الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Most real dynamic systems inherently contain nonlinearities, which are commonly unknown to the system designer. The control of nonlinear systems is still open problems due to their complexity natures. These problems become more complex when the system parameters are uncertain. Therefore, in controlling of such dynamic systems, one needs to handle unknown nonlinearities and uncertain parameters. Type-1 fuzzy logic system are not able to directly model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of the system because their fuzzy membership functions are totally crisp. On the other hand, type-2 fuzzy logic system (T2-FLS) is able to model and minimize such uncertainties because their fuzzy membership functions are themselves fuzzy. Computing the centroid and performing type-reduction (TR) for type-2 fuzzy sets and T2-FLSs are operations that must be taken into consideration. The Karnik-Mendel (KM) algorithms are the standard ways to do these operations. The performing TR is an operation that needs the KM algorithms at each iteration. Therefore, the high computational cost of the iterative KM algorithms in TR means that it is more expensive to spread T2-FLSs, which may prevent them from certain cost-sensitive real world applications.
The major objective of the research undertaken in this thesis was to develop and implement intelligent controllers based on T2-FLS using a new method of TR, which we propose it to handle the problems of uncertain parameters in real dynamic systems. The proposed TR method is able to decrease the computational cost of the T2-FLS and improves the performance of the controller. In this thesis, three interval type-2 fuzzy logic controllers have been proposed; the interval type-2 fuzzy proportional – derivative plus proportional – integral (IT2F-PD+PI) controller, the interval type-2 fuzzy proportional – derivative plus integral (IT2F-PD+I) controller and the interval type-2 fuzzy proportional – integral – derivative (IT2F-PID) controller. The analytical structures and stability analysis for the three proposed
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controllers have been derived in this study. Three adaptive interval type-2 fuzzy logic controllers have been proposed in this study. The interval type-2 fuzzy neural network controller has been proposed based on our new TR method. The proposed controllers have been designed and implemented practically using a low cost microcontroller (PIC18F4685) for controlling a nonlinear uncertain inverted pendulum system to overcome the system uncertainties. Simulation and practical results show good and significant improvement in the performance of the proposed controllers to respond the system uncertainties rather than type-1 fuzzy logic controllers and type-1 fuzzy neural network controller. Thus, the methodology proposed in this study can be used to realize a robust, practically realizable, fuzzy controller capable of controlling a real-life plant with system uncertainties with acceptable closed-loop response.