Rooh ul Amin

A comparison of the performance of some extreme learning machine empirical models for predicting daily horizontal diffuse solar radiation in a region of southern Iran

ABSTRACT This study uses the empirical models of extreme learning machine (ELM) method to predict daily horizontal diffuse solar radiation (HDSR). As a possibility for modification, the recent hybrid ELM methods such as complex ELM (C-ELM), self-adaptive evolutionary ELM (SaE-ELM), and online sequential ELM (OS-ELM) have been developed for the prediction of the daily HDSR. The empirical model of ELM predicts the HDSR using clearness index as the sole predictor. For this aim, two types of correlations are evaluated: (1) the diffuse fraction-clearness index and (2) the diffuse coefficient-clearness index. The measured diffuse and global solar radiation data sets of southern Iranian cities (Yazd, Shiraz, Bandar Abbas, Bushehr, and Zahedan) are utilized to evaluate the models. The precision of the C-ELM, SaE-ELM, OS-ELM, and ELM models is evaluated for different regions on the basis of five statistical performance evaluation parameters. The results confirm that the performance of hybrid ELM is pretty accurate and trustworthy. Fully complex ELM exhibits the best performance among these hybrid methods, having values of 0.87 and 0.30 for R2 and RMSE measures, respectively, in the testing phase. Therefore, it is able to be recognized as an appropriate tool for daily solar radiation forecasting issues.

Modelling and robust attitude trajectory tracking control of 3-DOF four rotor hover vehicle

Purpose The purpose of this paper is to present μ-synthesis-based robust attitude trajectory tracking control of three degree-of-freedom four rotor hover vehicle. Design/methodology/approach Comprehensive modelling of hover vehicle is presented, followed by development of uncertainty model. A μ-synthesis-based controller is designed using the DK iteration method that not only handles structured and unstructured uncertainties effectively but also guarantees robust performance. The performance of the proposed controller is evaluated through simulations, and the controller is also implemented on experimental platform. Simulation and experimental results validate that μ-synthesis-based robust controller is found effective in: solving robust attitude trajectory tracking problem of multirotor vehicle systems, handling parameter variations and dealing with external disturbances. Findings Performance analysis of the proposed controller guarantees robust stability and also ensures robust trajectory tracking performance for nominal system and for 15-20 per cent variations in the system parameters. In addition, the results also ensure robust handling of wind gusts disturbances. Originality/value This research addresses the robust performance of hover vehicle’s attitude control subjected to uncertainties and external disturbances using μ-synthesis-based controller. This is the only method so far that guarantees robust stability and performance simultaneously.

Teaching and Learning Object-Oriented Analysis and Design with 3D Game

Game based-learning methods are largely adopted in many academic fields. The multi-dimensional virtual reality based learning games help the learners to understand the problem dynamics effectively and to construct optimal solutions for them. This shows that games help learners in problem solving and enhancing their performance. Still the implementation of game-based learning is new to software engineering learning. Therefore, there is a need to design multi-dimensional game-based learning environments for teaching and learning software engineering courses and to support it with empirical evidence. To fill this research gap, this paper aims to introduce a multi-dimensional (3D) game for learning object-oriented analysis and design course. This game basically emulates the real-world projects handed over to students as a part of object oriented analysis and design course. The empirical investigation showed that by using the proposed 3D game students learned through practical experience and it eventually helped to enhance their learning outcome, knowledge on the concepts of software process, and proved conducive in timely completion of the project.

Wearable learning technology: A smart way to teach elementary school students

The immense growth of technology in past few decades has its due effect on academia, as well. To meet the growing needs of present generations anticipation for technology, smart wearable devices are introduced in the field of education. So far, the smart technology is becoming popular for its effective usage in education among university level students. However, making use of smart wearable devices for educating young children of elementary school level is still a challenge. It is yet to be explored that how do the children of elementary school level perceive and react to smart wearable devices? In this paper, we aim to state the usage of smart wearable technology for learning and education purpose of elementary level children. This paper reports the feasibility and explains the benefits of using smart wearable technology while teaching elementary school students.

Design of mixed sensitivity H∞ control for four-rotor hover vehicle

During the past one decade, multirotor unmanned aerial vehicles (MUAVs)s control has become an active research area for flight control community. The reasons behind are the benefits offered by MUAV and its numerous commercial and consumer applications. In this paper, design of a robust attitude controller for four-rotor hover vehicle that is a kind of MUAV, based on H∞ mixed-sensitivity synthesis is presented. Firstly, a linear model of four-rotor hover vehicle is derived, which is formulated as a nominal plant perturbed by parameter and unstructured uncertainties. H∞ mixed-sensitivity controller is designed due to the uncertain nature of the system. The efficacy of the proposed controller is proved by simulations and experiment. Performance analysis, simulation, and experimental results show that the proposed controller guarantees stability, exhibits good performance, and robustness margins against the parameter and unstructured uncertainties.

An adaptive sliding mode control based on radial basis function network for attitude tracking control of four rotor hover system

An adaptive sliding mode controller based on radial basis function network has been proposed to control the three degree-of-freedom four rotor hover system. Mathematical model of hover system is developed using equations of motions and radial basis function network is used to approximate the unmodeled dynamics of the system. Sliding mode controller based on adaptive law is designed to achieve chattering free, good attitude tracking control in the presence of modeling uncertainties. The stability of the proposed controller has been proved using the Lyapunov stability theory. Simulation results exhibit that the adaptive sliding mode controller in conjunction with radial basis function network achieves better control performance than the traditional PID, LQR controllers even without knowing the exact dynamical model.