Biological inspired control and machine learning for clinical rehabilitation and engineering systems

Doublein, Thomas (2024) Biological inspired control and machine learning for clinical rehabilitation and engineering systems. PhD thesis, University of Glasgow.

Abstract

Human quiet standing has been studied over the years in order to model controllers
able to replicate variability and intermittency present in human control mechanisms. The
Intermittent Control (IC) framework was proposed as a computational model and can
be described by a serie of open-loop trajectories with close-loop triggering. However, the
original implementation is based on a deterministic approach which requires knowledge of
the underlying system’s dynamics. This thesis explores the capabilities of a data-driven
stochastic Intermitent Controller, using Gaussian Processes (GP), applied mainly to a
Single Inverted Pendulum (SIP).

Throughout this thesis, the Intermitent Controller framework has been adapted to move
toward a data-driven intermittent controller using Reinforcement Learning (RL) and Data
Informativity (DI) to estimate the state feedback gains. Simulations show the benefit of
the open-loop trajectories created by IC in improving the overall estimation of the parameters,
compared to Continuous Controllers. These results are the initial basis for a deterministic
data-driven Intermittent Controller. In addition, the integration of GPs within
this IC framework is able to introduce varibility in the generated control input by using
probabilistic open-loop trajectories. Both these implementations have been combined to
create the first data-driven stochastic intermittent controller. Results presented in this
work are showing the capability of this newly controller approach to handle adaptation as
well as switching between deterministic like behavior to fully probabilistic characteristics
based on IC and GP parameters.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: T Technology > TA Engineering (General). Civil engineering (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: Gollee, Dr. Henrik
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84709
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 19 Nov 2024 15:13
• Last Modified: 22 Nov 2024 12:11
• Thesis DOI: 10.5525/gla.thesis.84709
• URI: https://theses.gla.ac.uk/id/eprint/84709

Related URLs

• Conference proceeding