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Improving the Resilience of Barrier ...

Alan, Anil,

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Improving the Resilience of Barrier Function-Based Safety-Critical Controllers Against Uncertainties With Application to Connected Autonomous Vehicles /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improving the Resilience of Barrier Function-Based Safety-Critical Controllers Against Uncertainties With Application to Connected Autonomous Vehicles // Anil Alan.
Author:	Alan, Anil,
Description:	1 electronic resource (165 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Automotive engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31631550
ISBN:	9798384046059

Improving the Resilience of Barrier Function-Based Safety-Critical Controllers Against Uncertainties With Application to Connected Autonomous Vehicles /
Alan, Anil,

Improving the Resilience of Barrier Function-Based Safety-Critical Controllers Against Uncertainties With Application to Connected Autonomous Vehicles /Anil Alan. - 1 electronic resource (165 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

As safety is an ever more pressing requirement for modern control systems deployed into real-world environments, there has been an increase in demand to design controllers with mathematically rigorous safety guarantees. Control barrier functions (CBFs) provide one such framework, which takes our understanding of the system into consideration, and utilizes theoretical properties of sets to avoid unsafe states. Its intuitive nature leads to practical solutions for designing safe-by-design controllers, and their efficacy has been demonstrated experimentally in real-world conditions. A limitation inherent in the CBF framework is its dependence on the accuracy of the model capturing the underlying dynamics of the system, which consequently raises the question of resilience against model imperfections. The main contribution of this dissertation is a methodical investigation of a range of viable solutions to improve the resilience of CBF-based controllers. We study solutions in a categorical approach, and present our contributions on improving multiple aspects such as performance and conservativeness.The theoretical investigation is introduced in two main categories: input-to-state safety (ISSf) and robust control barrier functions (RCBFs). While methods in the former group looks to treat uncertainties to a more manageable level, methods in the latter group aims a complete cancellation approach. As a result, safety guarantees provided by these approaches land on a spectrum ranging from arbitrarily small (graceful) safety degradation to absolute robust safety. This dissertation delves into both approaches with a scope on controller design, where we focus on maintaining the beneficial properties of the CBF framework while improving the robustness. Another contribution of this dissertation is on the application front, where we utilize practical examples to support the theoretical results. In particular, a case study of designing safe and energy-efficient controllers for an autonomous truck is introduced as the main application. Hard-to-model nature of intricate relationships between complex subsystems makes the autonomous truck an ideal platform to evaluate the benefits of the CBF-based controllers. We deploy controllers (with and without the robustness feature) on a full-scale truck, and evaluate the theoretical guarantees experimentally in challenging conditions such as emergency brake.

English

ISBN: 9798384046059Subjects--Topical Terms:

2181195
Automotive engineering.
Subjects--Index Terms:

Safety-critical control

Improving the Resilience of Barrier Function-Based Safety-Critical Controllers Against Uncertainties With Application to Connected Autonomous Vehicles /
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520 $a As safety is an ever more pressing requirement for modern control systems deployed into real-world environments, there has been an increase in demand to design controllers with mathematically rigorous safety guarantees. Control barrier functions (CBFs) provide one such framework, which takes our understanding of the system into consideration, and utilizes theoretical properties of sets to avoid unsafe states. Its intuitive nature leads to practical solutions for designing safe-by-design controllers, and their efficacy has been demonstrated experimentally in real-world conditions. A limitation inherent in the CBF framework is its dependence on the accuracy of the model capturing the underlying dynamics of the system, which consequently raises the question of resilience against model imperfections. The main contribution of this dissertation is a methodical investigation of a range of viable solutions to improve the resilience of CBF-based controllers. We study solutions in a categorical approach, and present our contributions on improving multiple aspects such as performance and conservativeness.The theoretical investigation is introduced in two main categories: input-to-state safety (ISSf) and robust control barrier functions (RCBFs). While methods in the former group looks to treat uncertainties to a more manageable level, methods in the latter group aims a complete cancellation approach. As a result, safety guarantees provided by these approaches land on a spectrum ranging from arbitrarily small (graceful) safety degradation to absolute robust safety. This dissertation delves into both approaches with a scope on controller design, where we focus on maintaining the beneficial properties of the CBF framework while improving the robustness. Another contribution of this dissertation is on the application front, where we utilize practical examples to support the theoretical results. In particular, a case study of designing safe and energy-efficient controllers for an autonomous truck is introduced as the main application. Hard-to-model nature of intricate relationships between complex subsystems makes the autonomous truck an ideal platform to evaluate the benefits of the CBF-based controllers. We deploy controllers (with and without the robustness feature) on a full-scale truck, and evaluate the theoretical guarantees experimentally in challenging conditions such as emergency brake.
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650 4 $a Mechanical engineering. $3 649730
650 4 $a Robotics. $3 519753
650 4 $a Systems science. $3 3168411
653 $a Safety-critical control
653 $a Control barrier functions
653 $a Robust control
653 $a Autonomous vehicles
653 $a Conservativeness
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