Mechanical Engineering ETDs

Publication Date

Winter 11-15-2024

Abstract

This Thesis develops a one-step predictive run-to-run controller (R2R-MPC) for automating a mechanical serial sectioning (MSS) system. MSS is a destructive material analysis process that iteratively removes slices of material and captures 2D images, reconstructing them into 3D representations. Commonly used in material science for characterizing materials and failure analysis, MSS typically operates in an open-loop fashion, which experiences high variability in material removal due to system disturbances. To address this, a robust closed-loop R2R-MPC is presented, modeling MSS process uncertainty using a linear differential inclusion identified from operational historical data. The R2R-MPC is posed as an optimization problem that computes incremental changes to minimize worst-case material removal errors. Combined with a run-to-run control framework, it provides integral action to reject disturbances and track removal rates. Simulation results demonstrate the efficacy of the robust R2R MPC compared to a conventional non-robust controller and are validated through experiments.

Keywords

closed-loop control, optimization, run-to-run control, predictive control

Degree Name

Mechanical Engineering

Level of Degree

Masters

Department Name

Mechanical Engineering

First Committee Member (Chair)

Claus Danielson

Second Committee Member

Tariq Khraishi

Third Committee Member

Andrew Polonsky

Document Type

Thesis

Language

English

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