2014 American Control Conference最新文献

A numerical investigation of phase and magnitude compensation in adaptive control of uncertain hammerstein systems with hysteretic nonlinearities 迟滞非线性不确定hammerstein系统自适应控制中相位和幅度补偿的数值研究

2014 American Control Conference Pub Date : 2014-12-01 DOI: 10.1109/CDC.2014.7039438

M. A. Janaideh, D. Bernstein

引用次数: 2

BRCM Matlab Toolbox: Model generation for model predictive building control BRCM Matlab工具箱:模型预测建筑控制的模型生成

2014 American Control Conference Pub Date : 2014-07-21 DOI: 10.1109/ACC.2014.6858967

David Sturzenegger, D. Gyalistras, Vito Semeraro, M. Morari, Roy S. Smith

引用次数: 110

Sparse identification in chemical master equations for monomolecular reaction networks 单分子反应网络化学主方程的稀疏辨识

2014 American Control Conference Pub Date : 2014-07-21 DOI: 10.1109/ACC.2014.6859312

K. K. Kim, Hong Jang, R. B. Gopaluni, Jay H. Lee, R. Braatz

引用次数: 1

Tailsitter heading estimation using a magnetometer 用磁力计估计后座航向

2014 American Control Conference Pub Date : 2014-07-21 DOI: 10.1109/ACC.2014.6859443

J. Beach, Matthew E. Argyle, T. McLain, R. Beard, S. Morris

引用次数: 1

Compositional finite-time stability analysis of nonlinear systems 非线性系统的组合有限时稳定性分析

2014 American Control Conference Pub Date : 2014-07-21 DOI: 10.1109/ACC.2014.6859034

S. Tabatabaeipour, M. Blanke

引用次数: 5

Occupancy estimation for smart buildings by an auto-regressive hidden Markov model 基于自回归隐马尔可夫模型的智能建筑占用估计

2014 American Control Conference Pub Date : 2014-07-21 DOI: 10.1109/ACC.2014.6859372

Bing Ai, Zhaoyan Fan, R. Gao

{"title":"Occupancy estimation for smart buildings by an auto-regressive hidden Markov model","authors":"Bing Ai, Zhaoyan Fan, R. Gao","doi":"10.1109/ACC.2014.6859372","DOIUrl":"https://doi.org/10.1109/ACC.2014.6859372","url":null,"abstract":"One of the primary energy consumers in buildings are the Heating, Ventilation, and Air-Conditioning (HVAC) systems, which usually operate on a fixed schedule, i.e., running from early morning until late evening during the weekdays. This fixed operation schedule does not take the dynamics of occupancy level in the building into consideration, therefore may lead to waste of energy. An estimate of the number of occupants in the building with time can contribute to improving the control policy of the building's HVAC system by reducing energy consumption. In this paper, the auto-regressive hidden Markov model (ARHMM), is investigated to estimate the number of occupants in a research laboratory in a building using a wireless sensor network deployed. The network is composed of stand-alone sensing nodes with wireless data transmission capability, a base station that collects data from the sensing nodes, and a server to analyze the data from the base station. Experimental results and numerical simulation demonstrate that the ARHMM is more effective in estimating the number of occupants in the laboratory than the HMM algorithm, especially when the occupancy level fluctuates frequently.","PeriodicalId":369729,"journal":{"name":"2014 American Control Conference","volume":"332 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115974536","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: 64

Computational issues on observability and optimal sensor locations 可观测性和最优传感器位置的计算问题

2014 American Control Conference Pub Date : 2014-07-21 DOI: 10.1109/ACC.2014.6858768

Sarah King, W. Kang, Liang Xu

{"title":"Computational issues on observability and optimal sensor locations","authors":"Sarah King, W. Kang, Liang Xu","doi":"10.1109/ACC.2014.6858768","DOIUrl":"https://doi.org/10.1109/ACC.2014.6858768","url":null,"abstract":"In this paper we discuss computational issues related to optimal sensor placement in numerical weather prediction (NWP). Specifically we will discuss the application of observability as a metric for sensor placement to an atmospheric flow model and the arising optimization problem. Atmospheric data assimilation is the process of estimating the initial system state based on observations needed in NWP to produce a forecast of future weather conditions. Optimal placement of sensors for data assimilation leading to an improvement in the analysis of the data assimilation and improved forecast quality is of great interest. The traditional definition of observability is not necessarily suitable for NWP applications because of the high dimensions used in NWP. We use the concept of partial observability where the observability of a system is computed on a reduced subspace and is obtained using dynamic optimization. This definition allows for a characterization of the observability of complicated systems. Using partial observability for optimal sensor placement leads to a max-min problem. We use an empirical gramian to reduce this problem into one of eigenvalue optimization. Our focus will be to develop computational methods that are both efficient and scalable. We will leverage tools typically available in data assimilation and introduce tools used in nonsmooth optimization. We will use the shallow water equations as a testbed for our method of optimal sensor placement in four dimensional variational data assimilation.","PeriodicalId":369729,"journal":{"name":"2014 American Control Conference","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131352598","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: 6

Risk adjusted forecasting of electric power load 电力负荷风险调整预测

2014 American Control Conference Pub Date : 2014-07-21 DOI: 10.1109/ACC.2014.6859465

Saahil Shenoy, D. Gorinevsky

引用次数: 12

NonLinear Fault Tolerant Flight Control for generic actuators fault models 通用作动器故障模型的非线性容错飞行控制

2014 American Control Conference Pub Date : 2014-07-21 DOI: 10.1109/ACC.2014.6858813

P. Castaldi, N. Mimmo, S. Simani

引用次数: 2

On-line impulse response extraction of a power transfer path using pulse compression probing 基于脉冲压缩探测的电力传输路径在线脉冲响应提取

2014 American Control Conference Pub Date : 2014-07-21 DOI: 10.1109/ACC.2014.6859305

N. Wu, Jianzhuang Huang

引用次数: 1