東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Real-Time Decision Making in Control and Optimization with Performance and Safety Guarantees.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Real-Time Decision Making in Control and Optimization with Performance and Safety Guarantees./
作者:	Li, Yingying.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	294 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Applied mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28720152
ISBN:	9798496525619

Real-Time Decision Making in Control and Optimization with Performance and Safety Guarantees.
Li, Yingying.

Real-Time Decision Making in Control and Optimization with Performance and Safety Guarantees. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 294 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Harvard University, 2021.

This item must not be sold to any third party vendors.

With the rapid evolution of sensing, communication, computation, and actuation technology, recent years have witnessed a growing interest in real-time decision making, which leverages the data received in real time to improve the decision-making performance. Real-time decision making enjoys great potentials in two major domains: (i) in applications with time-varying environments, harnessing real-time data can allow quick adaptation to the new environments, (ii) when the underlying models are unknown, one can learn the models and improve the decisions by real-time data from the interaction with the systems. However, when applying real-time decision making to these domains, there are several major challenges, three of which are discussed below. Firstly, the real-time data available are usually limited and imperfect, which calls for more understanding on the effective usage and fundamental values of the real-time data. Secondly, the implementability on large-scale systems calls for computation-efficient and communication-efficient algorithm design. Thirdly, safety is crucial for the applications in the real world. This thesis aims to tackle these challenges by studying real-time decision making in control and optimization with time-varying environments and/or unknown models. Specifically, the thesis consists of three parts.In Part I, we consider online convex optimization and online optimal control with time-varying cost functions. The future cost functions are unknown but some predictions on the future are available. We design gradient-based online algorithms to leverage predictions. Further, we consider different models of prediction errors for generality, and study our algorithms' regret guarantees in these models. We also provide fundamental lower bounds, which provide more insights into the fundamental values of the prediction information.In Part II, we consider optimal control with constraints on the states and actions. We consider two problems, one with time-varying cost functions but known systems, and the other with unknown systems in a time-invariant setting. We design online algorithms for both settings. We provide safety guarantees of our online algorithms, i.e., constraint satisfaction and feasibility. We also provide sublinear regret guarantees for both algorithms, which indicate that our algorithms achieve desirable performance while ensuring safety requirements.In Part III, we consider a decentralized linear quadratic control problem with unknown systems. We design a distributed policy gradient algorithm that only requires limited communication capacities. We provide a sample complexity bound for reaching a stationary point of the distributed policy optimization. We also provide stability guarantees for the controllers generated along the way.

ISBN: 9798496525619Subjects--Topical Terms:

2122814
Applied mathematics.
Subjects--Index Terms:

Learning-based control

Real-Time Decision Making in Control and Optimization with Performance and Safety Guarantees.
LDR:04047nmm a2200385 4500 001 2347523
005 20220801062226.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798496525619
035 $a (MiAaPQ)AAI28720152
035 $a AAI28720152
040 $a MiAaPQ $c MiAaPQ
100 1 $a Li, Yingying. $3 1036381
245 1 0 $a Real-Time Decision Making in Control and Optimization with Performance and Safety Guarantees.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 294 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
500 $a Advisor: Li, Na.
502 $a Thesis (Ph.D.)--Harvard University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a With the rapid evolution of sensing, communication, computation, and actuation technology, recent years have witnessed a growing interest in real-time decision making, which leverages the data received in real time to improve the decision-making performance. Real-time decision making enjoys great potentials in two major domains: (i) in applications with time-varying environments, harnessing real-time data can allow quick adaptation to the new environments, (ii) when the underlying models are unknown, one can learn the models and improve the decisions by real-time data from the interaction with the systems. However, when applying real-time decision making to these domains, there are several major challenges, three of which are discussed below. Firstly, the real-time data available are usually limited and imperfect, which calls for more understanding on the effective usage and fundamental values of the real-time data. Secondly, the implementability on large-scale systems calls for computation-efficient and communication-efficient algorithm design. Thirdly, safety is crucial for the applications in the real world. This thesis aims to tackle these challenges by studying real-time decision making in control and optimization with time-varying environments and/or unknown models. Specifically, the thesis consists of three parts.In Part I, we consider online convex optimization and online optimal control with time-varying cost functions. The future cost functions are unknown but some predictions on the future are available. We design gradient-based online algorithms to leverage predictions. Further, we consider different models of prediction errors for generality, and study our algorithms' regret guarantees in these models. We also provide fundamental lower bounds, which provide more insights into the fundamental values of the prediction information.In Part II, we consider optimal control with constraints on the states and actions. We consider two problems, one with time-varying cost functions but known systems, and the other with unknown systems in a time-invariant setting. We design online algorithms for both settings. We provide safety guarantees of our online algorithms, i.e., constraint satisfaction and feasibility. We also provide sublinear regret guarantees for both algorithms, which indicate that our algorithms achieve desirable performance while ensuring safety requirements.In Part III, we consider a decentralized linear quadratic control problem with unknown systems. We design a distributed policy gradient algorithm that only requires limited communication capacities. We provide a sample complexity bound for reaching a stationary point of the distributed policy optimization. We also provide stability guarantees for the controllers generated along the way.
590 $a School code: 0084.
650 4 $a Applied mathematics. $3 2122814
650 4 $a Theoretical mathematics. $3 3173530
650 4 $a Information technology. $3 532993
650 4 $a Decision making. $3 517204
650 4 $a Real time. $3 3562675
653 $a Learning-based control
653 $a Linear quadratic regulator
653 $a Online control
653 $a Online convex optimization
653 $a Real-time decision making
653 $a Reinforcement learning
690 $a 0364
690 $a 0642
690 $a 0489
710 2 $a Harvard University. $b Engineering and Applied Sciences - Applied Math. $3 3184332
773 0 $t Dissertations Abstracts International $g 83-05B.
790 $a 0084
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28720152