2012 IEEE 51st IEEE Conference on Decision and Control (CDC)最新文献

A packetized direct load control mechanism for demand side management 用于需求侧管理的分组直接负荷控制机制

2012 IEEE 51st IEEE Conference on Decision and Control (CDC) Pub Date : 2013-01-23 DOI: 10.1109/cdc.2012.6427392

Bowen Zhang, J. Baillieul

{"title":"A packetized direct load control mechanism for demand side management","authors":"Bowen Zhang, J. Baillieul","doi":"10.1109/cdc.2012.6427392","DOIUrl":"https://doi.org/10.1109/cdc.2012.6427392","url":null,"abstract":"Electricity peaks can be harmful to grid stability and result in additional generation costs to balance supply with demand. By developing a network of smart appliances together with a quasi-decentralized control protocol, direct load control (DLC) provides an opportunity to reduce peak consumption by directly controlling the on/off switch of the networked appliances. This paper proposes a packetized DLC (PDLC) solution that is illustrated by an application to air conditioning temperature control. Here the term packetized refers to a fixed time energy usage authorization. The consumers in each room choose their preferred set point, and then an operator of the local appliance pool will determine the comfort band around the set point. We use a thermal dynamic model to investigate the duty cycle of thermostatic appliances. Three theorems are proposed in this paper. The first two theorems evaluate the performance of the PDLC in both transient and steady state operation. The first theorem proves that the average room temperature would converge to the average room set point with fixed number of packets applied in each discrete interval. The second theorem proves that the PDLC solution guarantees to control the temperature of all the rooms within their individual comfort bands. The third theorem proposes an allocation method to link the results in theorem 1 and assumptions in theorem 2 such that the overall PDLC solution works. The direct result of the theorems is that we can reduce the consumption oscillation that occurs when no control is applied. Simulation is provided to verify theoretical results.","PeriodicalId":312426,"journal":{"name":"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)","volume":"2464 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131037839","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: 33

Network-level dynamics of diffusively coupled cells 扩散耦合细胞的网络级动力学

2012 IEEE 51st IEEE Conference on Decision and Control (CDC) Pub Date : 2012-12-18 DOI: 10.1109/CDC.2012.6426705

S. Waldherr, F. Allgöwer

引用次数: 2

Sub-optimal switching with dwell time constraints for control of viral mutation 基于停留时间约束的次优切换控制病毒突变

2012 IEEE 51st IEEE Conference on Decision and Control (CDC) Pub Date : 2012-12-13 DOI: 10.1109/CDC.2012.6426559

E. Hernández-Vargas, P. Colaneri, R. Middleton

引用次数: 8

Stabilization of a linear hyperbolic system with one boundary controlled transport PDE coupled with n counterconvecting PDEs 一个边界控制输运偏微分方程与n个反对流偏微分方程耦合的线性双曲系统的稳定性

2012 IEEE 51st IEEE Conference on Decision and Control (CDC) Pub Date : 2012-12-13 DOI: 10.1109/CDC.2012.6426367

F. D. Meglio, R. Vázquez, M. Krstić

引用次数: 10

Reduced-order modeling of aggregated thermostatic loads with demand response 考虑需求响应的聚合恒温负荷的降阶建模

2012 IEEE 51st IEEE Conference on Decision and Control (CDC) Pub Date : 2012-12-12 DOI: 10.1109/CDC.2012.6426010

Wei Zhang, Jianming Lian, Chin-Yao Chang, K. Kalsi, Yannan Sun

{"title":"Reduced-order modeling of aggregated thermostatic loads with demand response","authors":"Wei Zhang, Jianming Lian, Chin-Yao Chang, K. Kalsi, Yannan Sun","doi":"10.1109/CDC.2012.6426010","DOIUrl":"https://doi.org/10.1109/CDC.2012.6426010","url":null,"abstract":"Demand Response is playing an increasingly important role in smart grid control strategies. Modeling the dynamical behavior of a large population of appliances under demand response is especially important to evaluate the effectiveness of various demand response programs. In this paper an aggregate model is proposed for a class of second-order Thermostatically Controlled Loads (TCLs). The model efficiently includes statistical information of the population, systematically deals with heterogeneity, and accounts for a second-order effect necessary to accurately capture the transient dynamics in the collective response. A good performance of the model however requires a high state dimension which dramatically complicates its formal analysis and controller design. To address this issue, a model reduction approach is developed for the proposed aggre-gate model, which can significantly reduce its complexity with small performance loss. The original and the reduced-order aggregate models are validated against simulations of thousands of detailed building models using GridLAB-D (an open-source distribution simulation software). The results indicate that the reduced-order model can accurately reproduce the steady-state and transient dynamics generated by GridLAB-D simulations with a much reduced complexity.","PeriodicalId":312426,"journal":{"name":"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128483085","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: 25

Finite-time output stabilization of the double integrator 双积分器的有限时间输出镇定

2012 IEEE 51st IEEE Conference on Decision and Control (CDC) Pub Date : 2012-12-11 DOI: 10.1109/CDC.2012.6426565

E. Bernuau, W. Perruquetti, D. Efimov, Emmanuel Moulay

引用次数: 35

Model learning actor-critic algorithms: Performance evaluation in a motion control task 模型学习演员评价算法:运动控制任务中的性能评估

2012 IEEE 51st IEEE Conference on Decision and Control (CDC) Pub Date : 2012-12-10 DOI: 10.1109/CDC.2012.6426427

I. Grondman, L. Buşoniu, Robert Babuška

{"title":"Model learning actor-critic algorithms: Performance evaluation in a motion control task","authors":"I. Grondman, L. Buşoniu, Robert Babuška","doi":"10.1109/CDC.2012.6426427","DOIUrl":"https://doi.org/10.1109/CDC.2012.6426427","url":null,"abstract":"Reinforcement learning (RL) control provides a means to deal with uncertainty and nonlinearity associated with control tasks in an optimal way. The class of actor-critic RL algorithms proved useful for control systems with continuous state and input variables. In the literature, model-based actor-critic algorithms have recently been introduced to considerably speed up the the learning by constructing online a model through local linear regression (LLR). It has not been analyzed yet whether the speed-up is due to the model learning structure or the LLR approximator. Therefore, in this paper we generalize the model learning actor-critic algorithms to make them suitable for use with an arbitrary function approximator. Furthermore, we present the results of an extensive analysis through numerical simulations of a typical nonlinear motion control problem. The LLR approximator is compared with radial basis functions (RBFs) in terms of the initial convergence rate and in terms of the final performance obtained. The results show that LLR-based actor-critic RL outperforms the RBF counterpart: it gives quick initial learning and comparable or even superior final control performance.","PeriodicalId":312426,"journal":{"name":"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125829541","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: 26

A#: A distributed version of A* for factored planning a#:用于因子规划的A*的分布式版本

2012 IEEE 51st IEEE Conference on Decision and Control (CDC) Pub Date : 2012-12-10 DOI: 10.1109/CDC.2012.6426187

L. Jezequel, É. Fabre

引用次数: 12

Stability of bilinear sampled-data systems with an emulation of static state feedback 具有静态反馈仿真的双线性采样数据系统的稳定性

2012 IEEE 51st IEEE Conference on Decision and Control (CDC) Pub Date : 2012-12-10 DOI: 10.1109/CDC.2012.6426206

Hassan Omran, L. Hetel, J. Richard, F. Lamnabhi-Lagarrigue

引用次数: 9

Convergence and robustness of a point-to-point iterative learning control algorithm 点对点迭代学习控制算法的收敛性和鲁棒性

2012 IEEE 51st IEEE Conference on Decision and Control (CDC) Pub Date : 2012-12-10 DOI: 10.1109/CDC.2012.6426908

T. V. Dinh, C. Freeman, P. Lewin, Y. Tan

引用次数: 4