A new approach for instantaneous pole placement with recurrent neural networks and its application in control of nonlinear time-varying systems

In this paper, we first show that online computation of feedback gain used for pole placement of nonlinear systems in recent years is not reliable, and then we present a new approach for instantaneous pole placement and apply it with dynamical recurrent neural networks for online computation of feedback gain. Because of high-speed convergence of neural network to feedback gain, we can apply this method for pole placement of nonlinear time-varying systems. One strategy for realization of this method is instantaneous linearization, as we do here by simulation. The advantage of the proposed method is a global uniform asymptotical exponential stability (GUAES) of closed-loop system around the equilibrium point.