| Description |
1 online resource (xx, 194 pages) : illustrations (chiefly color) |
| Series |
Synthesis lectures on computer science |
| Contents |
Intro -- 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 |
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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 -- [DELETE] -- 6.1 Input-to-State Stability |
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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 |
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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 |
| Summary |
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 |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Automatic control.
|
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Control theory.
|
|
Machine learning.
|
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Automatic control
|
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Control theory
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Machine learning
|
| Form |
Electronic book
|
| Author |
Belta, Calin, author
|
| ISBN |
9783031293108 |
|
303129310X |
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