Echo State Neural Network Based State Feedback Control for SISO Afine Nonlinear Systems

Echo state network (ESTN) is a new recurrent neural networks (RNN) with a simpler training method. Based on ESTN, this paper address a state feedback control algorithm for a class of perturbed SISO nonlinear systems in the affine form. The control algorithm is implemented without aprior knowledge of the nonlinear system. The network weights can be tuned on line by the Recursive Least Squares (RLS) method without off line learning phase needed. The convergence and the Bounded Input Bounded Output (BIBO) stability of the ESTN controller are proven. Moreover, all signals involved in the closed loop are proven to be exponentially bounded and then the stability of the system. We have used the tracking problem of one-link rigid robotic manipulator system as an example to verify the effectiveness of the proposed method.