Data-driven methods for design of model predictive controllers
Edwards, William
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https://hdl.handle.net/2142/117840
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
Language
eng
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.
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