Ali M. El-Rifaie

This paper presents a comprehensive evaluation of a new control technique for the sphere motor system, aimed at achieving accurate tracking, robust and dispersion of vibrations. Control methods include the determination of a nonlinear model and the application of feedback linearization, followed by the optimization of the proportional derivative (PD) coefficients through the Adaptive Neuro-Fuzzy Inference System. In addition, the system’s reaction to harsh environments is managed using Long Short-Term Memory. In order to gain a deeper understanding, symmetrical environmental disturbances and trajectories are introduced during the testing phase. The results demonstrate the superior performance of the control strategy, with reduced vibrations, faster recovery and confirmed tracking accuracy. In addition, the control method shows its adaptability and reliability, as evidenced by the significant reduction in CO2 emissions compared to conventional PD control methods. The use of symmetric trajectories and visualizations further emphasizes the behavior of the system under symmetric conditions, strengthening the effectiveness and applicability of the control strategy in real-world scenarios. Overall, this study presents a promising solution for converting complex systems under different conditions and making them potentially applicable in various industrial contexts.

One of the most significant barriers to broadening the use of solar energy is low conversion efficiency, which necessitates the development of novel techniques to enhance solar energy conversion equipment design. The correct modeling and estimation of solar cell parameters are critical for the control, design, and simulation of PV panels to achieve optimal performance. Conventional optimization approaches have several limitations when solving this complicated issue, including a proclivity to become caught in some local optima. In this study, a Growth Optimization (GO) algorithm is developed and simulated from humans’ learning and reflection capacities in social growing activities. It is based on mimicking two stages. First, learning is a procedure through which people mature by absorbing information from others. Second, reflection is examining one’s weaknesses and altering one’s learning techniques to aid in one’s improvement. It is developed for estimating PV parameters for two different solar PV modules, RTC France and Kyocera KC200GT PV modules, based on manufacturing technology and solar cell modeling. Three present-day techniques are contrasted to GO’s performance which is the energy valley optimizer (EVO), Five Phases Algorithm (FPA), and Hazelnut tree search (HTS) algorithm. The simulation results enhance the electrical properties of PV systems due to the implemented GO technique. Additionally, the developed GO technique can determine unexplained PV parameters by considering diverse operating settings of varying temperatures and irradiances. For the RTC France PV module, GO achieves improvements of 19.51%, 1.6%, and 0.74% compared to the EVO, FPA, and HTS considering the PVSD and 51.92%, 4.06%, and 8.33% considering the PVDD, respectively. For the Kyocera KC200GT PV module, the proposed GO achieves improvements of 94.71%, 12.36%, and 58.02% considering the PVSD and 96.97%, 5.66%, and 61.20% considering the PVDD, respectively.