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Abstract
Direct current (DC) motors have superior features such as operating at different speeds, being affordable and easily controllable. Therefore, DC motors have many uses, such as machine tools and robotic systems in many factories up to the textile industry. The PID controller is one of the most common methods used to control DC motors. PID is a feedback controller with the terms Proportional, Integral, and Derivative. The proper selection of P, I, and D parameters is critical for achieving the desired control in the PID controller. In this study, the transfer function of a DC motor is first obtained, and the speed of the DC motor is controlled by the PID controller using this transfer function. Then, Particle Swarm Optimization (PSO), an optimization method based on swarm intelligence, is used to adjust the P, I, and D parameters. By using the obtained P, I, and D coefficients, the speed of the DC motor is tried to be controlled, and the effect of the filter coefficient on the system output is examined. The performance of the proposed PSO-PID controller with successful results is given in tables and graphics. Control and optimization studies are carried out with MATLAB Simulink.
Abstract
Green wall systems have been introduced all over the world as a sustainable solution to combat the hot environment inside buildings and provide thermal comfort by improving the thermal efficiency of the buildings. This study aims to find out whether green walls can be used to manage the inside thermal conditions of Aqaba buildings. It is intended to lessen the impact of Aqaba's harsh warm climate on internal building spaces and achieve a thermal comfort level. A physical live experiment was used to detect the thermal impact of green walls on internal spaces. The thermal performances of two identical real-scale test rooms, one of which had a fixed green facade, were compared. This study concludes that green facades have a significant potential to promote buildings' thermal behaviour in the hot summer of Aqaba and thermally similar regions.
Abstract
The compressive behavior of self-consolidating concrete columns strengthened by hybrid confinement of polypropylene fiber rope and glass fiber-reinforced polymer sheet was investigated experimentally. We cast and tested eight column specimens under axial compression load. (Six confined SCC columns and two conventional SCC columns.) The concrete grade utilized is SCC M40. For reinforcement, the SCC columns are enclosed with GFRP wrap and polypropylene fiber rope in various volumetric ratios. The compressive resistance of a confined SCC column, strength enhancement, stress-strain relationship, ductility ratio, and load deflection relationship are the parameters studied. The results are compared to establish the adequacy of the confinement. The wrapping of GFRP increases the load-carrying capacity and modulus of elasticity.
Abstract
Whenever a new material is replaced in concrete, some other tests other than strength and durability need to be carried out to validate the viability of the material replaced. This study aims to investigate the sustaining capacity of the light weight concrete manufactured with compost and Ground Granulated Blast Furnace Slag (GGBS) for M sand and cement respectively subjected to high temperature. Four concrete samples are tested, which includes the control specimen and three specimens are opted based on the optimum mix arrived from the strength and durability studies. Thermogravimetric Analysis (TGA) is done on the samples and they are heated up to 1,000 °C. For the specimens tested, the loss in mass with respect to the temperature is obtained. It is noted that the mass loss of the concrete samples with 15% GGBS along with compost at 0 & 10% is found lower than the control specimen. Also, from the loss in mass, the loss of chemically bound water and free CH content can be found, which aids in contributing strength to the concrete. For the concrete to be sustainable, compost can be replaced at 10% and GGBS at 15%.
Abstract
This paper aims to investigate the effect of liquid heterogeneity on sloshing in a two-dimensional rectangular tank. The container is excited horizontally, and the density of the liquid at equilibrium can almost be considered a linear dependence of the depth. The linearized equations representing the sloshing phenomenon are solved numerically using ANSYS 2020 R2 software and analytically using the separation method of variables in conjunction with the Fourier analysis. On the other hand, a comparison study has been carried out between the obtained results and other works related to the same phenomenon. Overall, the results exhibit considerable effects of variation in the Heterogeneity Coefficient β on the free surface's motion and the pressure distribution within the liquid. Furthermore, the free surface of liquid heterogeneity is also affected by the variation of Excitation Frequency Ω and the Filling Rate h of the liquid in the tank.
Abstract
The redundant manipulators have more DOFs (degree of freedoms) than it requires to perform specified task. The inverse kinematic (IK) of such robots are complex and high nonlinear with multiple solutions and singularities. As such, modern Artificial Intelligence (AI) techniques have been used to address these problems. This study proposed two AI techniques based on Neural Network Genetic Algorithm (NNGA) and Particle Swarm Optimization (PSO) algorithm to solve the inverse kinematics (IK) problem of 3DOF redundant robot arm. Firstly, the forward kinematics for 3 DOF redundant manipulator has been established. Secondly, the proposed schemes based on NNGA and PSO algorithm have been presented and discussed for solving the inverse kinematics of the suggested robot. Thirdly, numerical simulations have been implemented to verify the effectiveness of the proposed methods. Three scenarios based on triangle, circular, and sine-wave trajectories have been used to evaluate the performances of the proposed techniques in terms of accuracy measure. A comparison study in performance has been conducted and the simulated results showed that the PSO algorithm gives 7% improvement compared to NNGA technique for triangle trajectory, while 2% improvement has been achieved by the PSO algorithm for circular and sine-wave trajectories.
Abstract
Various developing countries are confronted with serious environmental difficulties due to excessive resource utilization and insufficient waste management system. In particular, construction and demolition waste poses a grave threat to the environment, contributing to escalating energy consumption, the depletion of landfill capacities, and the generation of harmful noise and dust pollution. Consequently, the research community is tasked with the daunting challenge of devising effective strategies to incorporate this waste material in producing concrete, without compromising the critical strength and durability characteristics. The investigation aims to attain the aforementioned objective by examining the effects of using recycled aggregates as a distinct partial replacement of 0%, 5%, 10%, 15%, and 20% on the compressive and split tensile strength traits, contingent upon 7 and 28 days of age of curing. Experimental test results show that the optimal concrete production is achieved when 10% of coarse aggregate is replaced with recycled aggregate, maintaining 98% of the materials compressive and split tensile strength. To further validate the obtained experimental data, model equations were derived through regression analysis and the framed model equation is further assessed for accuracy using error analysis. In this study, a MATLAB program was utilized for prediction of compressive and split tensile strength with five distinct network types and the Levenberg-Marquardt algorithm is used for optimization. A comparative analysis was conducted between the regression analysis values and the performance of the ANN modelling. The findings demonstrate that the Artificial Neural Network (ANN) serves as a highly effective model, offering significantly improved accuracy in predicting the optimal correlation between compressive strength and split tensile strength of concrete.
Abstract
This study has developed adaptive synergetic control (ASC) algorithm to control the angular position of moving plate in the electronic throttle valve (ETV) system. This control approach is inspired by synergetic control theory. The adaptive controller has addressed the problem of variation in systems parameters. The control design includes two elements: the control law and adaptive law. The adaptive law is developed based on Lyupunov stability analysis of the controlled system, and it is responsible for estimating the potential uncertainties in the system. The effectiveness of the proposed adaptive synergetic control has been verified by numerical simulation using MATLAB/Simulink. The results showed that the ASC algorithm could give good tracking performance in the presence of uncertainty perturbations. In addition, a comparison study has been made to compare the tracking performance of ASC and that based on conventional synergetic control (CSC) for the ETV system. The simulated results showed that the performance of ASC outperforms that based on CSC. Moreover, the results showed that the estimation errors between the actual and estimated uncertainties are bounded and there is no drift in the developed adaptive law of ASC.
Abstract
The Cyber-Physical and Vehicle Manufacturing Laboratory, a model Industry 4.0 laboratory, is applying new innovative solutions to improve the quality of education. As part of this, a digital twin of the lab was designed and built, where users can practice. In the virtual space, it is possible to apply the known robot motion types, and the tool centre and wrist speed have been measured virtually. Robot control tasks can be performed “offline” using parameters. This information can then be transferred to the actual physical robot unit. The stable diffusion 1.5 deep learning model generates 2D geometric shapes for trajectory, allowing users to perform unique tasks during education. The Google Colab cloud-based service was used to teach our rendered-type dataset. For the 3D simulation frame, we used V-REP, which was developed on a desktop PC equipped with an Intel Core i5 7600K processor, Nvidia GTX1070 VGA with 8 GB of DDR5 VRAM, and 64 GB of DDR4 memory modules. The following material describes an existing industrial six-axis robot arm and its implementation, which can be controlled and programmed while performing virtual measurements after integrating into a Cyber-Physical system and using deep learning techniques.
