Reduced order model predictive control of high-dimensional systems
Abstract/Contents
- Abstract
- Many physical systems are modeled by finite-dimensional sets of ordinary differential equations (ODEs). Others have dynamics that evolve over a continuum (i.e. are infinite-dimensional) and are best modeled by partial differential equations (PDEs), including systems with fluid flows, deformable/flexible structures, or fluid-structure interaction. In practice, PDE models are generally semi-discretized to produce high-fidelity finite-dimensional ODE models. Since the dimension of these models can range from thousands to millions, computational challenges severely limit the use of standard approaches to model-based controller design. 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.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Lorenzetti, Joseph Steven |
|---|---|
| Degree supervisor | Pavone, Marco, 1980- |
| Thesis advisor | Pavone, Marco, 1980- |
| Thesis advisor | Farhat, Charbel |
| Thesis advisor | Gerdes, J. Christian |
| Thesis advisor | Rock, Stephen M |
| Degree committee member | Farhat, Charbel |
| Degree committee member | Gerdes, J. Christian |
| Degree committee member | Rock, Stephen M |
| Associated with | Stanford University, Department of Aeronautics and Astronautics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Joseph Lorenzetti. |
|---|---|
| Note | Submitted to the Department of Aeronautics and Astronautics. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/xb656xk9170 |
Access conditions
- Copyright
- © 2021 by Joseph Steven Lorenzetti
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...