Neural Network-Based Robust Adaptive Output Feedback Control for MIMO Time-Varying Delay Systems

Farouk Zouari; Mufti Mahmud

doi:10.1007/978-3-031-98498-3_5

Neural Network-Based Robust Adaptive Output Feedback Control for MIMO Time-Varying Delay Systems

Farouk Zouari^*
, Mufti Mahmud

^*Corresponding author for this work

Department of Information and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper presents a robust neural adaptive output-feedback control strategy for multi-input-multi-output (MIMO) systems with time-varying delays. A linear state observer addresses unavailable state variables, while a neural network approximates unknown nonlinear functions. The control law mitigates external disturbances and various errors. The Gradient Algorithm with Projection tackles unknown control directions, and the Strictly Positive Real (SPR) condition, applied through the Lyapunov–Krasovskii method, aids in designing adaptation laws using output errors. This approach offers several advantages: it applies to a wide range of MIMO systems, avoids singularity issues, requires few adapting parameters, and ensures asymptotic convergence of tracking errors. A simulation example validates the method's effectiveness and feasibility.

Original language	English
Title of host publication	Applications of Artificial Intelligence and Data Science - 1st Global Conference, AAIDS 2024, Proceedings
Editors	Mufti Mahmud, Nelishia Pillay, M Shamim Kaiser
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	60-77
Number of pages	18
ISBN (Print)	9783031984976
DOIs	https://doi.org/10.1007/978-3-031-98498-3_5
State	Published - 2026
Event	1st Global Conference on Applications of Artificial Intelligence and Data Science, AAIDS 2024 - London, United Kingdom Duration: 3 Apr 2024 → 5 Apr 2024

Publication series

Name	Communications in Computer and Information Science
Volume	2601 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	1st Global Conference on Applications of Artificial Intelligence and Data Science, AAIDS 2024
Country/Territory	United Kingdom
City	London
Period	3/04/24 → 5/04/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

Lyapunov–Krasovskii method
MIMO time-varying delay systems
Neural network
Strictly Positive Real (SPR) condition
output-feedback control
unknown control direction problem

ASJC Scopus subject areas

General Computer Science
General Mathematics

Access to Document

10.1007/978-3-031-98498-3_5

Cite this

Zouari, F., & Mahmud, M. (2026). Neural Network-Based Robust Adaptive Output Feedback Control for MIMO Time-Varying Delay Systems. In M. Mahmud, N. Pillay, & M. S. Kaiser (Eds.), Applications of Artificial Intelligence and Data Science - 1st Global Conference, AAIDS 2024, Proceedings (pp. 60-77). (Communications in Computer and Information Science; Vol. 2601 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-98498-3_5

Zouari, Farouk ; Mahmud, Mufti. / Neural Network-Based Robust Adaptive Output Feedback Control for MIMO Time-Varying Delay Systems. Applications of Artificial Intelligence and Data Science - 1st Global Conference, AAIDS 2024, Proceedings. editor / Mufti Mahmud ; Nelishia Pillay ; M Shamim Kaiser. Springer Science and Business Media Deutschland GmbH, 2026. pp. 60-77 (Communications in Computer and Information Science).

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abstract = "This paper presents a robust neural adaptive output-feedback control strategy for multi-input-multi-output (MIMO) systems with time-varying delays. A linear state observer addresses unavailable state variables, while a neural network approximates unknown nonlinear functions. The control law mitigates external disturbances and various errors. The Gradient Algorithm with Projection tackles unknown control directions, and the Strictly Positive Real (SPR) condition, applied through the Lyapunov–Krasovskii method, aids in designing adaptation laws using output errors. This approach offers several advantages: it applies to a wide range of MIMO systems, avoids singularity issues, requires few adapting parameters, and ensures asymptotic convergence of tracking errors. A simulation example validates the method's effectiveness and feasibility.",

keywords = "Lyapunov–Krasovskii method, MIMO time-varying delay systems, Neural network, Strictly Positive Real (SPR) condition, output-feedback control, unknown control direction problem",

author = "Farouk Zouari and Mufti Mahmud",

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Zouari, F & Mahmud, M 2026, Neural Network-Based Robust Adaptive Output Feedback Control for MIMO Time-Varying Delay Systems. in M Mahmud, N Pillay & MS Kaiser (eds), Applications of Artificial Intelligence and Data Science - 1st Global Conference, AAIDS 2024, Proceedings. Communications in Computer and Information Science, vol. 2601 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 60-77, 1st Global Conference on Applications of Artificial Intelligence and Data Science, AAIDS 2024, London, United Kingdom, 3/04/24. https://doi.org/10.1007/978-3-031-98498-3_5

Neural Network-Based Robust Adaptive Output Feedback Control for MIMO Time-Varying Delay Systems. / Zouari, Farouk; Mahmud, Mufti.
Applications of Artificial Intelligence and Data Science - 1st Global Conference, AAIDS 2024, Proceedings. ed. / Mufti Mahmud; Nelishia Pillay; M Shamim Kaiser. Springer Science and Business Media Deutschland GmbH, 2026. p. 60-77 (Communications in Computer and Information Science; Vol. 2601 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2026

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AB - This paper presents a robust neural adaptive output-feedback control strategy for multi-input-multi-output (MIMO) systems with time-varying delays. A linear state observer addresses unavailable state variables, while a neural network approximates unknown nonlinear functions. The control law mitigates external disturbances and various errors. The Gradient Algorithm with Projection tackles unknown control directions, and the Strictly Positive Real (SPR) condition, applied through the Lyapunov–Krasovskii method, aids in designing adaptation laws using output errors. This approach offers several advantages: it applies to a wide range of MIMO systems, avoids singularity issues, requires few adapting parameters, and ensures asymptotic convergence of tracking errors. A simulation example validates the method's effectiveness and feasibility.

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Zouari F, Mahmud M. Neural Network-Based Robust Adaptive Output Feedback Control for MIMO Time-Varying Delay Systems. In Mahmud M, Pillay N, Kaiser MS, editors, Applications of Artificial Intelligence and Data Science - 1st Global Conference, AAIDS 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2026. p. 60-77. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-98498-3_5

Neural Network-Based Robust Adaptive Output Feedback Control for MIMO Time-Varying Delay Systems

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this