Adaptive deep neural network tracking controller augmented using only one-neuron hidden layers for nonlinear systems subject to high constraints and unknown uncertainties
by Hampu Ait Abbas
International Journal of Modelling, Identification and Control (IJMIC), Vol. 37, No. 3/4, 2021

Abstract: A new control strategy, adaptive tracking controller augmented using deep learning hybrid method, is proposed to achieve excellent tracking performances of nonlinear systems in the presence of structured and unstructured uncertainties. Since conventional controllers suffer from limitations due to the presence of these uncertainties, we contribute in this paper to demonstrate the feasibility of applying deep learning (DL) algorithm as an approximator for neglected dynamics and uncertain parameters. Thus, the key idea of the developed adaptive FLC augmented using DNN is to both replace the conventional controller Dcom and compensate adaptively the effect of modelling errors for Highly Uncertain NLSs. The weight adaptation rule of the DNN is derived from the Lyapunov stability analysis that ensures boundedness of the error signals. Simulations of the proposed adaptive controller based DNN are conducted then compared to the Dcom without DNN, PI controller, and adaptive controller based SHLNN to demonstrate its practical potential.

Online publication date: Thu, 07-Apr-2022

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