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

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. 

ABSTRACT:- For electrical drives good dynamic performance is mandatory so as to respond to the changes in command speed and torques, so various speed control techniques are being used for real time applications. The speed of a Three Phase Induction Motor can be controlled using various controllers like PID Controller, Fuzzy Logic Controller, Genetic Algorithm (GA) controller and Hybrid FuzzyGenetic Controller. Fuzzy-Genetic Controller is recently getting increasing emphasis in process control applications. The paper describes application of Hybrid Fuzzy-Genetic Controller in an enhancement of Static & Dynamic Response of the Three Phase Induction Motor under the effect of the external disturbances and noise that uses the Fuzzy-GA Controller for enhancement of Static & Dynamic Response of the Three Phase Induction Motor under the effect of the external disturbances and noise is implemented in MATLAB/SIMULINK. The simulation study indicates the superiority Hybrid Fuzzy-Genetic Controller over the Genetic Algorithm and fuzzy logic controller separately. This control seems to have a lot of promise in the applications of power electronics. The speed of the Three Phases Induction Motor Drives can be adjusted to a great extent so as to provide easy control and high performance. There are several conventional and numeric types of controllers intended for controlling the Three Phase Induction Motor speed and executing various tasks: PID Controller, Fuzzy Logic Controller; or the combination between them: Fuzzy-Swarm, Fuzzy-Neural Networks,  

Fuzzy-Genetic Algorithm, Fuzzy-Ants Colony. We describe in this paper the use of Hybrid FuzzyGenetic Controller for enhancement of Static & Dynamic Response of the Three Phases Induction Motor Drives under the effect of the external disturbances. In this case, the obtained results were simulated on Simulink of Matlab.

Keywords: Fuzzy Logic Controller (FLC), Genetic Algorithm (GA), Hybrid Fuzzy-Genetic Controller (FLC-GA) 

Abstract: For electrical drives good dynamic performance is mandatory so as to respond to the changes in command speed and torques, so various speed control techniques are being used for real time applications. The speed of a DC motor can be controlled using various controllers like PID Controller, Fuzzy Logic Controller, Particle Swarm Optimization (PSO) and Hybrid Fuzzy-PSO Controller. Fuzzy-PSO Controller is recently getting increasing emphasis in process control applications. The paper describes application of Hybrid Fuzzy-PSO Controller in an enhancement of stability and accuracy of the SEDC Motor under the effect of the external disturbances and noise that uses the Fuzzy-PSO Controller for enhancement of stability and accuracy of the SEDC Motor under the effect of the external disturbances and noise is implemented in MATLAB/SIMULINK.  The simulation study indicates the superiority Hybrid FuzzyPSO Controller over the Particle Swarm Optimization (PSO) and fuzzy logic controller separately. This control seems to have a lot of promise in the applications of power electronics. The speed of the SEDC motor can be adjusted to a great extent so as to provide easy control and high performance. There are several conventional and numeric types of controllers intended for controlling the SEDC motor speed and executing various tasks: PID Controller, Fuzzy Logic Controller; or the combination between them: FuzzySwarm, Fuzzy-Neural Networks, Fuzzy-Genetic Algorithm, Fuzzy-Ants Colony, Fuzzy-Particle Swarm Optimization. We describe in this paper the use of Hybrid Fuzzy-PSO Controller for enhancement of stability and accuracy of the SEDC Motor under the effect of the external disturbances and noise We describe in this paper the use of Particle Swarm Optimization (PSO) for designing an optimal fuzzy logic controller of a SEDC Motor. In this case, our approach will optimize the membership functions of a fuzzy logic controller (FLC) using PSO and the obtained results were simulated on Matlab environment.

Keywords: Separately Excited DC Motor (SEDC), Fuzzy Logic Controller (FLC), Particle Swarm Optimization(PSO).

Abstract— This paper presents an enhancement of stability and accuracy of the separately excited DC (SEDC) motor under the effect of the external disturbances (dynamic load changes) and noise by using conventional Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers. DC machines are characterized by their versatility. The aim of the proposed schemes is to improve tracking performance of separately excited DC motor as compared to the Proportional Integral (PI) control strategy. Performance of these new controllers has been verified through simulation using MATLAB/SIMULINK package. In this paper, The SEDC motor model is chosen according to its good electrical and mechanical performances more than other DC motor models. The modeling and simulation of the SEDC motor was done using the software package MATLAB/SIMULINK. A speed controller has been designed successfully for closed loop operation of the SEDC motor so that the motor runs very closed to the reference speed. The simulated system has a fast response with small overshoot and zero steady state error. Excellent results added to the simplicity of the drive system, makes the ANN based control strategy suitable for a vast number of industries, paper mills etc.