الفهرس 
1 INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Networked control systems (NCSs) are a type of control systems where sensors,
actuators, and controllers are interconnected by communication networks. Almost all
industrial applications require applying the NCSs to easily reduce the design cost,
wiring, maintenance difficulties, and failure risks and improve the efficiency, reliability,
and fault detection. In addition, the NCSs have more advantages but they have some
problems, which affect the system performance and may lead to instability, like
transmission delays and data dropouts. The conventional controllers are found
unsuitable when it required for controlling the NCSs in real time due to uncertainties.
Such uncertainties as packet data dropout which causes measurement uncertainties in
input/output data, a noise produced by the sensors, precision of the sensors, nonlinear
characteristic of the actuator and the system structure. All these factors have led to the
evolution of more advanced techniques, which are found to be more appropriate in
handling such complex nonlinear NCSs.
This thesis presents develops type-2 fuzzy controllers for NCSs. The aim of this
work is to control complex nonlinear NCSs and cope with uncertainties. Moreover, this
thesis aims to compensate the negative effect related to the presence of the
communication network such that the packet dropout and time-varying delay to improve
the performance of the system and guarantee the closed-loop stability. A part of the
thesis interest has been devoted to designing accurate models that can completely
simulate complex nonlinear dynamic systems with uncertainties to be used in the
control design.
In this thesis, four schemes based on type-2 fuzzy logic systems are proposed. The
first one is the interval type-2 Takagi-Sugeno-Kang fuzzy networked Wiener model.
The second one is the interval type-2 Takagi-Sugeno-Kang fuzzy networked
Hammerstein model. The third one is the adaptive interval type-2 Takagi-Sugeno-Kang
fuzzy controller. The fourth one is the model-based adaptive interval type-2 fuzzy
controller.ABSTRACT
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In this thesis, three learning algorithms for adapting the parameters of the proposed
schemes are proposed. The stability analysis for all the proposed schemes are studied.
Also, a compensation technique is designed to cope with significant time-varying delay
and packet dropout. The proposed schemes are compared with previously published
schemes to show the superiority and robustness of the proposed structures to handle the
system uncertainties. The proposed type-2 fuzzy controllers have been designed and
implemented practically based on a networked shunt wound DC machine subject to
packet dropout, time-varying delay and noisy measurement data. The practical results
indicate a good performance of the proposed controllers to deal with the network
problems, system uncertainties and nonlinearities.