Observer-Based Interval Type-2 Fuzzy Logic Control for Nonlinear Networked Control Systems with Delays Abdul-Wahid A. Saif 1 • Mohammad Mudasar 1 • Muhammad Mysorewala 1 • Moustafa Elshafei 1 Received: 13 July 2019 / Revised: 23 November 2019 / Accepted: 6 January 2020 Ó Taiwan Fuzzy Systems Association 2020 Abstract One of the popular area of research for the past decade in the academia as well as in the industry is net- worked 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 con- trol (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 con- sidered, 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–Kra- sovskii 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. Keywords Interval type-2 fuzzy logic control  Nonlinear networked control system  Observer-based control 1 Introduction A networked control system (NCS) can be defined as a traditional feedback control system which is closed by a communication channel network, which can be shared with different nodes other than the ones in the control system. In NCS, the controller is far from the system, so the sensors and actuators are connected through a shared communi- cation medium. This scheme leads to drastic reduction in system wiring, which makes it simple and low cost for installation and maintenance. These advantages also lead to an increase in the agility of the system which has led to more research in this field. However, the introduction of network for connecting sensors, actuators and controllers resulted in various issues such as delays, packet dropouts, limited communication capacity, packet transmission scheduling, and so on. This may result in inaccuracy in the information transmission or limited data transmission which may finally lead to the degradation in the perfor- mance of the control system and can even make the system unstable [1–6]. Since most of the physical systems and processes are nonlinear, an effective approach to tackle such systems is fuzzy logic. Takagi–Sugeno (T–S) fuzzy model-based control for such systems provides basis for stability anal- ysis and design of fuzzy control systems [7, 8]. & Abdul-Wahid A. Saif awsaif@kfupm.edu.sa Mohammad Mudasar g201105070@kfupm.edu.sa Muhammad Mysorewala Mysorewala@kfupm.edu.sa Moustafa Elshafei elshafei@kfupm.edu.sa 1 Systems Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia 123 Int. J. Fuzzy Syst. https://doi.org/10.1007/s40815-020-00799-9