Systems Science & Control Engineering: An Open Access Journal最新文献_第2页

Nonlinear control and estimation in induction machine using state estimation techniques 基于状态估计技术的异步电机非线性控制与估计

Systems Science & Control Engineering: An Open Access Journal Pub Date : 2014-11-03 DOI: 10.1080/21642583.2014.956842

M. Mansouri, H. Nounou, M. Nounou

{"title":"Nonlinear control and estimation in induction machine using state estimation techniques","authors":"M. Mansouri, H. Nounou, M. Nounou","doi":"10.1080/21642583.2014.956842","DOIUrl":"https://doi.org/10.1080/21642583.2014.956842","url":null,"abstract":"In this paper, several techniques are addressed for both estimation and control to be integrated into a unified closed-loop or feedback control system that is applicable for a general family of nonlinear control structures. The estimation techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). Specifically, two comparative studies are performed. In the first comparative study, the state variables are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square errors with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of the number of estimated model parameters on the accuracy and convergence of these techniques is also assessed. The results of both comparative studies show that the UKF provides a higher accuracy than the EKF, due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF and EKF and can still provide both convergence as well as accuracy-related advantages over other estimation methods. This is because the covariance is propagated through linearization of the underlying nonlinear model, when the state transition and observation models are highly nonlinear.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74269751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Estimation of Gaussian process regression model using probability distance measures 利用概率距离测度估计高斯过程回归模型

Systems Science & Control Engineering: An Open Access Journal Pub Date : 2014-10-31 DOI: 10.1080/21642583.2014.970731

X. Hong, Junbin Gao, Xinwei Jiang, C. Harris

引用次数: 6

Experimental analysis of 2 DOF quarter-car passive and hydraulic active suspension systems for ride comfort 二自由度四分之一车被动悬架与液压主动悬架平顺性的实验分析

Systems Science & Control Engineering: An Open Access Journal Pub Date : 2014-10-31 DOI: 10.1080/21642583.2014.913212

S. Patil, S. G. Joshi

引用次数: 28

An optimized model of electricity price forecasting in the electricity market based on fuzzy timeseries 基于模糊时间序列的电力市场电价预测优化模型

Systems Science & Control Engineering: An Open Access Journal Pub Date : 2014-10-31 DOI: 10.1080/21642583.2014.970733

B. Safarinejadian, Masihollah Gharibzadeh, M. Rakhshan

引用次数: 10

Text stream mining for Massive Open Online Courses: review and perspectives 大规模在线开放课程的文本流挖掘:综述和观点

Systems Science & Control Engineering: An Open Access Journal Pub Date : 2014-10-31 DOI: 10.1080/21642583.2014.970732

S. Shatnawi, M. Gaber, Ella Haig

引用次数: 16

Artificial neural network-based induction motor fault classifier using continuous wavelet transform 基于连续小波变换的人工神经网络感应电机故障分类器

Systems Science & Control Engineering: An Open Access Journal Pub Date : 2014-10-09 DOI: 10.1080/21642583.2014.956266

A. U. Jawadekar, Sudhir Paraskar, S. Jadhav, G. Dhole

{"title":"Artificial neural network-based induction motor fault classifier using continuous wavelet transform","authors":"A. U. Jawadekar, Sudhir Paraskar, S. Jadhav, G. Dhole","doi":"10.1080/21642583.2014.956266","DOIUrl":"https://doi.org/10.1080/21642583.2014.956266","url":null,"abstract":"Induction motors are used in industrial, commercial and residential applications because they have considerable merits over other types of electric motors. These motors are used in various operating stresses that give rise to faults. Most recurrent faults in induction motors are bearing faults, stator interturn faults and cracked rotor bars. Early detection of induction motor faults is crucial for their reliable and economical operation. This could be done by motor monitoring, incipient fault detection and diagnosis. In many situations, failure of critically loaded machine can shut down an entire industry process. This growing demand for high-quality and low-cost production has increased the need for automated manufacturing systems with effective monitoring and control capabilities. Condition monitoring and fault diagnosis of an induction motor are of great importance in the production line. It can reduce the cost of maintenance and risk of unexpected failures by allowing the early detection of catastrophic failures. This work documents experimental results for multiple fault detection in induction motors using signal-processing and artificial neural network-based approaches. Motor line currents recorded under various fault conditions were analyzed using continuous wavelet transform. A feedforward neural network was used for fault characterization based on fault features extracted using continuous wavelet transform.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84117552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 43

Random forests: from early developments to recent advancements 随机森林:从早期发展到最近的进展

Systems Science & Control Engineering: An Open Access Journal Pub Date : 2014-09-30 DOI: 10.1080/21642583.2014.956265

Khaled Fawagreh, M. Gaber, Eyad Elyan

{"title":"Random forests: from early developments to recent advancements","authors":"Khaled Fawagreh, M. Gaber, Eyad Elyan","doi":"10.1080/21642583.2014.956265","DOIUrl":"https://doi.org/10.1080/21642583.2014.956265","url":null,"abstract":"Ensemble classification is a data mining approach that utilizes a number of classifiers that work together in order to identify the class label for unlabeled instances. Random forest (RF) is an ensemble classification approach that has proved its high accuracy and superiority. With one common goal in mind, RF has recently received considerable attention from the research community to further boost its performance. In this paper, we look at developments of RF from birth to present. The main aim is to describe the research done to date and also identify potential and future developments to RF. Our approach in this review paper is to take a historical view on the development of this notably successful classification technique. We start with developments that were found before Breiman's introduction of the technique in 2001, by which RF has borrowed some of its components. We then delve into dealing with the main technique proposed by Breiman. A number of developments to enhance the original technique are then presented and summarized. Successful applications that utilized RF are discussed, before a discussion of possible directions of research is finally given.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74788861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 356

Fuzzy adaptive proportional-integral-derivative controller with dynamic set-point adjustment for maximum power point tracking in solar photovoltaic system 太阳能光伏系统最大功率点跟踪模糊自适应动态设定点调整比例-积分-导数控制器

Systems Science & Control Engineering: An Open Access Journal Pub Date : 2014-09-18 DOI: 10.1080/21642583.2014.956267

D. Karanjkar, S. Chatterji, Amod Kumar, S. Shimi

{"title":"Fuzzy adaptive proportional-integral-derivative controller with dynamic set-point adjustment for maximum power point tracking in solar photovoltaic system","authors":"D. Karanjkar, S. Chatterji, Amod Kumar, S. Shimi","doi":"10.1080/21642583.2014.956267","DOIUrl":"https://doi.org/10.1080/21642583.2014.956267","url":null,"abstract":"A novel fuzzy adaptive proportional-integral-derivative (PID) control strategy with online set-point tracking is presented for maximum power point tracking (MPPT) in solar photovoltaic (PV) system in this paper. The range of the membership functions of the fuzzy logic for online PID parameter tuner has been optimized using the relay feedback tuning method. The proposed MPPT controller has been designed with online set-point adjustment approach using current, radiation and temperature sensors. Real-time simulations have been carried out on MATLAB™/dSPACE™ ds1104 Research and Development control board platform for solar PV system with synchronous buck converter and resistive load. Performance of the proposed techniques has been compared with main existing MPPT techniques viz. perturb and observe, incremental conductance, fuzzy logic, neural network and adaptive neuro-fuzzy inference system-based methods of MPPT. Performance of various techniques has been compared based on tracking efficiency, steady state and dynamic behaviour. Experimental results showed the superiority of the proposed method for tracking maximum power point under rapidly varying solar radiations.","PeriodicalId":22127,"journal":{"name":"Systems Science & Control Engineering: An Open Access Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74438299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Design and implementation of an open circuit voltage prediction mechanism for lithium-ion battery systems 锂离子电池系统开路电压预测机制的设计与实现

Systems Science & Control Engineering: An Open Access Journal Pub Date : 2014-09-15 DOI: 10.1080/21642583.2014.956268

T. Stockley, K. Thanapalan, M. Bowkett, J. Williams

引用次数: 3

Variable sampling-time nonlinear model predictive control of satellites using magneto-torquers 基于磁力矩器的卫星变采样时间非线性模型预测控制

Systems Science & Control Engineering: An Open Access Journal Pub Date : 2014-08-26 DOI: 10.1080/21642583.2014.956841

Yi Cao, Wen‐Hua Chen

引用次数: 9