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Certifiable Synthesis and Analysis for Autonomy: Data-Driven and Analytical Techniques
Certifiable Synthesis and Analysis for Autonomy: Data-Driven and Analytical Techniques
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260209102858
- ISBN
- 9798291575390
- DDC
- 629.8
- 저자명
- Sun, Dawei.
- 서명/저자
- Certifiable Synthesis and Analysis for Autonomy: Data-Driven and Analytical Techniques
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 139 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
- 주기사항
- Advisor: Mitra, Sayan.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
- 초록/해제
- 요약Over the past few decades, progress in control theory, robotics, and machine learning has enabled autonomy across diverse application domains such as the chemical industry, mechanical manufacturing, and the aerospace industry. Despite these advances, the development and implementation of autonomous systems continue to confront numerous technical challenges. One particular issue is ensuring the certifiability of autonomous systems. As these systems enter more and more safety-critical applications, constructing certifiable systems becomes a crucial task for the community. In traditional industrial applications such as aircraft manufacturing, this problem has been extensively addressed with established certification standards. However, the same cannot be said for emerging autonomous systems interacting with complex environments, for instance, autonomous vehicles. Certifiability in these contexts remains a distant goal.In this dissertation, we focus on two types of problems, namely, the synthesis problem and the analysis problem. We combine data-driven approaches and analytical approaches to solve these two types of problems. The core contributions of this dissertation include (1) A formal definition of a general synthesis problem for temporal logic specifications. (2) A learning-based approach that incorporates contraction theory into machine learning to construct a tracking controller for a given dynamical system. Moreover, the tracking error of the synthesized controller is formally bounded. (3) An optimization-based path planner for signal temporal logic specifications. By combining the path planner and the tracking controller, we solve the synthesis problem defined in (1). (4) The notion of reachability functions and a tool, NeuReach, that can automatically construct a reachability function for a black-box system. (5) Demonstration of the proposed approaches on a perception-based control synthesis problem. For all the proposed approaches, we arm them with rigorous theoretical analysis. On the experimental side, we evaluate the proposed approaches on a variety of benchmarks in simulation. Moreover, we deploy some of the approaches on a quadcopter to complete a trajectory-tracking task and a safe landing task.
- 일반주제명
- Robotics
- 일반주제명
- Computer science
- 키워드
- Machine learning
- 키워드
- Control theory
- 키워드
- Safe autonomy
- 키워드
- Black-box system
- 기타저자
- University of Illinois at Urbana-Champaign Electrical & Computer Eng
- 기본자료저록
- Dissertations Abstracts International. 87-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■1001 ▼aSun, Dawei.
■24510▼aCertifiable Synthesis and Analysis for Autonomy: Data-Driven and Analytical Techniques
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a139 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-02, Section: B.
■500 ▼aAdvisor: Mitra, Sayan.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
■520 ▼aOver the past few decades, progress in control theory, robotics, and machine learning has enabled autonomy across diverse application domains such as the chemical industry, mechanical manufacturing, and the aerospace industry. Despite these advances, the development and implementation of autonomous systems continue to confront numerous technical challenges. One particular issue is ensuring the certifiability of autonomous systems. As these systems enter more and more safety-critical applications, constructing certifiable systems becomes a crucial task for the community. In traditional industrial applications such as aircraft manufacturing, this problem has been extensively addressed with established certification standards. However, the same cannot be said for emerging autonomous systems interacting with complex environments, for instance, autonomous vehicles. Certifiability in these contexts remains a distant goal.In this dissertation, we focus on two types of problems, namely, the synthesis problem and the analysis problem. We combine data-driven approaches and analytical approaches to solve these two types of problems. The core contributions of this dissertation include (1) A formal definition of a general synthesis problem for temporal logic specifications. (2) A learning-based approach that incorporates contraction theory into machine learning to construct a tracking controller for a given dynamical system. Moreover, the tracking error of the synthesized controller is formally bounded. (3) An optimization-based path planner for signal temporal logic specifications. By combining the path planner and the tracking controller, we solve the synthesis problem defined in (1). (4) The notion of reachability functions and a tool, NeuReach, that can automatically construct a reachability function for a black-box system. (5) Demonstration of the proposed approaches on a perception-based control synthesis problem. For all the proposed approaches, we arm them with rigorous theoretical analysis. On the experimental side, we evaluate the proposed approaches on a variety of benchmarks in simulation. Moreover, we deploy some of the approaches on a quadcopter to complete a trajectory-tracking task and a safe landing task.
■590 ▼aSchool code: 0090.
■650 4▼aRobotics
■650 4▼aComputer science
■653 ▼aMachine learning
■653 ▼aControl theory
■653 ▼aSafe autonomy
■653 ▼aAnalytical techniques
■653 ▼aBlack-box system
■690 ▼a0771
■690 ▼a0984
■690 ▼a0800
■71020▼aUniversity of Illinois at Urbana-Champaign▼bElectrical & Computer Eng.
■7730 ▼tDissertations Abstracts International▼g87-02B.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17365933▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
