國立虎尾科技大學 |

Nonlinear Model Predictive Control for Cooperative Control and Estimation.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Nonlinear Model Predictive Control for Cooperative Control and Estimation./
作者:	Ru, Pengkai.
面頁冊數:	1 online resource (153 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Aerospace engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369993868

Nonlinear Model Predictive Control for Cooperative Control and Estimation.
Ru, Pengkai.

Nonlinear Model Predictive Control for Cooperative Control and Estimation. - 1 online resource (153 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Recent advances in computational power have made it possible to do expensive online computations for control systems. It is becoming more realistic to perform computationally intensive optimization schemes online on systems that are not intrinsically stable and/or have very small time constants. Being one of the most important optimization based control approaches, model predictive control (MPC) has attracted a lot of interest from the research community due to its natural ability to incorporate constraints into its control formulation.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369993868Subjects--Topical Terms:

686400
Aerospace engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Nonlinear Model Predictive Control for Cooperative Control and Estimation.
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100 1 $a Ru, Pengkai. $3 1179261
245 1 0 $a Nonlinear Model Predictive Control for Cooperative Control and Estimation.
264 0 $c 2017
300 $a 1 online resource (153 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Kamesh Subbarao.
502 $a Thesis (Ph.D.) $c The University of Texas at Arlington $d 2017.
504 $a Includes bibliographical references
520 $a Recent advances in computational power have made it possible to do expensive online computations for control systems. It is becoming more realistic to perform computationally intensive optimization schemes online on systems that are not intrinsically stable and/or have very small time constants. Being one of the most important optimization based control approaches, model predictive control (MPC) has attracted a lot of interest from the research community due to its natural ability to incorporate constraints into its control formulation.
520 $a Linear MPC has been well researched and its stability can be guaranteed in the majority of its application scenarios. However, one issue that still remains with linear MPC is that it completely ignores the system's inherent nonlinearities thus giving a sub-optimal solution. On the other hand, if achievable, nonlinear MPC, would naturally yield a globally optimal solution and take into account all the innate nonlinear characteristics. While an exact solution to a nonlinear MPC problem remains extremely computationally intensive, if not impossible, one might wonder if there is a middle ground between the two. We tried to strike a balance in this dissertation by employing a state representation technique, namely, the state dependent coefficient (SDC) representation. This new technique would render an improved performance in terms of optimality compared to linear MPC while still keeping the problem tractable. In fact, the computational power required is bounded only by a constant factor of the completely linearized MPC.
520 $a The purpose of this research is to provide a theoretical framework for the design of a specific kind of nonlinear MPC controller and its extension into a general cooperative scheme. The controller is designed and implemented on quadcopter systems.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Aerospace engineering. $3 686400
655 7 $a Electronic books. $2 local $3 554714
690 $a 0538
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Texas at Arlington. $b Mechanical and Aerospace Engineering. $3 1148619
773 0 $t Dissertation Abstracts International $g 78-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10629295 $z click for full text (PQDT)