Volume 6, Issue 2

Navigational sensors are evolving both on a commercial and research level. However, the limitation still lies in the accuracy of the respective sensors. For a navigation system to reach a certain accuracy, multi sensors or fusion sensors are used. In this paper, a framework of fuzzy sensor data fusing is proposed to obtain an optimised navigational system. Different types of sensors without a known state of inaccuracy can be fused using the same method proposed. This is demonstrated by fusing compass/accelerometer and GPS signal. GPS has evolved as a choice of navigation method for outdoor autonomous system. Despite it emerging trend and application, one problem remains is that it is still prone to inaccuracies due to environmental factors. These factors are available and evaluated in the GPS receiver architecture. These inaccuracies are available in the extracted NMEA(National Maritime Electronics Association) protocols as SNR (Signal to noise ratio) and HDOP (Horizontal Dillusion of Precision). Dead reckoning sensors on the other hand does not depend on external radio signal coverage and can be used in areas with low coverage. Unfortunately, the errors are unbounded and has an accumulative effect over time.

Fuzzy linear programming (FLP ) is an application of fuzzy set theory in linear decision making problems and most of these problems are related to linear programming contains fuzzy constrains or crisp objectives function or contains crisp constrains with fuzzy objectives function, which called fuzzy linear programming (FLP) with triplet fuzzy numbers consist a hybrid fuzzy. The crisp constrains used in the problems of types (= or ≥) with a proposed optimization fuzzy objectives and fuzzy constrains. In this paper proposed method for solving fuzzy linear programming problem by using Two-phase technique to solve the problem and to determine the optima crisp objectives.

The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PIλDμ (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.

In this paper, we evaluate the performance of UMTS (Universal Mobile Telecommunication System) downlink system in vicinity of UWB system. The study is achieved via simulating a scenario of a building which is located within UMTS cell borders and utilizes from both UMTS and UWB appliances. The simulation results show that the UMTS system is considerably affected by the UWB interference. However, in order to battle this interference and achieve reasonable BER (Bit Error Rate) of 10-4, we found that it is very necessary to carefully raise the UMTS base station transmitted power against that of UWB interferer. So, the minimum requirements for UMTS system to overcome UWB interference are stated in this work.

Induction Motors have been used as the workhorse in the industry for a long time due to its easy build, high robustness, and generally satisfactory efficiency. However, they are significantly more difficult to control than DC motors. One of the problems which might cause unsuccessful attempts for designing a proper controller would be the time varying nature of parameters and variables which might be changed while working with the motion systems. One of the best suggested solutions to solve this problem would be the use of Sliding Mode Control (SMC). This paper presents the design of a new controller for a vector control induction motor drive that employs an outer loop speed controller using SMC. Several tests were performed to evaluate the performance of the new controller method, and two other sliding mode controller techniques. From the comparative simulation results, one can conclude that the new controller law provides high performance dynamic characteristics and is robust with regard to plant parameter variations.

Control of Induction Motor (IM) is well known to be difficult owing to the fact the models of IM are highly nonlinear and time variant. In this paper, to achieve accurate control performance of rotor position control of IM, a new method is proposed by using adaptive inverse control (AIC) technique. In recent years, AIC is a very vivid field because of its advantages. It is quite different from the traditional control. AIC is actually an open loop control scheme and so in the AIC the instability problem cased by feedback control is avoided and the better dynamic performances can also be achieved. The model of IM is identified using adaptive filter as well as the inverse model of the IM, which was used as a controller. The significant of using the inverse of the IM dynamic as a controller is to makes the IM output response to converge to the reference input signal. To validate the performances of the proposed new control scheme, we provided a series of simulation results.

In this paper an integrated electronic system has been designed, constructed and tested. The system utilizes an interface card through the parallel port in addition to some auxiliary circuits to perform fuzzy control operations for DC motor speed control with load and no load. Software is written using (C++ language Ver. 3.1) to display the image as control panel for different types of both conventional and fuzzy control. The main task of the software is to simulate: first, how to find out the correct parameters for fuzzy logic controller (membership’s function, rules and scaling factor). Second, how to evaluate the gain factors (KP, KI and KD) by Ziegler-Nichols method. When executing any type of control process the efficiency is estimated by drawing the relative speed response for this control.

This paper presents a method for improving the speed profile of a three phase induction motor in direct torque control (DTC) drive system using a proposed fuzzy logic based speed controller. A complete simulation of the conventional DTC and closed-loop for speed control of three phase induction motor was tested using well known Matlab/Simulink software package. The speed control of the induction motor is done by using the conventional proportional integral (PI) controller and the proposed fuzzy logic based controller. The proposed fuzzy logic controller has a nature of (PI) to determine the torque reference for the motor. The dynamic response has been clearly tested for both conventional and the proposed fuzzy logic based speed controllers. The simulation results showed a better dynamic performance of the induction motor when using the proposed fuzzy logic based speed controller compared with the conventional type with a fixed (PI) controller.

This Paper presents a novel hardware design methodology of digital control systems. For this, instead of synthesizing the control system using Very high speed integration circuit Hardware Description Language (VHDL), LabVIEW FPGA module from National Instrument (NI) is used to design the whole system that include analog capture circuit to take out the analog signals (set point and process variable) from the real world, PID controller module, and PWM signal generator module to drive the motor. The physical implementation of the digital system is based on Spartan-3E FPGA from Xilinx. Simulation studies of speed control of a D.C. motor are conducted and the effect of a sudden change in reference speed and load are also included.

This paper presents a new strategy for controllinginduction motors with unknown parameters. Using a simplelinearized model of induction motors, we design robust adaptivecontrollers and unknown parameters update laws. The controldesign and parameters estimators are proved to have globalstable performance against sudden load variations. All closedloop signals are guaranteed to be bounded. Simulations areperformed to show the efficacy of the suggested scheme

This paper deals with the application of Fuzzy-Neural Networks (FNNs) in multi-machine system control applied on hot steel rolling. The electrical drives that used in rolling system are a set of three-phase induction motors (IM) controlled by indirect field-oriented control (IFO). The fundamental goal of this type of control is to eliminate the coupling influence though the coordinate transformation in order to make the AC motor behaves like a separately excited DC motor. Then use Fuzzy-Neural Network in control the IM speed and the rolling plant. In this work MATLAB/SIMULINK models are proposed and implemented for the entire structures. Simulation results are presented to verify the effectiveness of the proposed control schemes. It is found that the proposed system is robust in that it eliminates the disturbances considerably.

In a human–robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators. The electromyography (EMG) signals can be used as a control source of artificial arm after it has been processed. The objective of this work is to achieve better classification with multiple parameters using KNearest Neighbor for different movements of a prosthetic arm. A K- Nearest Neighbor (K-NN) rule is one of the simplest and the most important methods in pattern recognition. The proposed structure is simulated using MATLAB Ver. R2009a, and satisfied results are obtained by comparing with conventional method of recognition using Artificial Neural Network(ANN), that explains the ability of the proposed structure to recognize the movements of human arm based EMG signals. Results show the proposed technique achieved a uniformly good performance with respect to ANN in term of time which is important in recognition systems, better accuracy in recognition when applied to lower SNR signal .

This is a design of an expert system in the focused ion beam optical system by using fuzzy logic technique to build an intelligent agent. Present software has been designed as an interpretation expert system, written in Visual C# for optimizing the calculation of three electrostatic lenses column. By using such rule based engine, the axial potential distributions for electrostatic fields undergo the constraints have been used to find spot size focusing for ions in the image plane which have values are very useful for getting and designing FIB model, over ranges of ion beam angles (5,10,30,50,75 and 100) mrad.

This paper aims to give an overview of the European Rail Traffic Management System (ERTMS) which is currently being implemented in Europe and other parts of the world. It provides some background on this system, the objectives behind the development of the ERTMS, its architecture, different application levels of the ERTMS and brief information on its implementation in Europe and worldwide. The paper assumes the readers have a little or no knowledge of the ERTMS.

Self-organizing systems arise in many different fields. In this work we analyze data from social and biological systems. A central question is to demonstrate the presence of the determinism in time-series extracted from such systems that appear apparently not correlated but that are two good benchmarks for the study of complexity in real systems. We will apply the Kaplan test and we will define an order parameter for the biological data to characterize the complexity of the system.

The objective in Data Grids is to reduce access and file (replica) transfer latencies, as well as to avoid single site congestion by the numerous requesters. To facilitate access and transfer of the data, the files of the Data Grid are distributed across the multiple sites. The effectiveness of a replica selection strategy in data grids depends on its ability to serve the requirement posed by the users’ jobs. Most jobs are required to be executed at a specific execution time. To achieve the QoS perceived by the users, response time metrics should take into account a replica selection strategy. Total execution time needs to factor latencies due to network transfer rates and latencies due to search and location. Network resources affect the speed of moving the required data and searching methods can reduce scope for replica selection. This paper presents a replica selection strategy that adapts its criteria dynamically so as to best approximate application providers’ and clients’ requirements. We introduce a new selection technique (EST) that shows improved performance over the more common algorithms.