000			03704 a2200193 4500
008			230501b |||||||| |||| 00| 0 eng d
020			_a9781107001183 (HB)
041			_aeng
080			_a53:51 _bBEC
100			_aBechhoefer, John
245			_aControl theory for Physicists
260			_bCambridge University Press _c2021 _aUnited Kingdom
300			_axiii, 645p
505			_aCover Half-title page Title page Copyright page Contents Preface Part I Core Material 1 Historical Introduction 1.1 Historical Overview 1.2 Lessons from History 1.3 Control and Information 1.4 Notes and References 2 Dynamical Systems 2.1 Introduction: The Pendulum as a Dynamical System 2.2 General Formulation 2.3 Frequency Domain 2.4 Time Domain 2.5 Stability 2.6 Bifurcations 2.7 Summary 2.8 Notes and References Problems 3 Frequency-Domain Control 3.1 Basic Feedback Ideas 3.2 Two Case Studies 3.3 Integral, Derivative, and PID 3.4 Feedforward 3.5 Stability of Closed-Loop Systems 3.6 Delays and Nonminimum Phase 3.7 Designing the Control 3.8 MIMO Systems 3.9 Summary 3.10 Notes and References Problems 4 Time-Domain Control 4.1 Controllability and Observability 4.2 Control Based on the State 4.3 Control Based (Indirectly) on the Output 4.4 Summary 4.5 Notes and References Problems 5 Discrete-Time Systems 5.1 Discretizing Signals 5.2 Tools for Discrete Dynamical Systems 5.3 Discretizing Dynamical Systems 5.4 Design of Digital Controllers 5.5 Summary 5.6 Notes and References Problems 6 System Identification 6.1 Physics or Phenomenology? 6.2 Measuring Dynamics 6.3 Model Building 6.4 Model Selection 6.5 Model Reduction 6.6 Summary 6.7 Notes and References Problems Part II Advanced Ideas 7 Optimal Control 7.1 One-Dimensional Example 7.2 Continuous Systems 7.3 Linear Quadratic Regulator 7.4 Dynamic Programming 7.5 Hard Constraints 7.6 Feedback 7.7 Numerical Methods 7.8 Summary 7.9 Notes and References Problems 8 Stochastic Systems 8.1 Kalman Filter 8.2 Linear Quadratic Gaussian Control 8.3 Bayesian Filtering 8.4 Nonlinear Filtering 8.5 Why State Estimation Can Be a Hard Problem 8.6 Stochastic Optimal Control 8.7 Smoothing and Prediction 8.8 Summary 8.9 Notes and References Problems 9 Robust Control 9.1 Robust Feedforward 9.2 Robust Feedback 9.3 Risk 9.4 Worst-Case Methods: The H Min-Max Approach 9.5 Summary 9.6 Notes and References Problems 10 Adaptive Control 10.1 Direct Methods 10.2 Indirect Methods 10.3 Adaptive Feedforward Control 10.4 Optimal Adaptive Control 10.5 Neural Networks 10.6 Summary 10.7 Notes and References Problems 11 Nonlinear Control 11.1 Feedback Linearization 11.2 Lyapunov-Based Design 11.3 Collective Dynamics 11.4 Controlling Chaos 11.5 Summary 11.6 Notes and References Problems Part III Special Topics 12 Discrete-State Systems 12.1 Discrete-State Models 12.2 Inferring States and Models 12.3 Control 12.4 Summary 12.5 Notes and References Problems 13 Quantum Control 13.1 Quantum Mechanics 13.2 Three Types of Quantum Control
520			_a"This book extends a tutorial I wrote on control theory (Bechhoefer, 2005). In both the article and this book, my goal has been "to make the strange familiar, and the familiar strange."1 The strange is control theory-feedback and feedforward, transfer functions and minimum phase, H8 metrics and Z-transforms, and many other ideas that are not usually part of the education of a physicist. The familiar includes notions such as causality, measurement, robustness, and entropy-concepts physicists think they know-that acquire new meanings in the light of control theory. I hope that this book accomplishes both tasks"-- Provided by publisher
650			_aPhysicists _aControl theory
690			_aPhysics
942			_cBK _01
999			_c59769 _d59769