《基于MATLAB的控制与估计》（第5版）由八章组成，包括控制算法和设计。主要内容包括状态估计和数据平滑、RLS和卡尔曼滤波器状态估计、控制系统设计、自适应控制和PID。书中的概念已经使用MATLAB和Simulink实现。提供了可下载的MATLAB和Simulink文件。
适合人群：大学生、研究生、控制类工程研究人员。
Preface：本书基于作者大部分的工作经验，旨在概述或解释作者在职业生涯早期就掌握的知识。本书几乎没有理论，但有很多算法及其设计方法。全书共由八章组成。第1章为状态估计和数据平滑。本章包括Luenberger观测器、滤波器、卡尔曼滤波器、扩展卡尔曼滤波器、比例积分卡尔曼滤波器、
滤波器、无迹卡尔曼滤波器、滑模观测器、惯性测量单元估计、数据融合思想和零相位滤波器。之所以把这部分内容放在全书的开头，是因为这些内容是控制算法与传感器之间的必要接口。第2章描述了几种数据平滑方法。数据平滑是通过使用算法对随时间变化的采集数据中存在的随机变化或噪声进行去除。这使得重要的模式脱颖而出。第3章描述了用于故障检测的RLS和卡尔曼滤波器状态估计方法，并给出了一个示例。第4章为控制系统的设计，用以减轻扰动的影响，包括扰动调节控制、
、主动抗扰控制和谐波振荡控制。第5章介绍了几种自适应控制方法，包括模型参考自适应控制、L1自适应控制和无模型自适应控制。第6章介绍了几种比例积分微分(PID)控制算法的调节方法。PID控制器是最常用，因此也是最重要的一种控制算法。第7章描述了几种自适应和非自适应前馈控制技术。第8章给出了一些读者可能感兴趣的应用实例。展示了书中介绍的使用控制系统和估计方法的一些技术。
很高兴将文稿整理在一起，希望读者能从中发现一些价值。这些概念已在MATLAB*/ SIMULINK*中实现。生成绘图和表格的源码可以从Mathworks Central File Exchange 下载。任何意见或建议都可以通过邮件发送给作者。
按照惯例，所提供的代码和想法仅用于指导、比较和学习——并非实际应用。使用MATLAB*代码或思想的风险由读者自行承担。读者（工程师）有责任确保自己的设计符合容许标准和项目需求。
在作者长期整理本书文稿时爱妻所表现的耐心，作者深表感激。
第4版相较于第1版新增了四个章节，并对自适应控制一章进行了重组。此外，在第7章中新增了三个应用示例程序。
2017年6月
第5版包括第2章，该章介绍了用以去除噪声的数据平滑技术。此外，书中还给出了一些说明。
目录：
Contents
Preface
Chapter 1 State Estimation and Data Smoothing
Two State Observers
Two State Tracking Observers
Two State Tracking Observers With Tracking Suppression
Three State Observers
Multi-Step Ahead Predictor
Alpha-Beta-Gamma Filters
Noise Filtering
H-Infinity Filters
Extended Kalman Filters
Proportional-Integral Kalman Filters
Unscented Kalman Filters
Sliding Mode Observers
Inertial Measurement Unit State Estimation
Principal IMU and Position Equations
Three axis IMU Fusion Algorithm
Preliminary Alignment of the IMU
Approximate Calibration of the IMU by Gradient Descent
Fusion of Position, Velocity, and Acceleration
Zero/Minimum Phase Filters
Signal to Noise Ratio On-Line Estimation
References
Chapter 2 Data Smoothing
2 Pole Butterworth Filter
3 Pole Butterworth Filter
2 Pole Super Smoother
3 Pole Super Smoother
Laguerre Filter
Arnaud Legoux Moving Average (Alma)
Double Exponential Moving Average (DEMA)
Modified Fractal Adaptive Moving Average
http://www.stockspotter.com/Files/frama.pdf
Generalized DEMA
Hull Moving Average Filter
Kaufman's Adaptive Moving Average
Triple Exponential Moving Average (TEMA)
Triangular Moving Average (TMA)
Simple Moving Median
Error Incorporation Filter
Guppy Multiple Moving Average (GMMA)
Volatility Index Dynamic Average (VIDYA)
Adaptive Smoothing Filters – Adaptive RSI, KAMA, and MAMA
EMA + RSI ema Filters
Zero Lag EMA
Gaussian Filter
Chapter 3 Fault Parameter Estimation
Recursive Least Squares (RLS)
Kalman Filter
Continuous Stirred Tank Reactor Example
Filters Designed As Functions of Frequency
References
Chapter 4 Disturbance Handling Control Strategies
Low Pass Filter
Notch Filter
Nonlinear PID
Disturbance Accommodating Control (DAC)
Adaptive DAC
Disturbance Utilization Control (DUC)
Harmonic Cancellation
Repetitive Control
Integral-Error Feedback LQR
H Infinity Control
Internal Model Control
Internal Model Control - 2 DOF
Model Predictive Control
MPC Without Constraints
MPC With Constraints
MPC With Model Variations
ADRC (Active Disturbance Rejection Control)
Override Control
Actuator Nonlinearities
Cascade Control
Sliding Mode Control
Chapter 5 Adaptive Control
Model Reference Adaptive Control
Introduction to L1 Adaptive Control
L1 Adaptive Control Examples
L1 Adaptive Control Transfer Functions
L1 Adaptive Parameter Values
L1 Alternatives to Filter C
L1 Alternatives to the Gamma Integrator
Projection Operator
References
Active Control (FIR, IIR, Filtered-U, Optimal)
Extracting Signals Corrupted with Sinusoids by Active Control
Model Free Adaptive Control
Simple Adaptive Control
References
Chapter 6 PID Tuning Concepts
Plant Parameter Estimation
PID Tuning Using Phase Margin
PI/PID by Pole Placement - 1
PID Tuning Using Pole Placement - 2
PID Tuning Using Internal Model Control Ideas
PID Tuning Tables
Loop-Shaping Approach
Anti-Windup For SISO Controllers
Anti-Windup For Internal Model Controllers
References
PID Plus Lead/Lag Controller
References
Lead Lag Design From PID Tuning Rules
Chapter 7 Feedforward Control
Input Shaping
References
Path/Trajectory Planning
References
Adaptive Feedforward Control
References
Adaptive Feedforward Control with Frequency Estimation
References
Non - Adaptive Feedforward Control
References
Chapter 8 Applications
IMU (Inertial Measurement Unit) Model
Basic Equations
Stationary IMU
Circular Motion IMU
Satellite Attitude Control
Simple Robot Arm Control
Heat Exchanger Control
Phase Lock Loop Control
Bridge Crane Model L1 Adaptive Control
Two Cart System
Chaotic Nonlinear Systems
Continuous Tank Stirred Reactor in Series
Continuous Fermenter Control with DAC and L1
Field Oriented Control (FOC)
L1 Adaptive Control of Simple MIMO Systems
Control of Simple Two Link Robot
Control of Rotary Inverted Pendulum (RIP)
References
书中实现的源码：
