Neural Network Adaptive Observer design for Nonlinear Systems with Partially and Completely Unknown Dynamics Subject to Variable Sampled and Delay Output Measurement

• Published in: Neurocomputing (Amsterdam) Vol. 561; p. 126865
• Main Authors: Zhuang, Xincheng, Tian, Yang, Wang, Haoping, Ali, Sofiane Ahmed
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 07.12.2023; Elsevier
• Subjects: Automatic; Engineering Sciences; Nonlinear observer; Partial and complete unknown nonlinear system; RBF neural network; Signal and Image processing; Variable sampled and delay measurement; Nonlinear observer; RBF neural network; Partial and complete unknown nonlinear system; Variable sampled and delay measurement
• ISSN: 0925-2312
• DOI: 10.1016/j.neucom.2023.126865

This paper proposes a novel Neural Network Adaptive Observer (NNAO) for Nonlinear Systems with Partially and Completely Unknown Dynamics (NSPCUD), subject to variable sampled and delayed output. The method involves designing a neural network observer for partially unknown nonlinear systems with sampled and delayed outputs, using a radial basis function (RBF) neural network to approximate the system’s unknown part. A new weight update algorithm is proposed, along with a closed-loop output predictor for coping with variable samples, and a closed-loop integral compensation to handle variable delay. This approach is then extended to cover completely unknown systems as well. Numerical simulations and comparisons between the proposed method and previous methods on autonomous ground vehicle models were conducted to verify the effectiveness of the proposed NNAO. •A neural network observer is proposed under variable sampled and delayed output.•A new weight update law is designed to construct the neural network observer.•The proposed observer is extended to more general nonlinear systems.•It is proved that the system state and weight estimation is UUB.•The effectiveness of the observer is demonstrated through AGV model by simulation.