Estimation of Voltage Regulator Stable Region Using Radial Basis Function Neural Network

Disturbance to the voltage regulator (VR) output caused by the abrupt change in load current can be compensated using an output capacitor with an internal parasitic element called the equivalent series resistance (ESR). However, the ESR value changes due to aging and temperature change factors, thereby creating a VR stable region in terms of ESR. In practice, time-consuming and high-expertise manual characterization is required to characterize the VR stable region during the design and manufacturing phases. Therefore, this research aims to develop an efficient and effective VR characterization method. In this work, the radial basis function neural network (RBFNN) approach was implemented to estimate the stable region. Results show that the RBFNN approach yields a stable region with higher estimation accuracy and faster characterization time than those of manual characterization. VR characterization using the RBFNN approach can efficiently and effectively estimate the VR stable region

Critical Equivalent Series Resistance Estimation for Voltage Regulator Stability Using Hybrid System Identification and Neural Network

Output capacitor in the voltage regulator (VR) circuit ensures stability especially during fast load transients. However, the capacitor parasitic, namely equivalent series resistance (ESR), may cause unstable VR operation. VR characterization in terms of ESR suggests stable range of capacitor ESR based on the ESR tunnel graph in the VR datasheet. Specifically, the stable ESR range is the critical ESR value, which lies on the failure region boundary of ESR tunnel graph. New or updated ESR tunnel graph through characterization is required for new product development or quality assurance purpose. However, the characterization is typically conducted manually in industry, thereby increases the manufacturing time and cost. Therefore, this work proposed a characterization approach that can reduce the time to determine the ESR tunnel graph based on the hybrid system identification and neural network (SI-NN) approach. This method utilised system identification (SI) to estimate the VR circuit model for certain operating points before predicting the transfer function coefficients for the remaining points using radial basis function neural network (RBFNN). Eventually, the critical ESR of failure region boundary was estimated. This hybrid SI-NN approach able to reduce the number of data that would be acquired manually to 25% compared to manual characterization, while provides critical ESR estimation with error less than 2%

Neural Network Based Prediction of Stable Equivalent Series Resistance in Voltage Regulator Characterization

High demand on voltage regulator (VR) currently requires VR manufacturers to improve their time-to-market, particularly for new product development. To fulfill the output stability requirement, VR manufacturers characterize the VR in terms of the equivalent series resistance (ESR) of the output capacitor because the ESR variation affects the VR output stability. The VR characterization outcome suggests a stable range of ESR, which is indicated in the ESR tunnel graph in the VR datasheet. However, current practice in industry manually characterizes VR, thereby increasing the manufacturing time and cost. Therefore, an efficient method based on multilayer neural network has been developed to obtain the ESR tunnel graph. The results show that this method able to reduce the VR characterization time by approximately 53% and achieved critical ESR prediction error less than 5%. This work demonstrated an efficient and effective approach for VR characterization in terms of ESR

Integration of 3D printing in computer-aided design and engineering course

Engineering students at an undergraduate level typically learn the design aspect and concept through lectures and practical sessions using computeraided software. However, the current computer-aided design and engineering (CAD/CAE) course did not expose the students to apply and relate the latest advanced technologies to solve global issues, for instance as listed in the United Nations Sustainable Development Goals (UN SDG). Therefore, an improved CAD/CAE course taken by the students of the Electrical and Electronic Engineering Programme in Universiti Kebangsaan Malaysia integrates 3D printing and conduct their project based on UN SDG themes. A total of 22 projects was produced, which involves both mechanical and electrical design with some of the physical models were 3D printed. Thus, students able to strengthen their understanding of the design concept through the integration of 3D printing and simultaneously aware of the current global issues

Visual servo algorithm of robot arm simulation for dynamic tracking and grasping application

Health pandemics such as Covid-19 have drastically shifted the world economics and boosted the development of automation technologies in the industries for continuous operation without human intervention. This paper elaborates on an approach to dynamically track and grasp moving objects using a robot arm. The robot arm has an eye-in-hand (EIH) configuration, where a camera is installed on the robot arm’s end effector. The working principle of the robot arm in this paper is mainly dependent on the recognition of augmented reality markers, i.e., Aruco markers, placed on the dynamically moving target object with continuous tracking. Then, the proposed system updates the predicted location for the markers using the Kalman filter for performing grasping. The proposed approach identifies the Aruco marker on the target object and estimates the object’s location using previous states, and performs grasping at the exact predicted location. When extracted information is updated, the vision system also implements a feedback control system for stability and reliability. The proposed approach is tested using simulation of the dynamic moving object with different speeds and directions. The robot arm with the Kalman filter can track and grasp the dynamic object at a speed of 0.2m/s with a 100% success rate while obtaining an 80% success rate at a speed of 0.3m/s. In conclusion, the moving object’s speed is directly proportional to the grasping time until it reaches the threshold speed for the camera in identifying the Aruco markers. Future works are required to improve the dynamic visual servo algorithm with motion planning when obstacles are present in the path of robot grasping

Strengthening programming skills among engineering students through experiential learning based robotics project

This study examined the educational effects in strengthening programming skills among university’s undergraduate engineering students via integration of a robotics project and an experiential learning approach. In this study, a robotics project was conducted to close the gap of students’ difficulty in relating the theoretical concepts of programming and real-world problems. Hence, an experiential learning approach using the Kolb model was proposed to investigate the problem. In this project, students were split into groups whereby they were asked to develop codes for controlling the navigation of a wheeled mobile robot. They were responsible for managing their group’s activities, conducting laboratory tests, producing technical reports and preparing a video presentation. The statistical analysis performed on the students’ summative assessments of a programming course revealed a remarkable improvement in their problem-solving skills and ability to provide programming solutions to a real-world problem

A combinatorial RGB and depth images CNN-based model for oil palm fruit bunch detection and heatmap localisation for a visual SLAM system

The harvesting job of cutting and collecting fruit bunches in oil palm plantations remains the most labour-intensive job in the oil palm processing cycle. The introduction of an autonomous vehicle to assist workers in the harvesting job promises better productivity. Such a driverless vehicle requires a software module known as simultaneous localisation and mapping (SLAM) to guide the vehicle to navigate autonomously. This work proposes a visual SLAM system with a distinctive capability of detecting and localising oil palm loose fresh fruit bunches (FFB) on the ground using intelligent image processing. This vehicle is equipped with a depth camera capable of capturing RGB images and depth images concurrently. Two VGG16-based convolutional neural network (CNN) models are trained using the acquired RGB and depth images dataset of loose FFBs on the ground. The output from the combinatorial FFB detection model is then fed into a visual SLAM system called RTAB-Map. By combining the FFB detection model and the visual SLAM system, the vehicle can plan for autonomous navigation safely, perform bunch pick-up tasks, and avoid collision with fruit bunches on the ground. The experiment results show that the proposed CNN model can detect and localise loose FFBs with significant accuracy in various lighting conditions

Optimal Design and Tuning of Novel Proportional Integral Derivative with Filter Thyristor-Controlled Series Compensator Stabiliser Using a New Hybrid Technique

This paper proposes a novel robust thyristor-controlled series compensator (TCSC) controller based on proportional integral derivative with filter (PIDF) and uses a new hybrid optimisation evolutionary programming sine cosine algorithm (EPSCA) to improve the power system’s angle stability. The problem of the PIDF-TCSC design is transformed into an optimisation problem based on performance indices, such as damping factor, damping ratio, and eigenvalues, where the multi-objective function is employed to obtain the optimal stabiliser parameters. To examine the robustness of PIDF-TCSC, it was tested on a single-machine infinite-bus power system under different operating conditions. The performance of the system with the PIDF-TCSC controller was compared with the simulation results, and the results obtained with the proposed EPSCA were compared with those obtained with SCA, moth flame optimisation, and EP-based PIDF-TCSC methods. Simulation results showed the effectiveness of EPSCA for the PIDF-TCSC design and the superior robust performance for the enhancement of power system stability compared with other techniques in different cases

Optimal Design and Tuning of Novel Proportional Integral Derivative with Filter Thyristor-Controlled Series Compensator Stabiliser Using a New Hybrid Technique

This paper proposes a novel robust thyristor-controlled series compensator (TCSC) controller based on proportional integral derivative with filter (PIDF) and uses a new hybrid optimisation evolutionary programming sine cosine algorithm (EPSCA) to improve the power system’s angle stability. The problem of the PIDF-TCSC design is transformed into an optimisation problem based on performance indices, such as damping factor, damping ratio, and eigenvalues, where the multi-objective function is employed to obtain the optimal stabiliser parameters. To examine the robustness of PIDF-TCSC, it was tested on a single-machine infinite-bus power system under different operating conditions. The performance of the system with the PIDF-TCSC controller was compared with the simulation results, and the results obtained with the proposed EPSCA were compared with those obtained with SCA, moth flame optimisation, and EP-based PIDF-TCSC methods. Simulation results showed the effectiveness of EPSCA for the PIDF-TCSC design and the superior robust performance for the enhancement of power system stability compared with other techniques in different cases

Neural Network Based Prediction of Stable Equivalent Series Resistance in Voltage Regulator Characterization

High demand on voltage regulator (VR) currently requires VR manufacturers to improve their time-to-market, particularly for new product development. To fulfill the output stability requirement, VR manufacturers characterize the VR in terms of the equivalent series resistance (ESR) of the output capacitor because the ESR variation affects the VR output stability. The VR characterization outcome suggests a stable range of ESR, which is indicated in the ESR tunnel graph in the VR datasheet. However, current practice in industry manually characterizes VR, thereby increasing the manufacturing time and cost. Therefore, an efficient method based on multilayer neural network has been developed to obtain the ESR tunnel graph. The results show that this method able to reduce the VR characterization time by approximately 53% and achieved critical ESR prediction error less than 5%. This work demonstrated an efficient and effective approach for VR characterization in terms of ESR