Dynamic Performance Analysis of a Five-Phase PMSM Drive Using Model Reference Adaptive System and Enhanced Sliding Mode Observer

This paper aims to evaluate the dynamic performance of a five-phase PMSM drive using two different observers: sliding mode (SMO) and model reference adaptive system (MRAS). The design of the vector control for the drive is firstly introduced in details to visualize the proper selection of speed and current controllers’ gains, then the construction of the two observers are presented. The stability check for the two observers are also presented and analyzed, and finally the evaluation results are presented to visualize the features of each sensorless technique and identify the advantages and shortages as well. The obtained results reveal that the de-signed SMO exhibits better performance and enhanced robustness compared with the MRAS under different operating conditions. This fact is approved through the obtained results considering a mismatch in the values of stator resistance and stator inductance as well. Large deviation in the values of estimated speed and rotor position are observed under MRAS, and this is also accompanied with high speed and torque oscillations

Robust Flux and Speed State Observer Design for Sensorless Control of a Double Star Induction Motor

In this paper, a robust flux and speed observer for sensorless control of a double star induction motor is presented. Proper operation of vector control of the double star induction motor requires reliable information from the process to be controlled. This information can come from mechanical sensors (rotational speed, angular position). Furthermore, mechanical flux and speed sensors are generally expensive and fragile and affect the reliability of the system. However, the control without sensors must-have performance that does not deviate too much from that which we would have had with a mechanical sensor. In this framework, this work mainly deals with the estimation of the flux and speed using a robust state observer in view of sensorless vector control of the double star induction motor. The evaluation criteria are the static and dynamic performances of the system as well as the errors between the reference values and those estimated. Extensive simulation results and robustness tests are presented to evaluate the performance of the proposed sensorless control scheme. Furthermore, under the same test conditions, a detailed comparison between the proposed state observer and the sliding mode-MRAS technique is carried out where the results of its evaluation are investigated in terms of their speed and flux tracking capability during load and speed transients and also with parameter variation. It is worth mentioning that the proposed state observer can obtain both high current quality and low torque ripples, which show better performance than that in the MRAS system

Optimization of an Autonomous Mobile Robot Path Planning Based on Improved Genetic Algorithms

Mobile robots are intended to operate in a variety of environments, and they need to be able to navigate and travel around obstacles, such as objects and barriers. In order to guarantee that the robot will not come into contact with any obstacles or other objects during its movement, algorithms for path planning have been demonstrated. The basic goal while constructing a route is to find the fastest and smoothest route between the starting point and the destination. This article describes route planning using the improvised genetic algorithm with the Bezier Curve (GA-BZ). This study carried out two main experiments, each using a 20x20 random grid map model with varying percentages of obstacles (5%, 15%, and 30% in the first experiment, and 25% and 50% in the second). In the initial experiments, the population (PN), generation (GN), and mutation rate (MR) of genetic algorithms (GA) will be altered to the following values: (PN = 100, 125, 150, or 200; GN = 100, 125, 150; and MR = 0.1, 0.3, 0.5, 0.7) respectively. The goal is to evaluate the effectiveness of AMR in terms of travel distance (m), total time (s), and total cost (RM) in comparison to traditional GA and GA-BZ. The second experiment examined robot performance utilising GA, GA-BZ, Simulated Annealing (SA), A-Star (A*), and Dijkstra's Algorithms (DA) for path distance (m), time travel (s), and fare trip (RM). The simulation results are analysed, compared, and explained. In conclusion, the project is summarised

Toward an Advanced Gas Composition Measurement Device for Chemical Reaction Analysis

The research details the development of a reactor-based monitoring system designed to identify and monitor gases generated within industrial chemical reactors. Consisting of nine MQ and DHT11 sensors, this reactor design allows for simultaneous measurement of temperature and humidity within the sample. Using a sensor array methodology, this research utilizes multiple sensors to collect and process analog signals to improve the accuracy of gas identification within samples. These analog signals obtained from the sensors are processed by an Arduino Mega 2560 microcontroller using the Arduino IDE software. The research, conducted on ten different samples, shows methane (CH4), hydrogen (H2), and alcohol (C2H6O) as the most concentrated gases. Notably, certain samples such as batik waste, honey, Robusta coffee, and sambal have a significant impact on methane gas concentrations. In addition, substances such as Robusta Coffee, Sprite, Syrup, and Oyster Sauce have a significant effect on hydrogen gas concentrations, while Robusta Coffee, Sambal, Arabica Coffee, and Pepper have a significant effect on alcohol gas concentrations. In addition, of the nine MQ sensors used, the MQ3, MQ4, and MQ8 are particularly effective at detecting alcohol, methane, and hydrogen gases, respectively, in the samples tested

Comparative Analysis Of Path-Finding Algorithm On Unrestricted Virtual Object Movable For Augmented Reality

Pathfinding is a necessary method in gaming, especially in 3D games. Path-finding is used by an object to find paths from one place to another based on the state of the map and other objects. Path-finding requires algorithms that can process quickly and produce the shortest path to reach a destination location. In this paper, path-finding is applied in Augmented Reality. The Intel RealSense camera is used to reconstruct the real environment and display virtual objects. The path-finding algorithm is reviewed that the A*, A* smooth, and Navigation Mesh algorithms. Each of these algorithms is implemented into the Unity 3D object game. Each object game will move simultaneously to the destination point with different starting positions and goals by avoiding many obstacles. It is obtained in the 3D simulation that the A* smooth algorithm is superior to the A* algorithm and NavMesh. The travel time required for a game object with an A* smooth algorithm is 1.54 seconds faster, and 1.4 seconds compared to A* and NavMesh. Virtual objects can use pathfinding algorithms as a navigation path in the real world. The navigation path is located in the grid area generated by Intel RealSense cameras

Vision-Based Line Following Robot in Webots

Line following robot is one of the popular robots commonly used for educational purposes. The most widely used sensors for the robots are photoelectric sensors. However, it is irrelevant, along with the development of autonomous vehicles and robotic vision. Robotic vision is a robot that can obtain information through image processing by the camera. The camera installed on the line following robot aims to detect image-based lines and to navigate the robot to follow the path. This paper proposed a method of image preprocessing along with its robot action for line-following robots. This includes image preprocessing such as dilation, erosion, Gaussian filtering, contour search, and centerline definition to detect path lines and to determine the proper robot action. The implementation of the robot is simulated using Webots simulator. OpenCV and Python are utilized to design line detection systems and robot movements. The simulation result shows that the method is implemented properly, and the robot can follow a different type of path lines such as zigzag, dotted, and curved line. The resolution of the cropped-image frame is the fundamental parameter in detecting path lines

K-Nearest Neighbor of Beta Signal Brainwave to Accelerate Detection of Concentration on Student Learning Outcomes

Intelligence, creativity, emotions, memory, and body movements are human activities controlled by the brain. While humans do an activity, the neural network in the brain produces an electrical current in the form of waves. Brainwaves are one of the biometric features that can be used to identify individual characteristics based on their activity and behavior patterns. Identifying individual characteristics requires a brain activity measurement using an Electroencephalogram (EEG). Measuring brainwaves requires a reliable, prominent, and constant activity stimulation by applying a series of cognitive tasks, such as the Culture Fair Intelligence Test (CFIT) and the Indonesian Competency Test (CT). This research aims to obtain relation patterns and accelerate the detection between brain concentration and learning outcomes. Beta signal acquisition is obtained from junior high school students while performing cognitive tasks. After data is obtained, the signal is extracted using the Fast Fourier Transform (FFT) to get its peak signal. The peak signal from FFT data on CFIT generated an average score of 0.214 with the category of Average. Meanwhile, the peak signal on CT generated an average score of 0.246 with the category "C+". K-Nearest Neighbor (KNN) algorithm is applied to identify patterns from extraction data with K-value=5; then, the accuracy is assessed using K-Fold Cross Validation with Kvalue=11. The resulting accuracy is 94.59%. Based on the KNN classification results, students' learning outcomes are influenced by their concentration. This research has successfully shortened the CFIT evaluation time from three days to one day

Implementing PID Control on Arduino Uno for Air Temperature Optimization

This research investigates the precise regulation of liquid filling in tanks, specifically focusing on water storage systems. It employs the Proportional-Integral-Derivative (PID) control method in conjunction with an HC-SR04 ultrasonic sensor and an Arduino Uno microcontroller. Given the paramount importance of water as a resource, accurate management of its storage is of utmost significance. The PID control method, known for its rapid responsiveness, minimal overshoot, and robust stability, effectively facilitates this task. Integrating the ultrasonic sensor and microcontroller further augments the precision of water level regulation. The article expounds upon the foundational principles of the PID control method and elucidates its application in the context of liquid tank filling. It offers a comprehensive insight into the hardware configuration, encompassing pivotal components such as the Arduino Uno microcontroller, HC-SR04 ultrasonic sensor, and the L298 driver responsible for water pump control. The experimental approach is meticulous, presenting results from tests involving the Proportional Controller, Proportional Integral (PI) Controller, and Proportional Integral Derivative (PID) Controller. These tests rigorously analyze the impact of varying Proportional Gain (Kp), Integral Gain (Ki), and Derivative Gain (Kd) parameters on crucial performance metrics such as response time, overshoot, and steady-state error. The findings underscore the critical importance of an optimal parameter configuration, emphasizing the delicate equilibrium between response speed, precision, and error minimization. This research significantly advances PID control implementation in liquid tank filling, offering insights that pave the way for developing more efficient liquid management systems across various sectors. The identified optimal parameter configuration is Kp = 5.0, Ki = 0.3, and Kd = 0.2

IoT-based Lava Flood Early Warning System with Rainfall Intensity Monitoring and Disaster Communication Technology

A lava flood disaster is a volcanic hazard that often occurs when heavy rains are happening at the top of a volcano. This flood carries volcanic material from upstream to downstream of the river, affecting populous areas located quite far from the volcano peak. Therefore, an advanced early warning system of cold lava floods is inarguably vital. This paper aims to present a reliable, remote, Early Warning System (EWS) specifically designed for lava flood detection, along with its disaster communication system. The proposed system consists of two main subsystems: lava flood detection and disaster communication systems. It utilizes a modified automatic rain gauge; a novel configured vibration sensor; Fuzzy Tree Decision algorithm; ESP microcontrollers that support IoT, and disaster communication tools (WhatsApp, SMS, radio communication). According to the experiment results, the prototype of rainfall detection using the tipping bucket rain gauge sensor can measure heavy and moderate rainfall intensities with 81.5% accuracy. Meanwhile, the prototype of earthquake vibration detection using a geophone sensor can remove noise from car vibrations with a Kalman filter and measure vibrations in high and medium intensity with an accuracy of 89.5%. Measurements from sensors are sent to the webserver. The disaster mitigation team uses data from the webserver to evacuate residents using the disaster communication method. The proposed system was successfully implemented in Mount Merapi, Indonesia, coordinated with the local Disaster Deduction Risk (DDR) forum. Doi: 10.28991/esj-2021-SP1-011 Full Text: PD

Real World And Virtual Object Obstacle In Augmented Reality Using Scene Perception

Augmented Reality is a technique for combining digital content with the real world in real-time. Intel RealSense 3D cameras are used to produce digital content in markerless based Augmented Reality. This camera reconstructs a real environment in three dimensions. Scene perception is a method for reconstructing real environments in three dimensions. Utilization of this camera in Augmented Reality in the form of an autonomous agent. An autonomous agent has a navigation function to get to the destination point by searching for paths called pathfinding. Autonomous agents have three behaviors, namely, seek, arrive, and action selection. These behaviors are used autonomous agents to get to the destination point by avoiding virtual and real obstacles that exist in the real world. The scene perception method is used to make a mesh. This mesh is a virtual grid in the real world that is used as an Augmented Reality area. The navigation results of the autonomous agent using the scene perception method in Augmented Reality can work properly. Autonomous agents can go to their destination point by avoiding virtual and real obstacles