First three degree of six degree of freedom robotic manipulator is controlled by a new fuzzy sliding feedback linearization controller. The robot arm has six revolute joints allowing the corresponding links to move horizontally. When developing a controller using conventional control methodology (e.g., feedback linearization methodology), a design scheme has to be produced, usually based on a system’s dynamic model. The work outline in this research utilizes soft computing applied to new conventional controller to address these methodology issues. Feedback linearization controller (FLC) is influential nonlinear controllers to certain systems which this method is based on compute the required arm torque using nonlinear feedback control law. When all dynamic and physical parameters are known FLC works superbly; practically a large amount of systems have uncertainties and fuzzy feedback linearization controller (FFLC) reduce this kind of limitation. Fuzzy logic provides functional capability without the use of a system dynamic model and has the characteristics suitable for capturing the approximate, varying values found in a MATLAB based area. To increase the stability and robustness new mathematical switching sliding mode methodology is applied to FFLC. Based on this research model free mathematical tunable gain new sliding switching feedback linearization controller applied to robot manipulator is presented to have a stable and robust nonlinear controller and have a good result compared with conventional and pure fuzzy logic controllers.

Recently, series compensation is widely used in transmission. However, this creates several problems to conventional protection approaches. This paper presents overcurrent and distance protection schemes, for fault classification in transmission lines with thyristor controlled series capacitor (TCSC) using support vector machine (SVM). The fault classification task is divided into four separate subtasks (SVMa, SVMb, SVMc and SVMg), where the state of each phase and ground is determined by an individual SVM. The polynomial kernel SVM is designed to provide the optimal classification conditions. Wide variations of load angle, fault inception angle, fault resistance and fault location have been carried out with different types of faults using PSCAD/EMTDC program. Backward faults have also been included in the data sets. The proposed technique is tested and the results verify its fastness, accuracy and robustness.

Very recently evolutionary optimization algorithms use the Genetic Algorithm to improve the result of Optimization problems. Several processes of the Genetic Algorithm are based on 'Random', that is fundamental to evolutionary algorithms, but important defections in the Genetic Algorithm are local convergence and high tolerances in the results, they have happened for randomness reason. In this paper we have prepared pseudo random numbers by Lorenz chaotic system for operators of Genetic Algorithm to avoid local convergence. The experimental results show that the proposed method is much more efficient in comparison with the traditional Genetic Algorithm for solving optimization problems.

In this paper, we present analysis of different training types for nonlinear autoregressive neural network, used for simulation of magnetic levitation system. First, the model of this highly nonlinear system is described and after that the Nonlinear Auto Regressive eXogenous (NARX) of neural network model is given. Also, numerical optimization techniques for improved network training are described. It is verified that NARX neural network can be successfully used to simulate real magnetic levitation system if suitable training procedure is chosen, and the best two training types, obtained from experimental results, are described in details.

This paper deals with detection of defects in the manufactured ceramic tiles to ensure high density quality. The problem is concerned with the automatic inspection of ceramic tiles using Artificial Neural Network (ANN). The performance of the technique has been evaluated theoretically and experimentally on samples. Architecture of the system involves binary matrix processing and utilization of Artificial Neural Network (ANN) to detect defects. The above automatic inspection procedures have been implemented and tested on company floor tiles. The results obtained confirmed the efficiency of the methodology in defect detection in raw tile and its relevance as a promising approach on matrix, as well as included in quality control and inspection programs.

The accurate control of motion is a fundamental concern in mechatronics applications, where placing an object in the exact desired location with the exact possible amount of force and torque at the correct exact time is essential for efficient system operation. An accurate modeling, simulation and dynamics analysis of actuators for mechatronics motion control applications is of big concern. The ultimate goal of this paper addresses different approaches used to derive mathematical models, building corresponding simulink models and dynamic analysis of the basic open loop electric DC motor system, used in mechatronics motion control applications, particularly, to design, construct and control of a mechatronics robot arm with single degree of freedom, and verification by MATLAB/Simulink. To simplify and accelerate the process of DC motors sizing, selection, dynamic analysis and evaluation for different motion applications, different mathematical models in terms of output position, speed, current, acceleration and torque, as well as corresponding simulink models, supporting MATLAB m.file and general function block models are to be introduced. The introduced models were verified using MATLAB/ Simulink. These models are intended for research purposes as well as for the application in educational process.
This paper is part I of writers' research about mechatronics motion control, the ultimate goal of this research addresses design, modeling, simulation, dynamics analysis and controller selection and design issues, of mechatronics single joint robot arm. where a electric DC motor is used and a control system is selected and designed to move a Robot arm to a desired output position, θ corresponding to applied input voltage, Vin and satisfying all required design specifications.

The main intention of this article is to represent fuzzy matrices with the help of reference function.Thereafter addition and multiplication of fuzzy matrices are defined keeping in pace with the newly represented fuzzy matrices. Here we study the determinant theory as well as the adjoint theory of square fuzzy matrices. The contribution of this article is to put forward a new way of expanding the determinant of fuzzy matrices and this process has led the foundation for defining the adjoint of square fuzzy matrices in a quite different way. In the process some properties of determinant as well as adjoint of fuzzy matrices are considered which are found to be almost analogus with the properties in crisp cases.

Software cost estimation is one of the most challenging task in project management. However, the process of estimation is uncertain in nature as it largely depends upon some attributes that are quite unclear during the early stages of development. In this paper a soft computing technique is explored to overcome the uncertainty and imprecision in estimation. The main objective of this research is to investigate the role of fuzzy logic technique in improving the effort estimation accuracy using COCOMO II by characterizing inputs parameters using Gaussian, trapezoidal and triangular membership functions and comparing their results. NASA (93) dataset is used in the evaluation of the proposed Fuzzy Logic COCOMO II. After analyzing the results it had been found that effort estimation using Gaussian member function yields better results for maximum criterions when compared with the other methods.

The paper develops an efficient people surveillance system capable of tracking multiple people on different terrains. Recorded video on rough terrains is affected by jitters resulting into significant error between the desired and captured video flow. Video stabilization is achieved by calculating the motion and compensational parameters using the LSE analytical solution to minimize the error between present and desired output video captured from an autonomous robot’s camera moving on a rough terrain used for surveillance of unidentified people. This is the first paper to the best of our knowledge which makes use of this method to design mobile wireless robot for human surveillance applications. As the method used is fast then conventional methods, making the proposed system a highly efficient surveillance system as compared to previous systems. The superiority of the method used is demonstrated using different evaluation parameters like RMCD, variability and reliability. The system can be used for surveillance of people under different environmental conditions.

Robust tracking of persons in real-world environments and in real-time is a common goal in many video applications. In this paper a computational system for the real-time tracking of multiple persons in natural environments is presented. Face detection has diverse applications especially as an identification solution which can meet the crying needs in security areas. The region extractor is based on the integration of skin-color, motion and silhouette features, while the face detector uses a simple, rule-based face detection algorithm and SVM. Exemplary results of the integrated system working in real-world video sequences. New intelligent processing methods, as well as security requirements make multiple-person tracking a hot area. This application is robust tracking in real-world environments and in real-time.