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Economic based control system design.

Omell, Benjamin Peter.

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Economic based control system design.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Economic based control system design./
作者:	Omell, Benjamin Peter.
面頁冊數:	137 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
Contained By:	Dissertation Abstracts International75-07B(E).
標題:	Engineering, Automotive. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3580125
ISBN:	9781303882050

Economic based control system design.
Omell, Benjamin Peter.

Economic based control system design. - 137 p.

Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.

Thesis (Ph.D.)--Illinois Institute of Technology, 2013.

EMPC differs from traditional MPC by directly utilizing a profit based function as the objective as opposed to a quadratic function that minimizes the distance from a set point that is predetermined. However, implementation of EMPC can result in unexpected and at times pathological closed-loop behavior, including inventory creep, bang-bang actuation and instability. To address these issues, an infinite-horizon version of EMPC is developed and shown to avoid many of the performance issues observed in the finite-horizon version. First, modifications to the EMPC problem will be used for the conceptual development of the Economic Linear Optimal Controller (ELOC), which is a statistically constrained linear feedback controller. Then, point-wise-in-time constraints can be reintroduced using one of two methods; Constrained ELOC or Infinite-Horizon EMPC IH-EMPC).

ISBN: 9781303882050Subjects--Topical Terms:

1018477
Engineering, Automotive.

Economic based control system design.
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500 $a Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
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520 $a EMPC differs from traditional MPC by directly utilizing a profit based function as the objective as opposed to a quadratic function that minimizes the distance from a set point that is predetermined. However, implementation of EMPC can result in unexpected and at times pathological closed-loop behavior, including inventory creep, bang-bang actuation and instability. To address these issues, an infinite-horizon version of EMPC is developed and shown to avoid many of the performance issues observed in the finite-horizon version. First, modifications to the EMPC problem will be used for the conceptual development of the Economic Linear Optimal Controller (ELOC), which is a statistically constrained linear feedback controller. Then, point-wise-in-time constraints can be reintroduced using one of two methods; Constrained ELOC or Infinite-Horizon EMPC IH-EMPC).
520 $a We also investigate the impact of problem formulation modifications on the ELOC. The first issue is that of disturbance modeling and the second is the impact of controller sample-time. The third topic concerns incorporation of computational delay in the feedback-loop, using both full and partial state information structures. Finally an illustration of the impact of plant-model mismatch is presented.
520 $a The Constrained ELOC formulation is further modified to allow for market responsive smart grid applications. In particular an Integrated Gasification Combined Cycle (IGCC) process with hydrogen storage will be used to demonstrate the Constrained ELOC for such applications. The ELOC will be used as a vehicle to exploit dispatch capabilities by pursuing directly the objective of maximizing revenue. The idea being that process modifications to enable dispatch capabilities will allow for a time-shift of power production away from periods of low energy value to periods of high value. An in depth discussion is provided on how energy value forecasts are incorporated into the design of the constrained ELOC. Finally, an extension of the ELOC to the controller embedded equipment design is provided.
520 $a The work concludes with a discussion of the computational aspects of solving the ELOC problem. In particular, the impact of reverse-convex constraints inherent to the ELOC problem are discussed along with existing solution methods. The main contribution of this final chapter is a novel application of the Generalized Bender's Decomposition (GBD) algorithm to the ELOC problem. This new approach is shown to retain global optimality, reduce computational effort (by orders of magnitude) and expand the class of problems one can solve.
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