2022 American Control Conference (ACC)最新文献_第5页

A distributed control invariant set computing algorithm for nonlinear cascade systems 非线性串级系统的分布式控制不变量集计算算法

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867576

Benjamin Decardi-Nelson, Jinfeng Liu

引用次数: 2

On Distributed Sampling for Mismatched Estimation of Remote Sources 远程源不匹配估计的分布式抽样研究

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867617

Yashodhara Pandit, Amitalok J. Budkuley

{"title":"On Distributed Sampling for Mismatched Estimation of Remote Sources","authors":"Yashodhara Pandit, Amitalok J. Budkuley","doi":"10.23919/ACC53348.2022.9867617","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867617","url":null,"abstract":"In this work, we study the problem of distributed sampling for the recovery of a remote source under information mismatch at the estimator. In particular, a centralized estimator seeks to estimate a remote Gaussian random signal, where unlike in the ‘classical’ estimation setup, we assume that the estimator has a fixed, unknown mismatch vis-à-vis source statistics, in particular, the source covariance matrix. Such a mismatched estimator deploys multiple samplers in the field, where each sampler observes an independently noise corrupted version of the remote source and then forwards its sampled version to the estimator. The estimator has a fixed limit on the number of samples it can concurrently process; given such a total sampling budget, it seeks to distribute these samples optimally among samplers so as to obtain a reasonably high fidelity sampled noisy observation of the remote source via the samplers. Using this sampled data, the mismatched estimator then outputs a source estimate which minimizes distortion (i.e., the overall mean squared error).Our principal goal in this work is to understand the distortion-versus-sampling rate trade-off for the mismatched Gaussian source estimation problem under general distributed configurations. In the high-rate sampling regime, where the estimator has a ‘large’ sampling budget and essentially every sampler can operate at ‘high’ sampling rate, we show the interesting result that for a wide range of parameters, the optimal distributed sampling strategy is a uniform sampling strategy but one which, interestingly, does not depend on the mismatch at the estimator. We also characterize the optimal distortion, which we show does indeed depend on the degree of mismatch. Our results also bring to the fore an interesting phenomenon where the optimal distortion behaves asymmetrically w.r.t. the nature of mismatch, i.e., even for identical mismatch magnitude, the distortion is significantly different depending on the sign of the mismatch.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115801350","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: 1

Kalman Estimation Based One-Step Look Ahead Control of Data-driven Model with Random Parameters 基于卡尔曼估计的随机参数数据驱动模型一步前视控制

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867874

Jie Wang, G. Chiu

引用次数: 0

Energy Efficient and Battery SOC-aware Coordinated Control of Connected and Autonomous Electric Vehicles 互联和自动驾驶电动汽车的节能和电池soc感知协调控制

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867292

Shaopan Guo, Xiangyu Meng, M. Farasat

引用次数: 0

Set-Based Reachability and the Explicit Solution of Linear MPC using Hybrid Zonotopes * 基于集的可达性和混合带拓扑线性MPC的显式解

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867853

Trevor J. Bird, Neera Jain, H. Pangborn, Justin P. Koeln

{"title":"Set-Based Reachability and the Explicit Solution of Linear MPC using Hybrid Zonotopes *","authors":"Trevor J. Bird, Neera Jain, H. Pangborn, Justin P. Koeln","doi":"10.23919/ACC53348.2022.9867853","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867853","url":null,"abstract":"This paper presents a closed-form solution to the exact reachable sets of closed-loop systems under linear model predictive control (MPC) using the hybrid zonotope, a new mixed-integer set representation. This is accomplished by directly embedding the Karush Kuhn Tucker conditions of a parametric quadratic program within the hybrid zonotope set definition as mixed-integer constraints, and thus representing the set of all optimizers over a set of parameters. Using the set of explicit MPC solutions, it is shown how the plant’s closed-loop dynamics may be propagated through an identity that is calculated algebraically and does not require solving any optimization programs or taking set approximations. The proposed approach captures the worst-case exponential growth in the number of convex sets required to represent the exact reachable set, but incurs only linear growth in the number of variables used in the hybrid zonotope set representation. Beyond reachability analysis, it is shown that the set of optimizers represented by a hybrid zonotope may be decomposed to give the explicit solution of general quadratic multi-parametric programs as a collection of constrained zonotopes.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130252661","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

Hybrid Physics-based and Data-driven Model Predictive Control for Multi-Zone Building’s Thermal Comfort Under Disjunctive Uncertainty 分离不确定性下多分区建筑热舒适的混合物理与数据驱动模型预测控制

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867890

Guoqing Hu, F. You

{"title":"Hybrid Physics-based and Data-driven Model Predictive Control for Multi-Zone Building’s Thermal Comfort Under Disjunctive Uncertainty","authors":"Guoqing Hu, F. You","doi":"10.23919/ACC53348.2022.9867890","DOIUrl":"https://doi.org/10.23919/ACC53348.2022.9867890","url":null,"abstract":"This paper proposes a hybrid approach that utilizes the knowledge of the disjunctive uncertainty sets and incorporates them into the model predictive control (MPC). This approach targets multi-zone building control to the thermal comfort, and it’s robust to the uncertain weather forecast errors. The control objective is to maintain each zone’s temperature and relative humidity within the specified ranges by leveraging the minimum cost of energy of the underlying heating system. The hybrid model is constructed by using a physics-based and regression method for the temperature and relative humidity of each zone in the building. The uncertainty space is on the basis of historical weather forecast error data, which are captured by a group of disjunctive uncertainty sets using a k-means clustering algorithm. Machine learning approaches based on principal component analysis and kernel density estimation are used to construct each basic uncertainty set and reduce the conservatism of resulting robust control action under disturbances. Based on the proposed hybrid model and data-driven disjunctive uncertainty set, a robust MPC framework is further developed. An affine disturbance feedback rule is employed to obtain a tractable approximation of the robust MPC problem. Besides, the feasibility and stability of the proposed hybrid approach are ensured and discussed in detail. A case study of controlling temperature and relative humidity of a multi-zone building in Ithaca, New York, USA, is presented. The results demonstrate that the proposed hybrid approach can reduce 9.8% to 17.9% of total energy consumption compared to conventional robust MPC approaches. Moreover, the proposed hybrid approach can essentially satisfy the thermal constraints that certainty equivalent MPC and robust MPC largely violate.","PeriodicalId":366299,"journal":{"name":"2022 American Control Conference (ACC)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134148589","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: 1

Monte-Carlo Tree Search with Neural Networks for Petri Nets Petri网中神经网络的蒙特卡罗树搜索

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867348

Mengsen Jia, A. Köhler, R. Fritz, Ping Zhang

引用次数: 0

Optimal Moving Average Estimation of Noisy Random Walks using Allan Variance-informed Window Length 基于Allan方差的随机行走窗口长度的最优移动平均估计

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867447

H. Haeri, Behrad Soleimani, Kshitij Jerath

引用次数: 0

A Computationally Efficient Control Allocation Method for Four-Wheel-Drive and Four-Wheel-Independent-Steering Electric Vehicles 四轮驱动与四轮独立转向电动汽车的高效控制分配方法

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867549

引用次数: 1

Determining dissipativity for nonlinear systems from noisy data using Taylor polynomial approximation 用泰勒多项式近似从噪声数据中确定非线性系统的耗散率

2022 American Control Conference (ACC) Pub Date : 2022-06-08 DOI: 10.23919/ACC53348.2022.9867806

Tim Martin, F. Allgöwer

引用次数: 6