IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

Abstract – Artificial Neural Networks (ANN) and Neuro - Fuzzy controllers can be used as intelligent controllers to control non-li¬near 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 (NARMA-L2) 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 essential part of Neuro-Fuzzy comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks are called Adaptive-Network-based Fuzzy Inference System (ANFIS), which possess certain advantages over neural networks. The concerned neural networks and Neuro - Fuzzy controllers for AVR is examined on different models of SG and loads. The results show that the Neuro-controllers and Neuro - Fuzzy controllers have excellent responses for all SG models and loads in view point of transient response and system stability. Also it shows that the margins of robustness for Neuro - Fuzzy controller are greater than Neuro-controller.

Abstract – Water has become the most important problem in relations between the countries of the Middle East in the recent years. It occupies an important place on the agenda of several international organizations. Water control and reduction loss of water discharge is a major challenge facing the design of new irrigation projects. A downstream control algorithm for demand operation of irrigation system is proposed in this paper through maintaining downstream end discharge of the canal at the target point by manipulating the upstream sluice gate in real time. The control of the water level and discharge for canal irrigation system has non-linear, time-varying and uncertainty characteristics. This paper compares three control algorithms; conventional PID, fuzzy neural network PID, and PID neural network control based on fuzzy neural network model. The simulation results show that the first control has larger over-shoot, longer adjusting time and poorer anti-interference ability. The second control overcomes above-mentioned short-comings, small overshoot, faster response speed, very small steady state error. Third control produces better effects than previous controllers in both steady performance and dynamic performance, including shorter steady-state time, non-overshot, no oscillator, and higher dynamic tracking rate.

Abstract – Thermocouples are usually used for measuring temperatures in steel industry, gas turbine, diesel engine and many industrial processes. Thermocouple usually have nonlinear Temperature-Voltage relationship (mV=f(T˚)). However, on the monitoring side, it is required to have the inverse relationship [T˚=f-1(mV)] to determined the actual temperature sensed by the thermocouple. In this work the neural network is fully utilized to represent the required inverse nonlinear relationship of different and most popular thermocouples (K, J, B) Types. Levenberg Marquardt is used as learning process to find these neural networks. It is found that each type of thermocouples under test can be represented by a single neural network structure. Moreover, the obtained results show the power of neural network in representing the inverse static relationship of each thermocouple that gives less than 1% of the actual measured temperature in the whole temperature range in comparison to polynomial fitting method.

Abstract –Physical disabilities such as full or partial loss of function in the shoulder and elbow are a common impairment in the elderly and a secondary effect due to strokes, trauma, sports and injuries. Rehabilitation programs are the main method to promote functional recovery in these subjects. This work focuses on designing and implementing a 3 DoF's non-wearable, light weight rehabilitation robot for rehabilitee the human arm that can be used in hospitals or homes. This robot structure eliminates arm singularity problem of the end effecter with respect to the robot base by adding an offset link. The design includes an adjustable mechanism standing on a seat for robot base and links to be adaptable for all body sizes and to align for all human arm lengths.
Intelligent PD-like Fuzzy Logic position controllers (FLCs) are designed for joints of the 3 DoF's robot to follow the desired medical trajectories during limited time with minimum overshoot and minimum oscillations in position response. These controllers are implemented using MATLAB Simulink. The controllers control the rehabilitation robot using Data Acquisition Card, (Advantech PCI-1712) that generates and reads the required digital and analog signals for robot. The experimental results are acceptable in terms of the practical application.

Abstract –This paper presents the modeling and control simulation for Lab-Volt 5250 five degree of freedom robot manipulator based on the standard Denavit- Hartenberg approach. The dynamic model of the robot derived using Euler- Lagrange equation which is the energy balance equation. This dynamic model has a very high nonlinearity that is represented by using MATLAB, m-file and simulation to run the dynamic model in open and close loop. In this research, the close loop simulation is done by using two types of control theory that applied to control each joint of the robot manipulator independently, the first one is PD controller and the second one is an intelligent controller which is PD-like fuzzy controller used to control the joint position.

Abstract - In this paper, a new optimization method for the Reciprocal Velocity Obstacles (RVO) is proposed. It uses the Artificial Bee Colony Optimization (ABC) for navigation control of multiple mobile robots with kinematic constraints. RVO is used for collision avoidance between the robots, while ABC is used to choose the best path for the robot maneuver to avoid colliding with other robots and to get to its goal faster. This method is applied on 24 mobile robots facing each other. Simulation results have shown that this method outperformed the ordinary RVO when the path was arbitrarily chosen.

Abstract—In the next generation of wireless communications, Multiple Input Multiple Output (MIMO) communication system will be a key technology to enhance the communication efficiency. The popular method for estimating the direction of arrival of sources impinging on an array of MIMO sensors is Multiple Signal Classification (MUSIC) method which is a problem of great interest in MIMO communication system. The iterative searching technique has been shown more likely to converge to a local maximum, causing errors in Direction of arrival (DOA) estimation. A new system is proposed to estimate direction of arrival of sources for Multiple Input Multiple Output (MIMO) communication system by combining Genetic Algorithm and (MUSIC) method. In the proposed model, by using Genetic algorithm the direction of arrival angles can be selected automatically good response by fast convergence, efficiency and yield more accuracy to estimate the direction of arrival of the sources over existing conventional spectral searching methods which is shown by the result of computer simulation for proposed system. The important feature of new system is that, it is observed that Genetic Algorithm (GA) combined with MUSIC method is a powerful alternative in online DOA estimation

Abstract – The electronic throttling angle control system is the newly common
requirement trend in automotive technology. It is used to regulate the amount of air flow
into the engine. Due to the existence of multiple non-smooth nonlinearities, the
controller design to the electronic throttle valve becomes a difficult task. These
nonlinearities including stick–slip friction, backlash, and a discontinuous nonlinear
spring involved in the system. The designed controller in the present work consists of
the estimated perturbation term with a negative sign (used to cancel the perturbation
term) and a stabilizing term used to stabilize the nominal system model. The
perturbation term consists of the external unknown input and the uncertainty in throttle
valve model including the nonlinear terms. The utilized estimator uses the sliding mode
control theory and based on the equivalent control methodology. The simulation results
show the effectiveness of the proposed controller in estimating the perturbation term.
And then in forcing the angle of the throttle valve to follow the desired opening angle in
presence of nonlinearities and disturbances in throttle system model and the variation in
its parameters.