内容简介：　　

　 随机衰落通信是一种典型的数据传输损耗现象。本书系统深入地研究了在随机衰落通信环境下的随机迭代学习控制问题，建立了系统化的设计与分析框架。

　 全书共包括三个部分。第一部分是针对随机衰落通信变量统计性质已知的情形，第二部分讨论了统计性质未知的情形，第三部分则进一步拓展到其他多类系统，包括多智能体系统、点对点跟踪系统、多传感器系统等。通过这些探索，给出了随机衰落通信对学习控制性能影响的明确刻画，建立了学习控制算法的设计与分析框架，发展了一系列处理相关随机估计问题的方法技术，为网络系统实现高精度跟踪提供了迭代学习控制方案。

　 本书可作为智能科学与技术、计算机科学与技术、控制科学与工程、系统科学、电气工程、电子科学与技术等相关学科领域的科研工作者、工程技术人员和高等院校师生的参考书，也可作为研究生和高年级本科生的教科书。

目录：
Preface xiii
Acknowledgments xv
Author Bios xvii

CHAPTER 1 ■ Introduction
　　1.1 Essence of Learning Control
　　1.2 Iterative Learning Control
　　1.3 Brief Formulation of Discrete-Time ILC
　　1.4 Fading Channels and Related Control Issues
　　1.5 Structure of This Monograph

SECTION I Known Channel Statistics
CHAPTER 2 ■ Learning Control over Random Fading Channel
　　2.1 Introduction
　　2.2 Problem Formulation
　　2.3 Output Fading Channel Case
　　2.4 Input Fading Channel Case
　　2.5 Illustrative Simulations
　　　　2.5.1 MIMO System
　　　　2.5.2 PMLM System
　　2.6 Summary
CHAPTER 3 ■ Tracking Performance Enhancement by Input Averaging
　　3.1 Introduction
　　3.2 Problem Formulation
　　3.3 Output Fading Case
　　　　3.3.1 Fading Effect Analysis
　　　　3.3.2 Learning Algorithm Design
　　　　3.3.3 Convergence Analysis
　　3.4 Input Fading Case
　　　　3.4.1 Fading Effect Analysis
　　　　3.4.2 Learning Algorithm Design
　　　　3.4.3 Convergence Analysis
　　3.5 Illustrative Simulations
　　　　3.5.1 MIMO Numerical Example
　　　　3.5.2 DC-Motor Example
　　3.6 Summary
CHAPTER 4 ■ Averaging Techniques for Balancing Learning and Tracking Abilities
　　4.1 Introduction
　　4.2 Problem Formulation
　　　　4.2.1 System Formulation
　　　　4.2.2 Fading Channel
　　　　4.2.3 Effect Analysis
　　　　4.2.4 Open Problems
　　4.3 Main Results
　　　　4.3.1 Averaging Techniques and Associated Learning Algorithms
　　　　4.3.2 MA-Based Learning Algorithm
　　　　4.3.3 GA-Based Learning Algorithm
　　　　4.3.4 FA-Based Learning Algorithm
　　　　4.3.5 Discussions
　　4.4 Proofs of Main Theorems
　　4.5 Discussions and Extensions
　　　　4.5.1 Plant Model
　　　　4.5.2 Varying System Responses, Disturbances, and Noise
　　　　4.5.3 General Fading Framework
　　4.6 Illustrative Simulations
　　　　4.6.1 MA-Based Scheme
　　　　4.6.2 GA-Based Scheme
　　　　4.6.3 FA-Based Scheme
　　　　4.6.4 Comparison between Three Schemes
　　4.7 Summary
　　
SECTION II Unknown Channel Statistics
CHAPTER 5 ■ Gradient Estimation Method for Unknown Fading Channels
　　5.1 Introduction
　　5.2 Problem Formulation
　　5.3 ILC Algorithms and Convergence Analysis
　　　　5.3.1 Algorithm Design
　　　　5.3.2 Convergence Analysis
　　　　5.3.3 Variants of the Algorithm with Constant Gains
　　5.4 Illustrative Simulations
　　5.5 Summary
CHAPTER 6 ■ Iterative Estimation Method for Unknown Fading Channels
　　6.1 Introduction
　　6.2 Problem Formulation
　　　　6.2.1 System Formulation
　　　　6.2.2 Fading Channel Formulation
　　　　6.2.3 Control Objective
　　　　6.2.4 Iterative Estimation of the Fading Gain
　　6.3 Learning Control for Output Fading Case
　　　　6.3.1 Fading Correction and Effect Analysis
　　　　6.3.2 Learning Algorithm Design
　　　　6.3.3 Convergence Analysis
　　6.4 Learning Control for Input Fading Case
　　　　6.4.1 Fading Estimation and Correction
　　　　6.4.2 Learning Algorithm Design
　　　　6.4.3 Convergence Analysis
　　　　6.4.4 A General Formulation
　　6.5 Illustrative Simulations
　　　　6.5.1 MIMO System
　　　　6.5.2 PMLM System
　　6.6 Summary
CHAPTER 7 ■ Learning-Tracking Framework under Unknown Nonrepetitive Channel Randomness
　　7.1 Introduction
　　7.2 Problem Formulation
　　　　7.2.1 Plant Model
　　　　7.2.2 Channel-Induced Randomness
　　　　7.2.3 Control Objective
　　7.3 Learning Control Scheme for Zero-Mean Additive Randomness Case
　　　　7.3.1 Estimation of Multiplicative Randomness
　　　　7.3.2 Channel Effect Analysis
　　　　7.3.3 Lifting Transformation
　　　　7.3.4 Algorithm Design and Analysis
　　7.4 Extensions to the Non-Zero-Mean Additive Randomness Case
　　　　7.4.1 Estimation of Channel Randomness
　　　　7.4.2 Channel Effect and Main Results
　　7.5 The Case of Consistent Estimation
　　　　7.5.1 Asymptotic Repetitiveness and Estimator
　　　　7.5.2 Channel Effect and Main Results
　　7.6 Illustrative Simulations
　　7.7 Summary

SECTION III Extensions of Systems and Problems
CHAPTER 8 ■ Learning Consensus with Faded Neighborhood Information
　　8.1 Introduction
　　8.2 Problem Formulation
　　8.3 Distributed Learning Consensus Scheme and Its Analysis
　　　　8.3.1 Distributed Learning Consensus Scheme
　　　　8.3.2 Performance Analysis
　　8.4 Extension to Nonlinear Systems
　　　　8.4.1 Problem Extensions and Learning Consensus Scheme
　　　　8.4.2 Performance Analysis
　　8.5 Simulations
　　　　8.5.1 Linear Agent Dynamics
　　　　8.5.2 Nonlinear Agent Dynamics
　　8.6 Summary
CHAPTER 9 ■ Point-to-Point Tracking with Fading Communications
　　9.1 Introduction
　　9.2 Problem Formulation
　　9.3 Learning Control Scheme and Its Analysis
　　　　9.3.1 Algorithm Design
　　　　9.3.2 Convergence Analysis for Case I
　　　　9.3.3 Convergence Analysis for Case II
　　9.4 Illustrative Simulations
　　　　9.4.1 Numerical Example
　　　　9.4.2 Injection Molding Process
　　9.5 Summary
CHAPTER 10 ■ Point-to-Point Tracking Using Reference Update Strategy
　　10.1 Introduction
　　10.2 Problem Formulation
　　10.3 Learning Control Scheme and Its Analysis for Output Fading
　　　　10.3.1 Fading Correction and Algorithm Design
　　　　10.3.2 Convergence Analysis for Output Fading Case
　　10.4 Learning Control Scheme and Its Analysis for Input Fading
　　　　10.4.1 Fading Correction and Analysis
　　　　10.4.2 Learning Control Scheme
　　　　10.4.3 Convergence Analysis
　　　　10.4.4 General Formulation
　　10.5 Illustrative Simulations
　　　　10.5.1 Numerical Example
　　　　10.5.2 Industrial SCARA Robot
　　10.6 Summary
CHAPTER 11 ■ Multi-Objective Learning Tracking with Faded Measurements
　　11.1 Introduction
　　11.2 Problem Formulation
　　　　11.2.1 Multi-Sensor Systems
　　　　11.2.2 Fading Communication
　　　　11.2.3 Control Objective
　　11.3 Solution to Multi-Objective Tracking Problem
　　　　11.3.1 Solution to Single-Sensor Tracking Problem
　　　　11.3.2 Solution to Multi-Sensor Tracking Problem
　　11.4 Learning Control Scheme Using Faded Measurements
　　　　11.4.1 Effect of Faded Measurements
　　　　11.4.2 Learning Algorithm with Constant Gain
　　　　11.4.3 Learning Algorithm with Decreasing Gain
　　　　11.4.4 Best Tracking Performance Evaluation
　　11.5 Illustrative Simulations
　　　　11.5.1 Constant Gain Case
　　　　11.5.2 Decreasing Gain Case
　　11.6 Summary
APPENDIX A ■ Technical Lemmas
　　Bibliography
　　Index