A new adaptive deep neural network controller based on sparse auto‐encoder for the antilock bracking system systems subject to high constraints

• Published in: Asian journal of control Vol. 23; no. 5; pp. 2145 - 2156
• Main Author: Ait Abbas, Hamou
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.09.2021
• Subjects: Adaptive algorithms; Adaptive control; adaptive DNN observer & controller; Antilock braking systems; Artificial neural networks; Coders; Compensators; Control algorithms; Control systems design; Control theory; Controllers; Deep learning; deep learning hybrid strategy; deep neural network; Error signals; Feasibility; Feedback control; Machine learning; Neural networks; Output feedback; Parameter uncertainty; parametric uncertainty; robustness test; Tracking errors; unmodeled dynamics
• ISSN: 1561-8625; 1934-6093
• DOI: 10.1002/asjc.2629

We contribute in the current paper to extend the universal function approximation property of deep learning hybrid strategy to design both : (a) adaptive deep neural network observer to estimate derivatives of the tracking error dynamics (b) and robust deep neural network (DNN) output feedback control scheme (OFCS) that will overcome successfully effects of both parametric variations and modeling errors. First, the designed controller employs OFCS to linearize the partially known antilock bracking (ABS) dynamics, and then, the dynamic compensator is involved to stabilize the linearized system. However, conventional controllers suffer from limitations due to the presence of these high uncertainties. Therefore, we aim to demonstrate for the first time in the control area the feasibility of applying deep learning algorithm based on sparse auto‐encoder as an approximator for neglected dynamics and uncertain parameters of the ABS. The estimated states are used as inputs to the neural network (NN) and in the adaptation laws as an error signal. Simulations of the proposed control algorithm based adaptive DNN observer are conducted then compared to bang–bang controller, PI controller, and adaptive controller‐based single hidden layer‐neural network with only one‐neuron in hidden layers (SHL(1N)NN) to demonstrate its practical potential. Furthermore, both feasibility and efficiency of involving deep learning algorithm (DLA) in the control area have been successfully confirmed through robustness test.