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All Journal Communications in Science and Technology Journal of Robotics and Control (JRC)
Alyazidi, Nezar M.
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Enhanced Trajectory Tracking of 3D Overhead Crane Using Adaptive Sliding-Mode Control and Particle Swarm Optimization Alyazidi, Nezar M.; Hassanine, Abdalrahman M.; Mahmoud, Magdi S.; Ma'arif, Alfian
Journal of Robotics and Control (JRC) Vol 5, No 1 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i1.18746

Abstract

Cranes hold a prominent position as one of the most extensively employed systems across global industries. Given their critical role in various sectors, a comprehensive examination was necessary to enhance their operational efficiency, performance, and facilitate the control of transporting loads. Furthermore, due to the complexities involved in disassembling and reinstalling cranes, as well as the challenges associated with precisely determining system parameters, it became essential to implement adaptive control methods capable of efficiently managing the system with minimal resource requirements. This work proposes a trajectory tracking control using adaptive sliding-mode control (SMC) with particle swarm optimization (PSO) to control the position and rope length of a 3D overhead crane system with unknown parameters. The PSO is mainly used to identify the model and estimate the uncertain parameters. Then, sliding-mode control is adapted using the PSO algorithm to minimize the tracking error and ensure robustness against model uncertainties. A model of the systems is derived assuming changing rope length. The model is nonlinear of second order with five states, three actuated states: position x and y, and rope length l, and two unactuated states, which are the rope angles θx and θy. The system has uncertain parameters, which are the system’s masses Mx, My and Mz, and viscous damping coefficients Dx, Dy and Dy. A simulation study is established to illustrate the influence and robustness of the developed controller and it can enhance the tracking trajectory under different scenarios to test the scheme.
Data-driven modeling approaches for pressure drop prediction in a multi-phase flow system Alyazidi, Nezar M.; F. Bawazir, Aiman; AL-Dogai, Ala S.
Communications in Science and Technology Vol 9 No 2 (2024)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.9.2.2024.1430

Abstract

Accurate prediction of pressure drops in multi-phase flow systems is essential for optimizing processes in industries such as oil and gas, where operational efficiency and safety depend on reliable modeling. Traditional models often need help with the complexities of multi-phase flow dynamics, resulting in high relative errors, particularly under varying flow regimes. In this study, we simulate a comprehensive multiphase flow experimental data collected from the lab. This study presents innovative methods for accurately modeling pressure drops in multi-phase flow systems. It also studies the complicated dynamics of multi-phase flows, which are flows with more than one phase at the same time. It does this by using two different data-driven models, nonlinear ARX and Hammerstein-Wiener, instead of neural networks (NNs), so that the models don’t get too good at fitting environments with lots of changes and little data. Our research applies system identification approaches to the intricacies of this domain, providing new insights into choosing the best appropriate modeling strategy for multi phase flow systems, taking into account their distinct properties. The experimental results show that the nonlinear Hammerstein-Wiener and ARX models were much better than other methods, with fitting accuracy rates of 81.12% for the Hammerstein-Wiener model and 86.52% for the ARX model. This study helps the creation of more advanced control algorithms by providing a reliable way to guess when the pressure drops and showing how to choose a model that fits the properties of the multi-phase flow. These findings contribute to enhanced pressure management and optimization strategies, setting a foundation for future studies on real-time flow control and broader industrial applications.
Co-Authors AL-Dogai, Ala S. Alfian Ma’arif F. Bawazir, Aiman Hassanine, Abdalrahman M. Mahmoud, Magdi S.