2017 13th IEEE International Conference on Control & Automation (ICCA)最新文献_第4页

Aircraft trajectory planning for improving vision-based target geolocation performance 提高基于视觉的目标定位性能的飞机轨迹规划

2017 13th IEEE International Conference on Control & Automation (ICCA) Pub Date : 2017-07-01 DOI: 10.1109/ICCA.2017.8003090

Lele Zhang, Jie Chen, Fang Deng

引用次数: 5

Mobile localization via unscented Kalman filter with sensor position uncertainties 基于传感器位置不确定性的无气味卡尔曼滤波移动定位

2017 13th IEEE International Conference on Control & Automation (ICCA) Pub Date : 2017-07-01 DOI: 10.1109/ICCA.2017.8003196

Xiaomei Qu, Lei Mu

引用次数: 0

Modeling and control analysis of a flapping-wing micro aerial vehicle 扑翼微型飞行器建模与控制分析

2017 13th IEEE International Conference on Control & Automation (ICCA) Pub Date : 2017-07-01 DOI: 10.1109/ICCA.2017.8003076

K. Peng, F. Lin, Ben M. Chen

引用次数: 3

Development of correlation-based process characteristics visualization method and its application to fault detection 基于关联的过程特征可视化方法的发展及其在故障检测中的应用

2017 13th IEEE International Conference on Control & Automation (ICCA) Pub Date : 2017-07-01 DOI: 10.1109/ICCA.2017.8003187

K. Fujiwara, M. Kano

{"title":"Development of correlation-based process characteristics visualization method and its application to fault detection","authors":"K. Fujiwara, M. Kano","doi":"10.1109/ICCA.2017.8003187","DOIUrl":"https://doi.org/10.1109/ICCA.2017.8003187","url":null,"abstract":"Although process monitoring is important for maintaining safety and product quality, it is difficult to understand process characteristics particularly when they are changing. Since the correlation among variables changes due to changes in process characteristics, process data visualization based on the correlation among variables helps process characteristic understanding. In the present work, a new correlation-based data visualization method is proposed by integrating joint decorrelation (JD) and stochastic proximity embedding (SPE). JD is a blind source separation (BSS) method that can separates sample based on the correlation, and SPE is a self-organizing algorithm that can map high-dimensional data to a two-dimensional plane. The proposed method, referred to as JD-SPE, separates samples based on the correlation using JD and the separated samples are visualized in the two-dimensional plane by SPE. Correlation matrices have to be constructed before sample separation for JD; however how to construct them is not clear. The present work also proposes a correlation matrix construction method for JD by using nearest correlation spectral clustering (NCSC), which is a correlation-based clustering method. In addition, a new process monitoring method based on multivariate statistical process control (MSPC) which is a well-known process monitoring algorithm and JD-SPE. This monitoring method is referred to as JD-SPE-r2. The proposed JD-SPE-Γ2 can detect a fault that can not detected by the conventional MSPC. The usefulness of the proposed methods is demonstrated through numerical examples.","PeriodicalId":379025,"journal":{"name":"2017 13th IEEE International Conference on Control & Automation (ICCA)","volume":"94 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134128194","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: 0

Incipient failure detection: A particle filtering approach with application to actuator systems 早期故障检测:粒子滤波方法在致动器系统中的应用

2017 13th IEEE International Conference on Control & Automation (ICCA) Pub Date : 2017-07-01 DOI: 10.1109/ICCA.2017.8003036

M. Balchanos, D. Mavris, Douglas W. Brown, G. Georgoulas, G. Vachtsevanos

{"title":"Incipient failure detection: A particle filtering approach with application to actuator systems","authors":"M. Balchanos, D. Mavris, Douglas W. Brown, G. Georgoulas, G. Vachtsevanos","doi":"10.1109/ICCA.2017.8003036","DOIUrl":"https://doi.org/10.1109/ICCA.2017.8003036","url":null,"abstract":"The background, simulation and experimental evaluation of an anomaly detector for Brushless DC motor winding faults is described in this paper in the context of an aircraft Electro-Mechanical Actuator (EMA) application. Results acquired from an internal Failure Modes and Effects Analysis (FMEA) study identified turn-to-turn winding faults as the primary mechanism, or mode, of failure. Physics-of-failure mechanisms used to develop a model for the identified fault are provided. Then, an experimental test procedure was devised and executed to validate the model. Additionally, a diagnostic feature, identified by the fault model and derived using Hilbert transform theory, was validated using the system model and experimental data for several fault dimensions. Next, a feature extraction routine preprocesses monitoring parameters and passes the resulting features to a particle filter. The particle filter, based on Bayesian estimation theory, allows for representation and management of uncertainty in a computationally efficient manner. The resulting anomaly detection routine declares a fault only when a specified confidence level is reached at a given false alarm rate. Finally, the real-time performance of the anomaly detector is evaluated using LabVIEW.","PeriodicalId":379025,"journal":{"name":"2017 13th IEEE International Conference on Control & Automation (ICCA)","volume":"498 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134217309","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: 3

Numerical algorithms for solving optimal control problems with integro-differential equations of the second kind as constraints 求解以第二类积分-微分方程为约束的最优控制问题的数值算法

2017 13th IEEE International Conference on Control & Automation (ICCA) Pub Date : 2017-07-01 DOI: 10.1109/ICCA.2017.8003059

Shihchung Chiang, T. Herdman

引用次数: 0

Asynchronous mix-triggered control 异步混合触发控制

2017 13th IEEE International Conference on Control & Automation (ICCA) Pub Date : 2017-07-01 DOI: 10.1109/ICCA.2017.8003065

A. Fu, M. Mazo

引用次数: 1

Hydrodynamic modelling for a small-scale underwater vehicle using computational fluid dynamics 基于计算流体动力学的小型水下航行器水动力建模

2017 13th IEEE International Conference on Control & Automation (ICCA) Pub Date : 2017-07-01 DOI: 10.1109/ICCA.2017.8003089

Xiaodong Liu, Yuhang Tan, Bo Di, Ben M. Chen

引用次数: 4

Parameters identification of robot manipulator based on particle swarm optimization 基于粒子群优化的机械臂参数辨识

2017 13th IEEE International Conference on Control & Automation (ICCA) Pub Date : 2017-07-01 DOI: 10.1109/ICCA.2017.8003078

N. Mizuno, Canh Son Nguyen

引用次数: 10

Distributed adaptive practical time-varying tracking control for second-order nonlinear multi-agent system using neural networks 二阶非线性多智能体系统的分布式自适应实用化时变跟踪控制

2017 13th IEEE International Conference on Control & Automation (ICCA) Pub Date : 2017-07-01 DOI: 10.1109/ICCA.2017.8003193

Jianglong Yu, Xiwang Dong, Qingdong Li, Fei Liu, Z. Ren, H. Ni

{"title":"Distributed adaptive practical time-varying tracking control for second-order nonlinear multi-agent system using neural networks","authors":"Jianglong Yu, Xiwang Dong, Qingdong Li, Fei Liu, Z. Ren, H. Ni","doi":"10.1109/ICCA.2017.8003193","DOIUrl":"https://doi.org/10.1109/ICCA.2017.8003193","url":null,"abstract":"Practical adaptive time-varying formation tracking problems for second-order nonlinear multi-agent systems are investigated using neural networks, where the time-varying formation tracking error can be arbitrarily small. Different from the previous work, the states of followers form a predefined time-varying formation while tracking the states of the leader with unknown control input. Besides, the dynamics of each agent has heterogeneous nonlinearity. Firstly, for the case where the control input of the leader is unknown, a nonlinear practical time-varying formation tracking protocol using adaptive neural networks is proposed which is constructed using only local neighboring information. Secondly, sufficient conditions for the second-order nonlinear multi-agent systems to achieve practical time-varying formation are presented, where a novel practical time-varying formation tracking feasibility condition is given. Thirdly, an approach is presented to design the control parameters for distributed practical formation tracking control protocol. The stability of the closed-loop system is proven by using the Lyapunov stability theory. Finally, simulation results are given to illustrate the effectiveness of the obtained results.","PeriodicalId":379025,"journal":{"name":"2017 13th IEEE International Conference on Control & Automation (ICCA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133136915","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: 5