2026-09-21T02:05:51Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/117840 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Hauser, Kris 2022-12 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms The student, William Edwards, accepted the attached license on 2022-12-06 at 22:07. The student, William Edwards, submitted this Thesis for approval on 2022-12-06 at 22:29. This Thesis was approved for publication on 2022-12-07 at 11:35. DSpace SAF Submission Ingestion Package generated from Vireo submission #18765 on 2023-04-12 at 07:39:14 Data-driven methods for design of model predictive controllers Edwards, William 2022-12-07 Robotics Model Predictive Control Automatic Tuning 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. Thesis eng https://hdl.handle.net/2142/117840 Copyright 2022 William Edwards M.S. Thesis Computer Science University of Illinois at Urbana-Champaign Computer Science