دانلود کتاب Adaptive and Learning-Based Control of Safety-Critical Systems

فهرست مطالب :

Preface
Motivation and Objectives
Intended Audience
Book Outline and Usage
Contents
Acronyms
Notation
1 Introduction
2 Stabilizing Control Design
2.1 Lyapunov Stability Theory
2.1.1 Stability Notions
2.1.2 Lyapunov Functions
2.2 Control Lyapunov Functions
2.3 Designing Control Lyapunov Functions
2.3.1 Feedback Linearization
2.3.2 Backstepping
2.3.3 Design Example
2.4 Notes
3 Safety-Critical Control
3.1 Safety and Set Invariance
3.2 Control Barrier Functions
3.3 High Order Control Barrier Functions
3.4 Notes
4 Adaptive Control Lyapunov Functions
4.1 Adaptive Nonlinear Control
4.2 Concurrent Learning Adaptive Control
4.2.1 Parameter Identification
4.2.2 Concurrent Learning
4.3 Exponentially Stabilizing Adaptive CLFs
4.4 Numerical Examples
4.5 Notes
5 Adaptive Safety-Critical Control
5.1 Adaptive Control Barrier Functions
5.2 Robust Adaptive Control Barrier Functions
5.3 High Order Robust Adaptive Control Barrier Functions
5.4 Numerical Examples
5.5 Notes
6 A Modular Approach to Adaptive Safety-Critical Control
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6.1 Input-to-State Stability
6.2 Modular Adaptive Stabilization
6.3 Input-to-State Safety
6.4 Numerical Examples
6.5 Notes
7 Robust Safety-Critical Control for Systems with Actuation Uncertainty
7.1 A Duality-Based Approach to Robust Safety-Critical Control
7.1.1 Robust Control Barrier Functions
7.1.2 Robust Control Lyapunov Functions
7.2 Online Learning for Uncertainty Reduction
7.3 Numerical Examples
7.4 Notes
8 Safe Exploration in Model-Based Reinforcement Learning
8.1 From Optimal Control to Reinforcement Learning
8.2 Value Function Approximation
8.3 Online Model-Based Reinforcement Learning
8.3.1 System Identification
8.3.2 Safe Exploration via Simulation of Experience
8.4 Numerical Examples
8.5 Notes
9 Temporal Logic Guided Safe Model-Based Reinforcement Learning
9.1 Temporal Logics and Automata
9.2 Simultaneous Stabilization and Safety
9.3 A Hybrid Systems Approach to LTL Control Synthesis
9.4 Temporal Logic Guided Reinforcement Learning
9.5 Numerical Examples
9.6 Notes
Index
Index