Textbook in PDF format

This book presents the concept of Control Barrier Function (CBF), which captures the evolution of safety requirements during the execution of a system and can be used to enforce safety. Safety is formalized using an emerging state-of-the-art approach based on CBFs, and many illustrative examples from autonomous driving, traffic control, and robot control are provided. Safety is central to autonomous systems since they are intended to operate with minimal or no human supervision, and a single failure could result in catastrophic results. The authors discuss how safety can be guaranteed via both theoretical and application perspectives. This presented method is computationally efficient and can be easily implemented in real-time systems that require high-frequency reactive control. In addition, the CBF approach can easily deal with nonlinear models and complex constraints used in a wide spectrum of applications, including autonomous driving, robotics, and traffic control. With the proliferation of autonomous systems, such as self-driving cars, mobile robots, and unmanned air vehicles, safety plays a crucial role in ensuring their widespread adoption. This book considers the integration of safety guarantees into the operation of such systems including typical safety requirements that involve collision avoidance, technological system limitations, and bounds on real-time executions. Adaptive approaches for safety are also proposed for time-varying execution bounds and noisy dynamics.
Machine Learning methodologies are utilized to take advantage of actual data that are generally available from the operation of an autonomous system. A “feasibility robustness” metric is introduced to measure the extent to which the feasibility problem we are concerned with is maintained in the presence of time-varying and unknown unsafe sets. The main idea rests on parameterizing the CBFs/HOCBFs involved in a safe-critical control problem and then learning the parameter values that maximize this feasibility robustness metric. The reader is expected to possess a basic knowledge of Machine Learning algorithms for classification problems.
1. Introduction to Autonomy and Safety
2. Control Barrier Functions
3. High Order Control Barrier Functions
4. Feasibility Guarantees for CBF-Based Optimal Control
5. Feasibility for CBF-Based Optimal Control Using Machine Learning
6. Adaptive Control Barrier Functions
7. Safety Guarantees for Systems with Unknown Dynamics: An Event-Driven Framework
8. Hierarchical Optimal Control with Barrier Functions
9. Applications to Autonomous Vehicles in Traffic Networks
10. Applications to Robotics