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Toward Co-Design of Autonomous Aeros...

Bradley, Justin M.

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Toward Co-Design of Autonomous Aerospace Cyber-Physical Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Toward Co-Design of Autonomous Aerospace Cyber-Physical Systems./
作者:	Bradley, Justin M.
面頁冊數:	148 p.
附註:	Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
Contained By:	Dissertation Abstracts International76-02B(E).
標題:	Engineering, Aerospace. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3636536
ISBN:	9781321181821

Toward Co-Design of Autonomous Aerospace Cyber-Physical Systems.
Bradley, Justin M.

Toward Co-Design of Autonomous Aerospace Cyber-Physical Systems. - 148 p.

Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.

Thesis (Ph.D.)--University of Michigan, 2014.

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

Modern vehicles are equipped with a complex suite of computing (cyber) and electromechanical (physical) systems. Holistic design, modeling, and optimization of such Cyber-Physical Systems (CPS) requires new techniques capable of integrated analysis across the full CPS. This dissertations introduces two methods for balancing cyber and physical resources in a step toward holistic co-design of CPS. First, an ordinary differential equation model abstraction of controller sampling rate is developed and added to the equations of motion of a physical system to form a holistic discrete-time-varying linear system representing the CPS controller. Using feedback control, this cyber effector, sampling rate, is then co-regulated alongside physical effectors in response to physical system tracking error. This technique is applied to a spring-mass-damper, inverted pendulum, and finally to attitude control of a small satellite (CubeSat). Additionally, two new controllers for discrete-time-varying systems are introduced; a gain-scheduled discrete-time linear regulator (DLQR) in which DLQR gains are scheduled over time-varying sampling rates, and a forward-propagation Riccati-based (FPRB) controller. The FPRB CPS controller shows promise in balancing cyber and physical resources.

ISBN: 9781321181821Subjects--Topical Terms:

1018395
Engineering, Aerospace.

Toward Co-Design of Autonomous Aerospace Cyber-Physical Systems.
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245 1 0 $a Toward Co-Design of Autonomous Aerospace Cyber-Physical Systems.
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500 $a Source: Dissertation Abstracts International, Volume: 76-02(E), Section: B.
500 $a Adviser: Ella M. Atkins.
502 $a Thesis (Ph.D.)--University of Michigan, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Modern vehicles are equipped with a complex suite of computing (cyber) and electromechanical (physical) systems. Holistic design, modeling, and optimization of such Cyber-Physical Systems (CPS) requires new techniques capable of integrated analysis across the full CPS. This dissertations introduces two methods for balancing cyber and physical resources in a step toward holistic co-design of CPS. First, an ordinary differential equation model abstraction of controller sampling rate is developed and added to the equations of motion of a physical system to form a holistic discrete-time-varying linear system representing the CPS controller. Using feedback control, this cyber effector, sampling rate, is then co-regulated alongside physical effectors in response to physical system tracking error. This technique is applied to a spring-mass-damper, inverted pendulum, and finally to attitude control of a small satellite (CubeSat). Additionally, two new controllers for discrete-time-varying systems are introduced; a gain-scheduled discrete-time linear regulator (DLQR) in which DLQR gains are scheduled over time-varying sampling rates, and a forward-propagation Riccati-based (FPRB) controller. The FPRB CPS controller shows promise in balancing cyber and physical resources.
520 $a Second, we propose a cost function of cyber and physical parameters to optimize an Unmanned Aircraft System (UAS) trajectory for a pipeline surveillance mission. Optimization parameters are UAV velocity and mission-critical surveillance task execution rate. Metrics for pipeline image information, energy, cyber utilization, and time comprise the cost function and Pareto fronts are analyzed to gain insight into cyber and physical tradeoffs for mission success. Finally, the cost function is optimized using numerical methods, and results from several cost weightings and Pareto front analyses are tabulated. We show that increased mission success can be achieved by considering both cyber and physical parameters together.
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