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Reduced Order Model Predictive Control of High-Dimensional Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reduced Order Model Predictive Control of High-Dimensional Systems./
作者:	Lorenzetti, Joseph Steven.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	137 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Robots. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28812958
ISBN:	9798494454935

Reduced Order Model Predictive Control of High-Dimensional Systems.
Lorenzetti, Joseph Steven.

Reduced Order Model Predictive Control of High-Dimensional Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 137 p.

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

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

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

Dynamics models are used to develop control algorithms for many applications, including aerospace vehicles, self-driving cars, robots, industrial plants, and more. Physics-based models are particularly common, due to their interpretability and ability to efficiently encode knowledge of the system's behavior. An ideal model accurately represents the system's dynamics while not introducing significant complexity into the control algorithm. However, conflict between model accuracy and complexity often arises, and thus the two must be balanced to satisfy practical requirements. While lowdimensional models provide sufficient accuracy for some control applications, there are also many where high-dimensional models are required. One important application is the control of systems best modeled by partial differential equations (PDEs), such as systems with fluid flows or fluidstructure interaction (e.g. highly maneuverable or flexible aircraft), deformable structures (e.g. soft robots), and more. In practice, infinite-dimensional PDE models are generally semi-discretized to produce high-fidelity ordinary differential equation (ODE) models that are finite-dimensional. Since the dimension of these models can range from thousands to millions, standard model-based controller design can be extremely challenging.In this thesis, we propose an approach for efficiently designing high-performing controllers based on high-dimensional models. Specifically, we develop a model predictive control (MPC) algorithm for solving constrained optimal control problems that leverages high-fidelity, but low-dimensional, reduced order approximations of the original model to satisfy practical computational requirements. In the linear setting, we combine existing ideas from tube MPC with novel approaches for controller synthesis and analysis to develop a reduced order MPC (ROMPC) scheme for solving robust, output feedback control problems, and we provide theoretical closed-loop performance guarantees that explicitly account for model reduction error. We also extend the ROMPC scheme to the nonlinear setting by exploiting piecewise-affine reduced order models. We motivate and validate the proposed approach through two case studies. First, we use a linear, coupled rigid-body/fluid dynamics model for aircraft control, where the high-dimensional computational fluid dynamics (CFD) model has over one million dimensions. Second, we use a nonlinear finite element model (FEM) with over ten thousand dimensions to control a soft robot. Simulation and hardware experiments are used in both studies to demonstrate the practicality and performance of ROMPC.

ISBN: 9798494454935Subjects--Topical Terms:

529507
Robots.

Reduced Order Model Predictive Control of High-Dimensional Systems.
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