Indirect Adaptive Neural Control based on VLRs of nonlinear MIMO systems

For nonlinear (NL) Multi-Input Multi-Output (MIMO) systems with uncertain dynamics, we propose in this study an indirect adaptive neural control strategy using a neural network (NN) based on a variable learning rate (VLR). The developed VLRs are utilized to ensure rapid NN parameter adaption, learn the NE and the NC, reduce the tracking error and assure a faster output convergence. Through numerical simulations, the proposed VLRs effectiveness is demonstrated.