Measurement and Control

Tran, Huu Khoa; Nguyen, Thanh Nam

doi: 10.1177/0020294018768744pmid: N/A

In this study, the Genetic Algorithm operability is assigned to optimize the proportional–integral–derivative controller parameters for both simulation and real-time operation of quadcopter flight motion. The optimized proportional–integral–derivative gains, using Genetic Algorithm to minimum the fitness function via the integral of time multiplied by absolute error criterion, are then integrated to control the quadcopter flight motion. In addition, the proposed controller design is successfully implemented to the experimental real-time flight motion. The performance results are proven that the highly effective stability operation and the reliable of waypoint tracking.

Wolak, Artur

doi: 10.1177/0020294018770916pmid: N/A

This article describes the processes of degradation of five engine oils belonging to the same SAE (Society of Automotive Engineers) viscosity class but launched by different manufacturers. The direction and intensity of changes in the total acid number have been analyzed. As part of the experiment, the changes in engine oils occurring during operation have been examined. The operating conditions throughout the test can be described as “severe,” that is, frequent starting of the engine, short distance driving, and extended engine idling. All engine oils were operated in passenger cars of a uniform fleet of 25 vehicles. The total acid number was determined in accordance with the ASTM D664. The obtained results have led to the development of a statistical model enabling to calculate average predictive values of the total acid number for a given mileage. The results may facilitate decision-making regarding the service life of engine oils.

Yan, Xiaolong; Chen, Guoguang; Tian, Xiaoli

doi: 10.1177/0020294018769828pmid: N/A

It is critical to measure the roll angle of a spinning missile quickly and accurately. Magnetometers are commonly used to implement these measurements. At present, the estimation of roll angle parameters is usually performed with the unscented Kalman filter algorithm. In this paper, the two-step adaptive augmented unscented Kalman filter algorithm is proposed to calibrate the biaxial magnetometer and circuit measurements quickly, which allows accurate estimates of the missile roll angle. Unlike the existing algorithms, the state vector of the algorithm is based on the missile roll angle parameters and the error factors caused by the magnetometer and the measurement circuit errors. Next, a two-step fast fitting algorithm is used to fit the initial value. After satisfying the stop rule, the state vector of the filter is configured to estimate the roll angle parameters and the calibration parameters. This method is evaluated by running numerous simulations. In the experiment, the algorithm completes the calibration of the magnetometer and the measurement circuit 1 s after the missile launches, with a sampling rate of 1 ms and an output roll attitude angle with a 0.0015 rad precision. The conventional unscented Kalman filter algorithm requires more time to achieve such a high accuracy. The simulation results demonstrate that the proposed two-step augmented unscented Kalman filter outperforms the conventional unscented Kalman filter in its estimation accuracy and convergence characteristics.

Wang, Biao; Mao, Zhizhong

doi: 10.1177/0020294018771097pmid: N/A

The presence of outliers is the main reason leading to ineffectiveness of advanced data-driven control methods in electric arc furnace systems. This paper proposes a hybrid method dedicated to detecting outliers in electric arc furnace systems, where process data are characterized as unlabeled, imbalanced, non-stationary and noisy. First, the raw data are divided into certain number of clusters. Then, with each cluster, a one-class classifier can be trained. So with these well-trained sub-models, new test points can be investigated. Those points that are rejected by all sub-models will be labeled as outliers. With the combination of one-class classification and clustering technique, the intricate data in electric arc furnace can be processed effectively. In addition, the detector will be updated with a specific strategy to enhance its adaptiveness. A series of experiments are carried out, and comparative results have shown the effectiveness of our method.

Göksu, Hüseyin

doi: 10.1177/0020294018769080pmid: N/A

A vehicle, when running, makes a complex sound emission from the engine, the exhaust, the air conditioner, and other mechanical parts. Analysis of this sound for the purpose of vehicle identification is an interesting practice which has security- and transportation-related applications. Engine speed variation, which causes shifts in the frequency content of the emissions, makes Fourier-based methods ineffective in terms of providing a stable signature for the vehicle. We search for an engine speed–independent acoustic signature for the vehicle, and for this purpose, we propose wavelet packet analysis rather than traditional time- or frequency-domain methods. Wavelet packet analysis, by providing arbitrary time–frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. Under varying engine speed, sound emissions are recorded from four cars and analyzed by wavelet packet analysis. Wavelet packet analysis subimages are further analyzed to obtain feature vectors in the form of log energy entropy, norm entropy, and energy. These feature vectors are fed into a classifier, multilayer perceptron, for evaluation. While norm entropy achieves a classification rate of 100%, log energy entropy and energy achieves classification rates of 99.26% and 97.79%, respectively. These results indicate that, wavelet packet analysis along with norm entropy and multilayer perceptron provides an accurate vehicle-specific acoustic signature independent of the engine speed.

Göksu, Hüseyin

doi: 10.1177/0020294018768340pmid: N/A

Fluid, when running through pipes, makes a complex sound emission whose parameters change nonlinearly with respect to flow speed. Especially, in household pipe systems, there may be spraying effects and resonance effects which make the emission more complex. We present a novel approach for predicting flow speed based on wavelet packet analysis of sound emissions rather than traditional time and frequency domain methods. Wavelet packet analysis, by providing arbitrary time–frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. Wavelet packet analysis subimages are further analyzed to obtain feature vectors of norm entropy. These feature vectors are fed into a multilayer perceptron for prediction. Prediction accuracy of 98.62%, with 3.99E−04 L/s mean absolute error and its corresponding 1.85% relative error is achieved. Time sensitivity is ±0.453 s and is open to improvement by varying window width. The result indicates that the proposed method is a good candidate for flow measurement by acoustic analysis.

Zhai, Chao; Ho, Yuh-Shan

doi: 10.1177/0020294018768352pmid: N/A

As an emerging research direction in the field of systems and control, distributed control or decentralized control has attracted great interests of researchers in the past decade. In this paper, a bibliometric analysis of the relevant publications is presented based on the data collected from the Science Citation Index Expanded Web of Science. In particular, we make a discussion on the trend of total publications, journal distribution, top research organizations (i.e. universities and institutes), and publication performance of nations, and the focus is on highly cited articles and authors, subject categories, and the future trend of hot topics. Some key bibliometric indexes such as single country articles, first author articles, and internationally collaborative articles are employed to give us a detailed picture about the intrinsic relationship and the state of the art of distributed control publications. Finally, the statistical analysis indicates that multi-agent systems are extremely popular in recent years and will dominate the future research on distributed control.