The Permanent Magnet Synchronous Motor (PMSM) has been widely used in the low to medium power system due to its characteristics of high efficiency, high torque to inertia ratio, high reliability and fast dynamic performance. With the advent of the vector control methods,permanent magnet synchronous motor can be operated like separately excited dc motor high performance application. The complexity of PI controller tuning and high response time is overcome by Fuzzy controller. Which has less response time and high accuracy without any mathematical calculation .This paper presents a simulation of speed control system on fuzzy logic approach for an indirect vector controlled permanent magnet synchronous drive by applying space vector modulation. The design, analysis and simulation of the proposed system is done using MATLAB version 8.

This paper proposes the simulation of Permanent Magnet Synchronous Motor (PMSM) drive using Iterative Learning (ILC) Controller. The Iterative Learning Controller an approach to improve the tracking performance of a system that operates repetitively over a fixed time interval. In this paper, traditional Pi-controller based torque control method is compared with the Iterative Learning Controller based torque control method and the results are obtained. The simulation result shows that ILC controller reduces the torque ripples much better than Pi-controller. The design, analysis, and simulation of ILC based controller method is simulated using MATLAB SIMULINK R2010b.

This article aims to provide a technique for identifying and categorizing interturn insulation problems in variable-speed motor drives by combining Salp Swarm Optimization (SSO) with Recurrent Neural Network (RNN). The goal of the proposed technique is to detect and classify Asynchronous Motor faults at their early stages, under both normal and abnormal operating conditions. The proposed technique uses a recurrent neural network in two phases to identify and label interturn insulation concerns, with the first phase being utilised to establish whether or not the motors are healthy. In the second step, it discovers and categorises potentially dangerous interturn errors. The SSO approach is used in the second phase of the recurrent neural network learning procedure, with the goal function of minimizing error in mind. The proposed CSSRN technique simplifies the system for detecting and categorizing the interturn insulation issue, resulting in increased system precision. In addition, the proposed model is implemented in the MATLAB/Simulink, where metrics such as accuracy, precision, recall, and specificity may be analysed. Similarly, existing methods such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), and Salp Swarm Algorithm Artificial Neural Network (SSAANN) are utilised to evaluate metrics such as Root mean squared error (RMSE), Mean bias error (MBE), Mean absolute percentage error (MAPE), consumption, and execution time for comparative analysis.

Permanent magnet synchronous motors (PMSM) are widely used in high performance applications demanding smooth torque, accurate speed and position control. However the existing PMSM drive produces high torque ripples. The major challenge is to develop new torque control schemes to minimize the torque ripples so as to suit the application requirements. This paper describes two instantaneous field oriented torque control techniques − 1) Iterative learning controller (ILC) in combination with hysteresis pulse width modulation and 2) Iterative learning controller in combination with space vector pulse width modulation (SVPWM) to reduce torque ripples in PMSM drive. These techniques are simulated for drive performance under different load conditions using the Simulink library in MATLAB Version 10.0 and the results obtained are presented and compared. The simulation results show that the ILC with SVPWM technique has less torque ripples.

Cement-based materials are increasingly and widely employed in infrastructure development; however, they pollute our environment by generating carbon dioxide, which is detrimental to our civilization. In self-cleaning concrete, photocatalysts accelerate the decomposition of organic particles; thus, photocatalytic degradation of gaseous pollutants could reduce pollution. The incorporation of photocatalytic components enhanced the mechanical self-cleaning properties of cement mortar. In this study, 46 percent by weight of rutile TiO₂ was added to mortar, and the results were compared to those of a control sample. On the proposed mortar cubes, both fresh mortar and hardening mortar experiments were conducted. Because the initial and final setting times of TiO₂ differ from those of conventional cement mortar, the surplus TiO₂water-cement ratio had to be modified. The adaptability of the sol-gel method enables the use of various process parameters to influence the properties of the produced TiO₂ nanoparticles. The compressive strength was calculated for 7, 14, 21, and 28days, and an ultrasonic velocity test was performed after 28days. On mortar samples, acid and sulfate attack experiments were performed. The M-3 mortar mixture containing 5 rutile exhibited the highest level of strength compared to the other mixtures. The M-3 exhibits a strength that is 10.96 greater than that of the control mix. The impact of acid and sulfate attack on the strength of mix M-2 is relatively modest in comparison to other mixtures. Using RhB (rhodamine color) discoloration under UV light, such as sunlight, the photocatalytic mortar is concentrated; a typical test for self-cleaning cementitious materials reveals the presence of more photocatalytic material, which yields the best results.