Asymptotic Tracking Control for Uncertain MIMO Systems: A Biologically Inspired ESN Approach

In this study, a biologically inspired echo state network (ESN)-based method is established for the asymptotic tracking control of a class of uncertain multi-input multi-output (MIMO) systems. By mimicking the characters of real biological systems, a diversified multiclustered echo state network (DMCESN) is proposed in this work and then it is applied to deal with the modeling uncertainties and coupling nonlinearities in the control systems. Different from the most existing neural network (NN)-based control methods that only ensure the uniform ultimate boundedness result, the proposed method can allow the tracking error to achieve asymptotic convergence through rigorous theoretical analysis. The effectiveness of the proposed method is also confirmed by numerical simulation by comparing with multilayer feedforward network-based control scheme and traditional ESN-based control, admitting better tracking performance of the proposed control.