Assuring Safe Operation of Robotic Systems under Uncertainty

Control and Learning Methods

By: Cong Li, Yongchao Wang, Fangzhou Liu, Xinglong Zhang

Write A Review

eText | 28 November 2025 | Edition Number 1

At a Glance

Format
ePUB

eText

$171.60

or 4 interest-free payments of $42.90 with

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

Assuring Safe Operation of Robotic Systems under Uncertainty: Control and Learning Methods applies set-theoretic and reinforcement learning approaches to formulate, analyze, and solve the challenge of ensuring safe operation of robotic systems in an uncertain environment.

The authors adopt learning-supported, set-theoretic methods—specifically, the barrier Lyapunov function and the control barrier function—to achieve desirable robust safety with guaranteed performance in continuous-time nonlinear control applications. They also combine reinforcement learning with control theory to ensure safe learning and optimization. The reinforcement learning-based optimization framework incorporates safety and robustness guarantees by applying theoretical analysis tools from the field of control.

This book will be of interest to researchers, engineers, and students specializing in robot planning and control.