الفهرس 
IntroductionOnly 14 pages are availabe for public view
Abstract
During the recent decades, the state estimation of the dynamic systems and the state
observation problem has been an active topic of research in different areas such as
automatic control applications. Several conventional nonlinear observers have been
suggested during the past decade and some of these are only applicable to systems with
specific model structures. However, for most practical processes, defining an exact model
is a hard task or is not possible at all. So, the main objective of this thesis is developing
an intelligent nonlinear observer-based adaptive control to improve the performance of
the unknown nonlinear systems.
In this thesis, the proposed observer is developed using a new structure and the
proposed adaptive dynamic programming (ADP) algorithm based observer is developed
using new structure to get stable controllers for nonlinear systems. The observer-based
adaptive control for controlling the systems without a priori knowledge of the system
dynamics gives high performance for nonlinear and time varying parameters of the
system. In this thesis, two proposed observer-based adaptive controller structures are
introduced.
The first proposed observer-based adaptive control is developed using diagonal
recurrent neural network (DRNN). The updating weights for the proposed DRNN
observer are developed based on the Lypaunov second method. ADP is designed based
on a critic DRNN, which is constructed to approximate the optimal cost function, which
can be applied to systems with higher degrees of nonlinearity and without a priori
knowledge of the system dynamics. The main objective of the first proposed controller is
to make the system states go to zero where the initial values are set as non-zero values.
The second proposed observer-based adaptive control is performed using reinforcement
learning (RL) algorithm which the critic and actor parts are implemented based on
quantum diagonal recurrent neural network (QDRNN). The proposed adaptive tracking
neural network control guarantee the faster convergence due to the developed updated
algorithm for the controller parameters, which is derived based on the Lyapunov stability. The stability analysis for all the proposed controllers is studied. The main objective of the
second proposed controller is to make the states of the controlled system track the desired
states.
The proposed adaptive systems have been simulated and compared with other
existing controllers in the previous publications. Simulation results indicate that the
response of the proposed controllers have good performance compared with other
existing algorithms. The proposed controllers have been designed and implemented
practically using a microcontroller for controlling the speed and position of a DC motor.
Practical results show good and significant improvement in the performance of the
proposed controllers to respond the system uncertainties and external disturbances.