Learning-Based Adaptive Control

An Extremum Seeking Approach - Theory and Applications
 
 
Butterworth-Heinemann (Verlag)
  • 1. Auflage
  • |
  • erschienen am 2. August 2016
  • |
  • 282 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-0-12-803151-3 (ISBN)
 

Adaptive control has been one of the main problems studied in control theory. The subject is well understood, yet it has a very active research frontier. This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control. As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change. This book offers a new perspective about how to deal with these variations. By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained.


  • Includes a good number of Mechatronics Examples of the techniques.
  • Compares and blends Model-free and Model-based learning algorithms.
  • Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.


Mouhacine Benosman worked at universities in Rome, Italy, Reims, France, and Glasgow, Scotland before spending 5 years as a Research Scientist with the Temasek Laboratories at the National University of Singapore.
He is presently senior researcher at the Mitsubishi Electric Research Laboratories (MERL), Cambridge, USA. His research interests include modelling and control of flexible systems, non-linear robust and fault tolerant control, vibration suppression in industrial machines, multi-agent control with applications to smart-grid, and more recently his research focus is on learning and adaptive control with application to mechatronics systems.
The author has published more than 40 peer-reviewed journals and conferences, and more than 10 patents in the field of mechatronics systems control. He is a senior member of the IEEE society and an Associate Editor of the Control System Society Conference Editorial Board.
  • Englisch
  • Oxford
  • |
  • USA
Elsevier Science
  • 23,11 MB
978-0-12-803151-3 (9780128031513)
0128031514 (0128031514)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Learning-Based Adaptive Control: An Extremum Seeking Approach - Theory and Applications
  • Copyright
  • Contents
  • Preface
  • Acknowledgments
  • Chapter 1: Some Mathematical Tools
  • 1.1 Norms Definitions and Properties
  • 1.2 Vector Functions and Their Properties
  • 1.3 Stability of Dynamical Systems
  • 1.4 Dynamical Systems Affine in the Control
  • 1.4.1 Exact Input-Output Linearization by Static-State Feedback and the Notion of Zero Dynamics
  • 1.5 Geometric, Topological, and Invariance Set Properties
  • 1.6 Conclusion
  • References
  • Chapter 2: Adaptive Control: An Overview
  • 2.1 Introduction
  • 2.2 Adaptive Control Problem Formulation
  • 2.3 Model-Based Adaptive Control
  • 2.3.1 Direct Model-Based Adaptive Control
  • 2.3.1.1 Linear Direct Model-Based Adaptive Control
  • 2.3.1.2 Nonlinear Direct Model-Based Adaptive Control
  • 2.3.2 Indirect Model-Based Adaptive Control
  • 2.3.2.1 Linear Indirect Model-Based Adaptive Control
  • 2.3.2.2 Nonlinear Indirect Model-Based Adaptive Control
  • 2.4 Model-Free Adaptive Control
  • 2.5 Learning-Based Adaptive Control
  • 2.6 Conclusion
  • References
  • Chapter 3: Extremum Seeking-Based Iterative Feedback Gains Tuning Theory
  • 3.1 Introduction
  • 3.2 Basic Notations and Definitions
  • 3.3 Problem Formulation
  • 3.3.1 Class of Systems
  • 3.3.2 Control Objectives
  • 3.4 Extremum Seeking-Based Iterative Gain Tuning for Input-Output Linearization Control
  • 3.4.1 Step One: Robust Control Design
  • 3.4.2 Step Two: Iterative Auto-Tuning of the Feedback Gains
  • 3.5 Mechatronics Examples
  • 3.5.1 Electromagnetic Actuators
  • 3.5.1.1 System Modeling
  • 3.5.1.2 Robust Controller
  • 3.5.1.3 Learning-Based Auto-Tuning of the Controller Gains
  • 3.5.1.4 Simulation Results
  • 3.5.2 Two-Link Rigid Manipulators
  • 3.5.2.1 System Modeling
  • 3.5.2.2 Robust Controller
  • 3.5.2.3 Learning-Based Auto-Tuning of the Feedback Gains
  • 3.5.2.4 Simulations Results
  • 3.6 Conclusion and Discussion of Open Problems
  • References
  • Chapter 4: Extremum Seeking-Based Indirect Adaptive Control
  • 4.1 Introduction
  • 4.2 Basic Notations and Definitions
  • 4.3 ES-Based Indirect Adaptive Controller for the Case of General Nonlinear Models With Constant Model Uncertainties
  • 4.4 ES-Based Indirect Adaptive Controller for General Nonlinear Models With Time-Varying Model Uncertainties
  • 4.5 The Case of Nonlinear Models Affine in the Control
  • 4.5.1 Control Objectives
  • 4.5.2 Adaptive Controller Design
  • 4.5.2.1 Nominal Controller
  • 4.5.2.2 Lyapunov Reconstruction-Based ISS Controller
  • 4.5.2.3 MES-Based Parametric Uncertainties Estimation
  • 4.6 Mechatronics Examples
  • 4.6.1 The Case of Electromagnetic Actuators
  • 4.6.1.1 Controller Design
  • 4.6.1.2 Numerical Results
  • 4.6.2 The Case of Two-Link Rigid Manipulators
  • 4.6.3 MES-Based Uncertainties Estimation
  • 4.7 Conclusion
  • References
  • Chapter 5: Extremum Seeking-Based Real-Time Parametric Identification for Nonlinear Systems
  • 5.1 Introduction
  • 5.2 Basic Notations and Definitions
  • 5.3 ES-Based Open-Loop Parametric Identification for Nonlinear Systems
  • 5.3.1 Problem Formulation
  • 5.3.2 Open-Loop Parameters Estimation
  • 5.4 ES-Based Closed-Loop Parametric Identification for Nonlinear Systems
  • 5.4.1 Problem Formulation
  • 5.4.2 Parametric Estimation in the Case of Nonlinear Systems Affine in the Control
  • 5.4.2.1 Case 1
  • 5.4.2.2 Case 2
  • 5.5 Identification and Stable PDEs' Model Reduction by ES
  • 5.5.1 ES-Based ROM Parameters' Identification
  • 5.5.1.1 POD Basis Functions
  • 5.5.1.2 MES-Based Open-Loop Parameters' Estimation for PDEs
  • 5.5.2 MES-Based PDEs' Stable Model Reduction
  • 5.5.2.1 Different Closure Models for ROM Stabilization
  • 5.5.2.2 MES-Based Closure Models' Auto-Tuning
  • 5.6 Application Examples
  • 5.6.1 Electromagnetic Actuator
  • 5.6.2 Robot Manipulator With Two Rigid Arms
  • 5.6.3 The Coupled Burgers' PDE
  • 5.6.3.1 Burgers' Equation ES-Based Parameters' Estimation
  • 5.6.3.2 Burgers' Equation ES-Based POD ROM Stabilization
  • 5.7 Conclusion and Open Problems
  • References
  • Chapter 6: Extremum Seeking-Based Iterative Learning Model Predictive Control (ESILC-MPC)
  • 6.1 Introduction
  • 6.2 Notation and Basic Definitions
  • 6.3 Problem Formulation
  • 6.3.1 Robust Positive Invariant Sets
  • 6.3.2 Tightening the Constrains
  • 6.3.3 Invariant Set for Tracking
  • 6.3.4 MPC Problem
  • 6.4 The DIRECT ES-Based Iterative Learning MPC
  • 6.4.1 DIRECT-Based Iterative Learning MPC
  • 6.4.2 Proof of the MPC ISS-Guarantee and the Learning Convergence
  • 6.5 Dither MES-Based Adaptive MPC
  • 6.5.1 Constrained Linear Nominal MPC
  • 6.5.2 MES-Based Adaptive MPC Algorithm
  • 6.5.3 Stability Discussion
  • 6.6 Numerical Examples
  • 6.6.1 Example for the DIRECT-Based ILC MPC
  • 6.6.2 Example for the Dither-Based ESILC-MPC
  • 6.7 Conclusion and Open Problems
  • References
  • Conclusions and Further Notes
  • References
  • Index
  • Back Cover

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