This book stems from the growing use of learning-based techniques, such as reinforcement learning and adaptive control, in the control of autonomous and safety-critical systems. Safety is critical to many applications, such as autonomous driving, air traffic control, and robotics. As these learning-enabled technologies become more prevalent in the control of autonomous systems, it becomes increasingly important to ensure that such systems are safe. To address these challenges, the authors provide a self-contained treatment of learning-based control techniques with rigorous guarantees of stability and safety. This book contains recent results on provably correct control techniques from specifications that go beyond safety and stability, such as temporal logic formulas. The authors bring together control theory, optimization, machine learning, and formal methods and present worked-out examples and extensive simulation examples to complement the mathematical style of presentation. Prerequisites are minimal, and the underlying ideas are accessible to readers with only a brief background in control-theoretic ideas, such as Lyapunov stability theory.

Table of Contents

1 Introduction

2 Stabilizing Control Design

3 Safety-Critical Control

4 Adaptive Control Lyapunov Functions

5 Adaptive Safety-Critical Control

6 A Modular Approach to Adaptive Safety-Critical Control

7 Robust Safety-Critical Control for Systems with Actuation Uncertainty

8 Safe Exploration in Model-Based Reinforcement Learning

9 Temporal Logic Guided Safe Model-Based Reinforcement Learning

About the Authors

Max Cohen is a Ph.D. Candidate and National Science Foundation Graduate Research Fellow in the Department of Mechanical Engineering at Boston University. He received his B.S. in Mechanical Engineering from the University of Florida and his M.S. in Mechanical Engineering from Boston University. Outside of academia he has worked as a Systems Engineer at Lockheed Martin and held research internships at MIT Lincoln Laboratory. His research interests include nonlinear control theory, learning-based control, and hybrid systems, with applications in robotics and autonomous systems.​

Calin Belta, Ph.D, is a Professor in the Department of Mechanical Engineering at Boston University, where he holds the Tegan family Distinguished Faculty Fellowship. He is also the Director of the BU Robotics Lab. He received B.Sc. and M.Sc. degrees from the Technical University of Iasi and M.Sc. and Ph.D. degrees from the University of Pennsylvania. His research interests include dynamics and control theory, with particular emphasis on hybrid and cyber-physical systems, formal synthesis and verification, and applications in robotics and systems biology. He has received the Air Force Office of Scientific Research Young Investigator Award and the National Science Foundation CAREER Award. He is a Fellow and Distinguished Lecturer of