Certifiable synthesis and analysis for autonomy: Data-driven and analytical techniques

Sun, Dawei

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https://hdl.handle.net/2142/122050 Copy

Description

Title

Certifiable synthesis and analysis for autonomy: Data-driven and analytical techniques

Author(s)

Sun, Dawei

Issue Date

2023-12-01

Director of Research (if dissertation) or Advisor (if thesis)

Mitra, Sayan

Doctoral Committee Chair(s)

Mitra, Sayan

Committee Member(s)

• Dullerud, Geir E.
• Srikant, Rayadurgam
• Belabbas, Mohamed Ali

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

Machine learning, Control theory, Safe autonomy, Robotics

Abstract

Over 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.

Graduation Semester

2023-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/122050

Copyright and License Information

Copyright 2023 Dawei Sun

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