2015 54th IEEE Conference on Decision and Control (CDC)最新文献_第8页

A distributed algorithm for solving general linear equations over networks 求解网络上一般线性方程的分布式算法

2015 54th IEEE Conference on Decision and Control (CDC) Pub Date : 2015-12-01 DOI: 10.1109/CDC.2015.7402773

Mu Yang, Choon Yik Tang

引用次数: 12

Output cluster synchronization of heterogeneous linear multi-agent systems 异构线性多智能体系统的输出集群同步

2015 54th IEEE Conference on Decision and Control (CDC) Pub Date : 2015-12-01 DOI: 10.1109/CDC.2015.7402649

Zhongchang Liu, W. Wong

引用次数: 7

Progressive time optimal control of reactive systems 无功系统的渐进时间最优控制

2015 54th IEEE Conference on Decision and Control (CDC) Pub Date : 2015-12-01 DOI: 10.1109/CDC.2015.7402767

S. Ware, R. Su

引用次数: 2

A Bayesian approach for model identification of LPV systems with uncertain scheduling variables 具有不确定调度变量的LPV系统模型辨识的贝叶斯方法

2015 54th IEEE Conference on Decision and Control (CDC) Pub Date : 2015-12-01 DOI: 10.1109/CDC.2015.7402326

F. Abbasi, J. Mohammadpour, R. Tóth, N. Meskin

{"title":"A Bayesian approach for model identification of LPV systems with uncertain scheduling variables","authors":"F. Abbasi, J. Mohammadpour, R. Tóth, N. Meskin","doi":"10.1109/CDC.2015.7402326","DOIUrl":"https://doi.org/10.1109/CDC.2015.7402326","url":null,"abstract":"This paper presents a Gaussian Process (GP) based Bayesian method that takes into account the effect of additive noise on the scheduling variables for identification of linear parameter-varying (LPV) models in input-output form. The proposed method approximates the noise-free coefficient functions by a local linear expansion on the observed scheduling variables. Therefore, additive noise on the scheduling variables is reconstructed as a corrective term added to the output noise that is proportional to the squared gradient obtained from the posterior of the Gaussian Process. An iterative procedure is given so that the obtained solution converges to the best estimation of the coefficient functions according to the given measure of fitness. Moreover, the expectation and covariance functions estimated by GP are modified for the noisy scheduling variable case to include the noise contribution on the estimated expectation and covariance functions. The model training procedure identifies noise level in the measurements including outputs and scheduling variables by estimating the noise variances, as well as other defined hyperparameters. Finally, the performance of the proposed method is compared to the standard GP approach through a numerical example.","PeriodicalId":308101,"journal":{"name":"2015 54th IEEE Conference on Decision and Control (CDC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125505969","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

Zero-delay rate-distortion optimization for partially observable Gauss-Markov processes 部分可观测高斯-马尔可夫过程的零延迟率失真优化

2015 54th IEEE Conference on Decision and Control (CDC) Pub Date : 2015-12-01 DOI: 10.1109/CDC.2015.7403118

Takashi Tanaka

引用次数: 14

Computation of the Lyapunov measure for almost everywhere stochastic stability 几乎处处随机稳定性的李雅普诺夫测度的计算

2015 54th IEEE Conference on Decision and Control (CDC) Pub Date : 2015-12-01 DOI: 10.1109/CDC.2015.7403329

U. Vaidya, V. Chinde

引用次数: 6

Dynamic state estimation in the presence of compromised sensory data 感知数据受损时的动态状态估计

2015 54th IEEE Conference on Decision and Control (CDC) Pub Date : 2015-12-01 DOI: 10.1109/CDC.2015.7403132

Yorie Nakahira, Yilin Mo

引用次数: 29

Representing externally positive systems through minimal eventually positive realizations 通过最小的最终积极的实现来表现外部积极的系统

2015 54th IEEE Conference on Decision and Control (CDC) Pub Date : 2015-12-01 DOI: 10.1109/CDC.2015.7403225

C. Altafini

引用次数: 6

Optimal sensor transmission energy allocation for linear control over a packet dropping link with energy harvesting 带能量收集的丢包链路线性控制中传感器传输能量的最优分配

2015 54th IEEE Conference on Decision and Control (CDC) Pub Date : 2015-12-01 DOI: 10.1109/CDC.2015.7402374

S. Knorn, S. Dey

{"title":"Optimal sensor transmission energy allocation for linear control over a packet dropping link with energy harvesting","authors":"S. Knorn, S. Dey","doi":"10.1109/CDC.2015.7402374","DOIUrl":"https://doi.org/10.1109/CDC.2015.7402374","url":null,"abstract":"This paper studies a closed loop linear control system. The sensor computes a state estimate and sends it to the controller/actuator in the receiver block over a randomly fading packet dropping link. The receiver sends an ACK/NACK packet to the transmitter over a link. It is assumed that the transmission energy per packet at the sensor depletes a battery of limited capacity, replenished by an energy harvester. The objective is to design an optimal energy allocation policy and an optimal control policy so that a finite horizon LQG control cost is minimized. It is shown that in case the receiver to sensor feedback channel is free of errors, a separation principle holds. Hence, the optimal LQG controller is linear, the Kalman filter is optimal and the optimal energy allocation policy is obtained via solving a backward dynamic programming equation. In case the feedback channel is erroneous, the separation principle does not hold. In this case, we propose a suboptimal policy where the controller still uses a linear control, and the transmitter minimizes an expected sum of the trace of an “estimated” receiver state estimation error covariance matrix. Simulations are used to illustrate the relative performance of the proposed algorithms and various heuristic algorithms for both the perfect and imperfect feedback cases. It is seen that the dynamic programming based policies outperform the simple heuristic policies by a margin.","PeriodicalId":308101,"journal":{"name":"2015 54th IEEE Conference on Decision and Control (CDC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116452248","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: 10

Robust taxi dispatch under model uncertainties 模型不确定性下的鲁棒出租车调度

2015 54th IEEE Conference on Decision and Control (CDC) Pub Date : 2015-12-01 DOI: 10.1109/CDC.2015.7402643

Fei Miao, Shuo Han, Shan Lin, George J. Pappas

{"title":"Robust taxi dispatch under model uncertainties","authors":"Fei Miao, Shuo Han, Shan Lin, George J. Pappas","doi":"10.1109/CDC.2015.7402643","DOIUrl":"https://doi.org/10.1109/CDC.2015.7402643","url":null,"abstract":"In modern taxi networks, large amount of real-time taxi occupancy and location data are collected from networked in-vehicle sensors. They provide knowledge of system models on passenger demand and taxi supply for efficient dispatch control and coordinating strategies. Such dispatch approaches face a new challenge: how to deal with future customer demand uncertainties while fulfilling system's performance requirements, such as balancing service across the whole city and minimizing taxis' total idle cruising distance. To address this problem, we present a novel robust optimization method for taxis dispatch problems to consider polytope model uncertainties of highly spatiotemporally correlated demand and supply models. An objective function concave over the uncertain demand parameters and convex over the variables is formulated according to the design requirements. We transform the robust optimization problem to an equivalent convex optimization form by strong duality and minimax theorem, and computational tractability is guaranteed. By Monte-Carlo simulations, we show that the robust taxi dispatch solutions in this work are less probable to get large costs compared with non-robust results.","PeriodicalId":308101,"journal":{"name":"2015 54th IEEE Conference on Decision and Control (CDC)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122349220","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: 16