Abstract— In order to better control the 3-DOF helicopter model, this work is constructed to fulfill and satisfy high performance requirements while taking robustness, reference tracking, and actuator effort into consideration. As a result, the helicopter model dynamics were modeled and shown in a state-space for the first time utilizing the system identification technique. MATLAB was used to identify, design, and simulate the H∞ controller for the nonlinear system. This ideal controller, known as LQG (Linear Quadratic Gaussian), was created, modeled, and applied to the actual model for the nonlinear system using MATLAB. Both controllers' outcomes were displayed. An explanation of the fundamental distinctions between the H∞ controller and the LQG controller is also provided.

Keywords— H∞ controller, LQG controller, 3-DOF helicopter model, MATLAB/SIMULINK

Ahmed Mohammed Alfalah Asmaeil, (10-2025), ليبيا: The First International conference on electrical and electronic engineering Iceee2025, 29-34

Abstract:- This paper introduces a technique for modeling and effectively controlling the trajectory tracking of a robotic manipulator through an LQG controller optimized by the IPSO algorithm. Extensive simulations have been conducted within the MATLAB simulation environment. The LQG, which combines a Kalman Filter (KF) and a Linear Quadratic Regulator (LQR), is developed to follow the desired input for the robotic manipulator while minimizing the impact of process and measurement noise on its performance. The parameters of the LQG controller, which include the elements of the state and control weighting matrices for the LQR and KF, are optimally adjusted using the IPSO method. The validation of the proposed hybrid LQG-IPSO controller is conducted based on the control criteria parameters. The findings demonstrate that the proposed hybrid LQGIPSO controller can achieve excellent movement performance. To fully leverage the capabilities of the IPSO algorithm, careful adjustment and determination of IPSO parameters such as inertia weight, iteration count, acceleration constants, and particle quantity are essential. Therefore, The initial focus of this study is to conduct a comparative analysis of various fitness functions; simulation results indicate that the proposed hybrid LQGIPSO control method achieves commendable fitness outcomes with minimal steady-state error (ess). Following this, a comparison of the performances of a robotic manipulator using the hybrid LQG-IPSO controller, hybrid LQR-IPSO controller, LQG controller, and LQR controller is also presented. Based on the analysis, it can be concluded that superior performance parameters are attained with the hybrid LQG-IPSO controller when compared to its counterparts—the hybrid LQR-IPSO controller, LQG controller, and LQR controller—offering enhanced performance.

Keyw Keywords: Linear Quadratic Gaussian (LQG) controller, Linear Quadratic Regulator (LQR) controller, Improved Particle Swarm Optimization (IPSO) algorithm, Particle Swarm Optimization (PSO) algorithm and MATLAB etc

ABSTRACT:- The use of technologies from many fields, such as mass spectrometry, next-generation sequencing, or image processing, is common in experiments in the life sciences. Complex scripts are frequently used to govern data flow, data transformation, and statistical analysis when passing data between such tools. Such scripts not only tend to be platform dependant, but also tend to expand as the experiment goes on and are rarely clearly documented, which makes the experiment harder to reproduce. Workflow systems like KNIME Analytics Platform, which offers a platform for graphically linking tools and ensures the same results across various operating systems, aim to address these issues. systems that are frequently employed in the biological sciences and describe how they compare and contrast with KNIME. KNIME is an open source program that enables programmers and scientists to share their own extensions with the scientific community. The unified data model of KNIME allows for interoperability, and we describe a few additions from the life sciences that make it easier to explore, analyze, and visualize data. In addition, we mention additional workflow. According to the American Cancer Society, skin cancer is the most prevalent form of malignancy in humans. It is typically identified visually, with first clinical screenings, dermoscopic (skin-related) analysis, a biopsy, and histological examinations as potential follow-up steps. Errors (mutations) in the DNA of skin cells are the cause of skin cancer. The cells proliferate out of control and aggregate into a mass of cancer cells as a result of the mutations. In this paper, convolutional neural networks are used to attempt to categorize photos of skin lesions. The deep neural networks demonstrate enormous potential for classifying images while taking into account the extreme environmental heterogeneity. Due to the current state of technology, it is imperative to use machines rather than people to address the widespread problem of skin cancer. One of the best ways to address skin cancer issues is deep learning. Huge data, virtual reality, augmented reality, and miniservices are all used in the new research area of deep learning in contemporary technology. The advent of powerful arithmetic capabilities enabled deep learning applications using Mobile net (CNN) to revolutionize image classification. The various forms of skin cancer can be categorized using deep learning. Transfer Learning was used during the training on many models. The model's best level of accuracy was over 77.333 %. To guarantee the validity and reproducibility of the aforementioned result, the dataset employed is openly accessible.

Keywords: Artificial Intelligence (AI), Convolutional Neural Networks (CNNs), Deep Learning (DL), Skin Cancer, Image Classification, Knime Analytics Platform Software etc.

ABSTRACT:- This paper introduces design of different compensators and their output response for speed control of Direct Current (DC) motor with One and Two Degree of Freedom. DC motors are used in many industrial and commercial applications require higher performance, reliability, variable speed, easier controlling stability, accuracy, speed and position control of motor is required. In this paper in order to simulate the approach a MATLAB/SIMULINK model having one and two degree of freedom phase lead, phase lag and phase lead-lag compensators are constructed to control the modeled Direct Current (DC) motor for enhancement of static & dynamic response. In addition to that, the effect of adding two degree of freedom compensators on the transient and steady state response of the system is studied.

Keywords: DC Motor, Phase Lead Compensator, Phase Lag Compensator, Phase Lag-Lead, MATLAB/Simulink etc

ABSTRACT In this paper, modeling, simulation and performance analysis of the three phase permanent magnet brushless direct current (PMBLDC) motor using Fuzzy Logic Controller (FLC), Genetic Algorithm controller (FGAC), hybrid Neuro-Genetic Algorithm Controller (NGAC), Hybrid -Fuzzy controller (NFC) and Hybrid Neuro-Fuzzy controller (NFC) based on Genetic Algorithm (GA) under the effect of external load for improving the transient and steady state response is presented. The applications of the three phase permanent magnet brushless direct current (PMBLDC) motor are increasing day by day. In order to have proper utilization of these motors and to control them effectively it is important to have proper mathematical modeling of these motors. Similarly effective control these motors are also essential to have successful application of the devices across multiple domains. This paper handles both these important aspects. A m been derived to represent a three phase permanent magnet brushless direct current (PMBLDC) motor model, its utility can be noted from the fact that different motors can be analyzed easily using the proposed model. The mathematical model was subjected to open loop and closed loop analysis to study the stability and performance. In this paper in order to validate the approach a MATLAB/SIMULINK model having intelligent controllers are constructed to control the modeled three phase permanent magnet brushless direct current (PMBLDC) motor under the effect of external disturbances for enhancement of static & dynamic response. 

Keywords Permanent Magnet Brushless Direct Current (PMBLDC) motor, Fuzzy Logic Controller (FLC), Genetic Algorithm (GA), Hybrid Neuro-Fuzzy controller (NFC) based on Genetic Algorithm GA, MATLAB/Simulink etc.

Abstract:- In this paper, modeling, simulation and performance analysis of the permanent magnet brushless direct current (PMBLDC) motor using classical controller (PID Controller) and optimal controllers ( Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) optimal Controllers Based on Kalman Filter) for disturbance attenuation and noise suppression is presented. The applications of the permanent magnet brushless direct current (PMBLDC) motor are increasing day by day. In order to have proper utilization of these motors and to control them effectively it is important to have proper mathematical modeling of these motors. Similarly effective control these motors are also essential to have successful application of the devices across multiple domains. This paper handles both these important aspects. A mathematical model has been derived to represent permanent magnet brushless direct current (PMBLDC) motor model to study the stability and performance. In order to maintain the stability and to achieve the best performance by reducing disturbance attenuation and noise suppression, the three optimal controllers are developed in this paper. the system performance simulation of these optimal controllers with PID controller is presented using MATLAB program to control the modeled permanent magnet brushless direct current (PMBLDC) motor for disturbance attenuation and noise suppression.. The simulation results show that and Linear Quadratic Gaussian (LQG) Based on Kalman Filter with PID controller provides best as compared to PID controller, Linear Quadratic Regulator (LQR) with PID controller and Kalman Filter with PID controller.

Keyword: Permanent Magnet Brushless Direct Current (PMBLDC) motor, PID Controller, Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian (LQG), Integral Linear Quadratic Regulator , MATLAB/Simulink etc. 

In recent years, researchers are paying more attention to high efficiency, high process stability and eco-friendly nanofiber fabrication techniques. Among all of the nanofiber fabrication methods, electrospinning including solution electrospinning and melt electrospinning is the most promising method for nanofiber mass production. Compared to solution electrospinning, melt electrospinning could be applied in many areas such as tissue engineering and wound dressings due to the absence of any toxic solvent involvement. Capillary melt electrospinning generates only one jet with low efficiency. Hence, we have proposed polymer melt differential electrospinning (PMDES) method, which could produce multiple jets with smallest interjet distance of 1.1 mm from an umbrella shape spinneret, thus improving the production efficiency significantly. Many techniques such as material modification, suction wind, etc

Polyphenylene sulfide (PPS) superfine fiber is a promising high efficient material for high temperature dedusting. In this paper, the PPS superfine fiber was prepared by melt-electrospinning for the first time. In order to produce the finer fibers, the polypropylene (PP) was added to PPS and the effects of blending ratio, spinning voltage, spinning temperature, and spinning distance on resultant fibers were investigated systematically. The superfine fibers of PPS with an average diameter of 4.12 μm were successfully prepared at the optimized condition, which was one third of that by melt-spinning method. With the decrease of 76.21 % fibers diameter, the filter efficiency increased from 87.03 % to 98.05 %. The presented method provides a new way for the scalable and green fabrication of PPS superfine fibers.