Data-driven methods for design of model predictive controllers

Edwards, William

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https://hdl.handle.net/2142/117840 Copy

Description

Title

Data-driven methods for design of model predictive controllers

Author(s)

Edwards, William

Issue Date

2022-12-07

Director of Research (if dissertation) or Advisor (if thesis)

Hauser, Kris

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• robotics
• model predictive control
• automatic tuning

Abstract

Model predictive control (MPC) is a powerful feedback technique that is often used in data-driven robotics. The performance of data-driven MPC depends on the accuracy of the model, which often requires careful tuning. Furthermore, specifying the task with an objective function and synthesizing a feedback policy are not straightforward and typically lead to suboptimal solutions driven by trial and error. In this work, we seek to address these challenges by investigating data-driven methods for system identification, task specification, and control synthesis of unknown dynamical systems. First, we conduct a case study on the design of a data-driven MPC for performing automatic needle insertion in deep anterior lamellar keratoplasty, a challenging ophthalmic microsurgery task. We propose a data-driven method for controller synthesis and selection and demonstrate that the synthesized controller outperforms a state-of-the-art baseline in ex vivo physical experiments. Next, we present AutoMPC, an open-source Python package for automatic synthesis of data-driven MPC. We demonstrate the AutoMPC outperforms a state-of-the-art offline reinforcement learning algorithm on several standard control benchmarks. We further demonstrate that AutoMPC outperforms standard control baselines in physical experiments on an underwater soft robot.

Graduation Semester

2022-12

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 William Edwards

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