Dr. Amjad J. Humaid; Dr. Hamid M. Hasan; Dr. Firas A. Raheem
Volume 14, Issue 3 , December 2014, , Page 42-51
Abstract
Abstract— Nowadays congestion in computer networks is pointed out as an
important and a challenging problem. TCP (Transmission Control Protocol) has the
mechanism to avoid congestion in computer networks. TCP detects congestion by
checking acknowledgements or time-out processing and adjusts TCP ...
Read More ...
Abstract— Nowadays congestion in computer networks is pointed out as an
important and a challenging problem. TCP (Transmission Control Protocol) has the
mechanism to avoid congestion in computer networks. TCP detects congestion by
checking acknowledgements or time-out processing and adjusts TCP window sizes of
senders. However, this control method shows low efficiency in communications
because it is based on a mechanism that avoid congestion after congestion once appears
in computer networks. TCP random early detection RED is another popular congestion
control scheme. The fundamental idea behind this control algorithm randomly drops the
incoming packets proportional to the average queuing length and to keep the queuing
length to a minimum. To achieve high efficiency and high reliability of communications
in computer networks, many control strategies based on advanced control theories have
been introduced to tackle the congestion problem. Model Predictive Control (MPC) is
the only practical control method that takes account of system constraints explicitly, and
the only ‘advanced control’ method to have been adopted widely in industry. MPC is a
model-based method which uses online optimization in real time to determine control
signals. The solution to optimization problem is usually formulated with the help of a
process model and measurements. At each control interval, an optimization algorithm
attempts to determine the plant dynamics by computing a sequence of control input
values satisfying the control specifications. In this work, a planning strategy based on
MPC will be developed for congestion control problem. A "preset controllers" approach
will be introduced for such application. The effectiveness of considered controller will
assessed in terms of how well it could show good tracking performance, maximizing the
utilization of the available bandwidth and to what extent it could cope with system
uncertainties.
Dr. Abdulrahim Thiab Humod
Volume 13, Issue 3 , December 2013, , Page 51-64
Abstract
Abstract – Artificial Neural Networks (ANN) can be used as intelligent controllers to
control non-linear dynamic systems through learning, which can easily accommodate
the non linearity’s, time dependencies, model uncertainty and external disturbances.
Modern power systems are complex and non-linear ...
Read More ...
Abstract – Artificial Neural Networks (ANN) can be used as intelligent controllers to
control non-linear dynamic systems through learning, which can easily accommodate
the non linearity’s, time dependencies, model uncertainty and external disturbances.
Modern power systems are complex and non-linear and their operating conditions can
vary over a wide range. The Nonlinear Auto-Regressive Moving Average (NARMAL2)
model system is proposed as an effective neural networks controller model to
achieve the desired robust Automatic Voltage Regulator (AVR) for Synchronous
Generator (SG) to maintain constant terminal voltage. The concerned neural networks
controller for AVR is examined on different models of SG and loads. The results shows
that the neuro-controllers have excellent responses for all SG models and loads in view
point of transient response and system stability compared with conventional PID
controllers. Also shows that the margins of robustness for neuro-controller are greater
than PID controller.
Ali Majeed Mahmood
Volume 12, Issue 2 , December 2012, , Page 1-8
Abstract
Abstract: This paper attempts to tune any controller without the knowledge of mathematical model for the system to be controlled. For that purpose, the optimization algorithm of MATLAB / Nonlinear Control Design Blockset (NCD) is adapted for On-line tuning for controller parameters. To present the methodology, ...
Read More ...
Abstract: This paper attempts to tune any controller without the knowledge of mathematical model for the system to be controlled. For that purpose, the optimization algorithm of MATLAB / Nonlinear Control Design Blockset (NCD) is adapted for On-line tuning for controller parameters. To present the methodology, a PID controller is verified with the physical plant using the engine speed control System where the problem of maintaining the engine idle speed at a reference value with the applied load is studied. A Proportional Integral Derivative (PID) Controller is used to solve this problem, but to get the best controller parameters the (NCD) Blockset is used for tuning the PID parameters. Simulation shows promising results in the idle speed response by comparing NCD tuning results with the trial and error results. The analytical results are carried out MATLAB / SIMULINK.
Dr. Amjad J. Humaidi
Volume 10, Issue 1 , December 2010, , Page 107-120
Abstract
Abstract:
Pneumatic servo system has been applied in many industry fields. The system has many advantages, such as high speed, high flexibility and low price. However, the application of the system is restricted because the physical parameters have strong nonlinearity, inaccuracy and uncertainty, so ...
Read More ...
Abstract:
Pneumatic servo system has been applied in many industry fields. The system has many advantages, such as high speed, high flexibility and low price. However, the application of the system is restricted because the physical parameters have strong nonlinearity, inaccuracy and uncertainty, so that it is very difficult to find an optimal controller by means of traditional control theory. Proportional integral derivative (PID) control is one of the earlier control strategies; it has a simple control structure and can be easily tuned. Optimization of PID controller parameters is one of the recent control solutions; especially when the system is of high complexity. In this paper foraging strategy has been adopted to optimize the gains of PID controller for positioning control of a pneumatic system. The foraging theory is based on the assumption that animals search for nutrients in a way that maximize their energy intake per unit time spent for foraging. The bacterial foraging algorithm is a non-gradient and stochastical optimization technique; as no need for measurement and analytical description. In the work, the optimization model of E. coli bacterial foraging has been used and the performance index (cost) is based on Integral Square Error (ISE) for obtaining sub-optimal values of controller parameters. The behavior of bacteria (solutions) over their lifetime has been simulated and the effect of foraging parameters on cost function has been studied.
Mr. Qussay F. Ad; Doory; Dr. Mohammed Yousif Hassan
Volume 7, Issue 1 , June 2007, , Page 87-96
Abstract
Abstract:
A combination of fuzzy logic and neural network can generate a fuzzy neural
controller which in association with a neural network emulator can improve the output
response of the controlled system. This combination uses the neural network training
ability to adjust the membership functions ...
Read More ...
Abstract:
A combination of fuzzy logic and neural network can generate a fuzzy neural
controller which in association with a neural network emulator can improve the output
response of the controlled system. This combination uses the neural network training
ability to adjust the membership functions of a PID like fuzzy neural controller. Such
controller can be used to adaptively control nonlinear MIMO systems.
The goal of the controller is to force the controlled system to follow a reference
model with required transient specifications of minimum overshoot, minimum rise time
and minimum steady state error. The fuzzy membership functions were tuned using the
propagated error between the plant outputs and the desired ones.
To propagate the error from the plant outputs to the controller, a neural network
is used as a channel to the error. This neural network uses the back propagation
algorithm as a learning technique.
The controller was tested using two inputs / two outputs nonlinear time invariant
model. Different reference (set-point) inputs were applied to the closed loop system.
Also, different values of loads and disturbances were applied to the closed loop system.
Simulation results show that the controller achieves the design requirements.
