptive control applications. A scheme for combining back-...