Supervisory control with online learning for stabilization and near-optimal performance of time-varying linear systems

Model-based control methods are widely used in robotics because they use system equations to compute efficient control actions. However, these methods often struggle in real-world situations where the system model is not perfect or where there are unexpected disturbances. In addition, solving nonlinear optimization problems in real time can be too slow or too demanding for systems with limited onboard computing power. To address these challenges, this study proposes a hybrid control approach that combines classical control, optimal planning and online learning. The system we focus on is a 2D quadrotor, modeled as a six-dimensional system controlled using force and torque inputs. At the lower level, we use three different types of controllers: a basic Proportional-Derivative (PD) controller, a trajectory planner using nonlinear programming (NLP), and a control law based on Pontryagin’s Maximum Principle (PMP), which we implement using PyTorch. At the higher level, we add a Multi-Armed Bandit (MAB) layer using the EXP3 algorithm. This layer learns over time which controller performs best based on feedback like tracking error and energy usage. It allows the system to switch between controllers depending on how well they are working at each moment. Our results show that this combination of planning and learning can make the system more reliable and adaptive, even in uncertain environments. While we apply this to a quadrotor, the same idea can be used for many other types of robotic systems.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Industrial Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Roy, Dhritiman

Contributors dc:contributor

• Li, Yingying

• Online Learning
• Multi-armed Bandit
• Adaptive Control
• Pontryagin’s Maximum Principle

Statement dc:rights

• Copyright 2025 Dhritiman Roy

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/129620

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F129620