Autonomous system control in unknown operating conditions
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Full Text E-thesis
Date
2021-08-24
Authors
Sohège, Yves
Journal Title
Journal ISSN
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Publisher
University College Cork
Published Version
Abstract
Autonomous systems have become an interconnected part of everyday life with the
recent increases in computational power available for both onboard computers and
offline data processing. The race by car manufacturers for level 5 (full) autonomy in
self-driving cars is well underway and new flying taxi service startups are emerging
every week, attracting billions in investments. Two main research communities,
Optimal Control and Reinforcement Learning stand out in the field of autonomous
systems, each with a vastly different perspective on the control problem. Controllers
from the optimal control community are based on models and can be rigorously
analyzed to ensure the stability of the system is maintained under certain operating
conditions. Learning-based control strategies are often referred to as model-free and
typically involve training a neural network to generate the required control actions
through direct interactions with the system. This greatly reduces the design effort
required to control complex systems. One common problem both learning- and model-
based control solutions face is the dependency on a priori knowledge about the system
and operating conditions such as possible internal component failures and external
environmental disturbances. It is not possible to consider every possible operating
scenario an autonomous system can encounter in the real world at design time. Models
and simulators are approximations of reality and can only be created for known
operating conditions. Autonomous system control in unknown operating conditions,
where no a priori knowledge exists, is still an open problem for both communities and
no control methods currently exist for such situations.
Multiple model adaptive control is a modular control framework that divides the
control problem into supervisory and low-level control, which allows for the
combination of existing learning- and model-based control methods to overcome the
disadvantages of using only one of these. The contributions of this thesis consist of
five novel supervisory control architectures, which have been empirically shown to
improve a system’s robustness to unknown operating conditions, and a novel low-
level controller tuning algorithm that can reduce the number of required controllers
compared to traditional tuning approaches. The presented methods apply to any
autonomous system that can be controlled using model-based controllers and can
be integrated alongside existing fault-tolerant control systems to improve robustness
to unknown operating conditions. This impacts autonomous system designers by
providing novel control mechanisms to improve a system’s robustness to unknown
operating conditions.
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Keywords
Fault-tolerant control , Learning-based control , Randomized control
Citation
Sohège, Y. 2021. Autonomous system control in unknown operating conditions. PhD Thesis, University College Cork.