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Observer-Based Interval Type-2 Fuzzy Logic Control for Nonlinear Networked Control Systems with Delays

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Abstract

One of the popular area of research for the past decade in the academia as well as in the industry is networked control systems (NCS). Variable time delays induced by the network and data packet dropout during transmission of data are among the problems encountered in these type of systems. Researchers proposed and developed various control strategies over the past years to deal with the above-mentioned problem. Fuzzy logic control (FLC) is a widely used technique for dealing with control problems, and the most commonly used one is type-1 FLC. However, the interval type-2 (IT2) is proven to be better at handling uncertainties compared to the type-1 FLC. In this paper, a nonlinear NCS with delays is considered, and an observer-based IT2 FLC is designed in order to improve the control of NCS due to the presence of uncertainties and delays. The developed scheme includes the design of a state feedback controller based on IT2 FLC, and an observer-based IT2 FLC using Lyapunov–Krasovskii theory. To investigate the effectiveness of the proposed scheme, simulations are performed considering various network delays. Firstly, the results of IT2 FLCs are compared with that of type-1 FLCs and improvement is observed. Secondly, the newly developed observer-based IT2 FLC is compared with the IT2 state feedback FLC developed in the literature. Results show faster and more effective response using the newly developed technique.

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Authors and Affiliations

  1. Systems Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia

    Abdul-Wahid A. Saif, Mohammad Mudasar, Muhammad Mysorewala & Moustafa Elshafei

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  1. Abdul-Wahid A. Saif
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  2. Mohammad Mudasar
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  3. Muhammad Mysorewala
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  4. Moustafa Elshafei
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Correspondence to Abdul-Wahid A. Saif.

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Saif, AW.A., Mudasar, M., Mysorewala, M. et al. Observer-Based Interval Type-2 Fuzzy Logic Control for Nonlinear Networked Control Systems with Delays. Int. J. Fuzzy Syst. 22, 380–399 (2020). https://doi.org/10.1007/s40815-020-00799-9

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  • DOI: https://doi.org/10.1007/s40815-020-00799-9

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