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State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials

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STATE FEEDBACK CONTROL AND KALMAN FILTERING WITH MATLAB/SIMULINK TUTORIALS

Discover the control engineering skills for state space control system design, simulation, and implementation

State space control system design is one of the core courses covered in engineering programs around the world. Applications of control engineering include things like autonomous vehicles, renewable energy, unmanned aerial vehicles, electrical machine control, and robotics, and as a result the field may be considered cutting-edge. The majority of textbooks on the subject, however, lack the key link between the theory and the applications of design methodology.

State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials provides a unique perspective by linking state space control systems to engineering applications. The book comprehensively delivers introductory topics in state space control systems through to advanced topics like sensor fusion and repetitive control systems. More, it explores beyond traditional approaches in state space control by having a heavy focus on important issues associated with control systems like disturbance rejection, reference tracking, control signal constraint, sensor fusion and more. The text sequentially presents continuous-time and discrete-time state space control systems, Kalman filter and its applications in sensor fusion.

State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials readers will also find:

  • MATLAB and Simulink tutorials in a step-by-step manner that enable the reader to master the control engineering skills for state space control system design and Kalman filter, simulation, and implementation
  • An accompanying website that includes MATLAB code
  • High-end illustrations and tables throughout the text to illustrate important points
  • Written by experts in the field of process control and state space control systems

State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials is an ideal resource for students from advanced undergraduate students to postgraduates, as well as industrial researchers and engineers in electrical, mechanical, chemical, and aerospace engineering.

Author(s): Liuping Wang, Robin Ping Guan
Publisher: Wiley-IEEE Press
Year: 2022

Language: English
Pages: 449
City: Hoboken

Cover
Title Page
Copyright
Contents
Author Biography
Preface
Acknowledgments
List of Symbols and Acronyms
About the Companion Website
Part I Continuous‐time State Feedback Control
Chapter 1 State Feedback Controller and Observer Design
1.1 Introduction
1.2 Motivation for Going Beyond PID Control
1.3 Basics in State Feedback Control
1.3.1 State Feedback Control
1.3.2 Controllability
1.3.3 Food for Thought
1.4 Pole‐assignment Controller
1.4.1 The Design Method
1.4.2 Similarity Transformation for Controller Design
1.4.3 MATLAB Tutorial on Pole‐assignment Controller
1.4.4 Food for Thought
1.5 Linear Quadratic Regulator (LQR) Design
1.5.1 Motivational Example
1.5.2 Linear Quadratic Regulator Design
1.5.3 Selection of Q and R Matrices
1.5.4 LQR with Prescribed Degree of Stability
1.5.5 Food for Thought
1.6 Observer Design
1.6.1 Motivational Example for Observer
1.6.2 Observer Design
1.6.3 Observability
1.6.4 Duality between Controller and Observer
1.6.5 Observer Implementation
1.6.6 Food for Thought
1.7 State Estimate Feedback Control System
1.7.1 State Estimate Feedback Control
1.7.2 Separation Principle
1.7.3 Food for Thought
1.8 Summary
1.9 Further Reading
Problems
Chapter 2 Practical Multivariable Controllers in Continuous‐time
2.1 Introduction
2.2 Practical Controller I: Integral Action via Controller Design
2.2.1 The Original Control Law
2.2.2 Integrator Windup Scenarios
2.2.3 Proposed Practical Multivariable Controller
2.2.4 Anti‐windup Implementation
2.2.5 MATLAB Tutorial on Design and Implementation
2.2.6 Application to Drum Boiler Control
2.2.7 Food for Thought
2.3 Practical Controller II: Integral Action via Observer Design
2.3.1 Integral Control via Disturbance Estimation
2.3.2 Anti‐windup Mechanism
2.3.3 MATLAB Tutorial on Design and Implementation
2.3.4 Application to Sugar Mill Control
2.3.5 Design for Systems with Known States
2.3.6 Food for Thought
2.4 Drive Train Control of a Wind Turbine
2.4.1 Modelling of Wind Turbine's Drive Train
2.4.2 Configuration of The Control System
2.4.3 Design Method I
2.4.4 Design Method II
2.4.5 MATLAB Tutorial on Design Method II
2.4.6 Food for Thought
2.5 Summary
2.6 Further Reading
Problems
Part II Discrete‐time State Feedback Control
Chapter 3 Introduction to Discrete‐time Systems
3.1 Introduction
3.2 Discretization of Continuous‐time Models
3.2.1 Sampling of a Continuous‐time Model
3.2.2 Stability of Discrete‐time System
3.2.3 Examples of Discrete‐time Models from Sampling
3.2.4 Food for Thoughts
3.3 Input and Output Discrete‐time Models
3.3.1 Input and Output Models
3.3.2 Finite Impulse Response and Step Response Models
3.3.3 Non‐minimal State Space Realization
3.3.4 Food for Thought
3.4 z‐Transforms
3.4.1 z‐Transforms for Commonly Used Signals
3.4.2 z‐Transfer Functions
3.4.3 Food for Thought
3.5 Summary
3.6 Further Reading
Problems
Chapter 4 Discrete‐time State Feedback Control
4.1 Introduction
4.2 Discrete‐time State Feedback Control
4.2.1 Basic Ideas
4.2.2 Controllability in Discrete‐time
4.2.3 Food for Thought
4.3 Discrete‐time Observer Design
4.3.1 Basic Ideas about Discrete‐time Observer
4.3.2 Observability in Discrete‐time
4.3.3 Food for Thought
4.4 Discrete‐time Linear Quadratic Regulator (DLQR)
4.4.1 Objective Function for DLQR
4.4.2 Optimal Solution
4.4.3 Observer Design using DLQR
4.4.4 Food for Thought
4.5 Discrete‐time LQR with Prescribed Degree of Stability
4.5.1 Basic Ideas about a Prescribed Degree of Stability
4.5.2 Case Studies
4.5.3 Food for Thought
4.6 Summary
4.7 Further Reading
Problems
Chapter 5 Disturbance Rejection and Reference Tracking via Observer Design
5.1 Introduction
5.2 Disturbance Models
5.2.1 Commonly Encountered Disturbance Signals
5.2.2 State Space Model with Input Disturbance
5.2.3 Food for Thought
5.3 Compensation of Input and Output Disturbances in Estimation
5.3.1 Motivational Example
5.3.2 Input Disturbance Observer Design
5.3.3 MATLAB Tutorial for Augmented State Space Model
5.3.4 The Observer Error System
5.3.5 Output Disturbance Observer Design
5.3.6 Food for Thought
5.4 Disturbance‐Observer‐based State Feedback Control
5.4.1 The Control Law
5.4.2 MATLAB Tutorial for Control Implementation
5.4.3 Food for Thought
5.5 Analysis of Disturbance‐Observer‐based Control System
5.5.1 Controller Transfer Function
5.5.2 Disturbance Rejection
5.5.3 Reference Tracking
5.5.4 A Case Study
5.5.5 Food for Thought
5.6 Anti‐windup Implementation of the Control Law
5.6.1 Algorithm for Anti‐windup Implementation
5.6.2 Heating Furnace Control
5.6.3 Example for Bandlimited Disturbance
5.6.4 Food for Thought
5.7 Summary
5.8 Further Reading
Problems
Chapter 6 Disturbance Rejection and Reference Tracking via Control Design
6.1 Introduction
6.2 Embedding Disturbance Model into Controller Design
6.2.1 Formulation of Augmented State Space Model
6.2.2 MATLAB Tutorial
6.2.3 Controllability and Observability
6.2.4 Food for Thought
6.3 Controller and Observer Design
6.3.1 Controller Design and Control Signal Calculation
6.3.2 Adding Reference Signal
6.3.3 Observer Design and Implementation
6.3.4 MATLAB Tutorial for Control Implementation
6.3.5 Food for Thought
6.4 Practical Issues
6.4.1 Reducing Overshoot in Reference Tracking
6.4.2 Anti‐windup Implementation
6.4.3 Control System using NMSS Realization
6.4.4 Food for Thought
6.5 Repetitive Control
6.5.1 Basic Ideas about Repetitive Control
6.5.2 Determining the Disturbance Model D(z)
6.5.3 Robotic Arm Control
6.5.4 Food for Thought
6.6 Summary
6.7 Further Reading
Problems
Part III Kalman Filtering
Chapter 7 The Kalman Filter
7.1 Introduction
7.2 The Kalman Filter Algorithm
7.2.1 State Space Models in the Kalman Filter
7.2.2 An Intuitive Computational Procedure
7.2.3 Optimization of Kalman Filter Gain
7.2.4 Kalman Filter Examples with MATLAB Tutorials
7.2.5 Compensation of Sensor Bias and Load Disturbance
7.2.6 Food for Thought
7.3 The Kalman Filter in Multi‐rate Sampling Environment
7.3.1 KF Algorithm for Missing Data Scenarios
7.3.2 Case Studies with MATLAB Tutorial
7.3.3 Food for Thought
7.4 The Extended Kalman Filter (EKF)
7.4.1 Linearization in Extended Kalman Filter
7.4.2 The Extended Kalman Filter Algorithm
7.4.3 Case Studies with MATLAB Tutorial
7.4.4 Food for Thought
7.5 The Kalman Filter with Fading Memory
7.5.1 The Algorithm for KF with Fading Memory
7.5.2 Food for Thought
7.6 Relationship between Kalman Filter and Observer
7.6.1 One‐step Kalman Filter Algorithm
7.6.2 Kalman Filter and Observer
7.6.3 Food for Thought
7.7 Summary
7.8 Further Reading
Problems
Chapter 8 Addressing Computational Issues in KF
8.1 Introduction
8.2 The Sequential Kalman Filter
8.2.1 Basic Ideas about Sequential Kalman Filter
8.2.2 Non‐diagonal R
8.2.3 MATLAB Tutorial for Sequential Kalman Filter
8.2.4 Food for Thought
8.3 The Kalman Filter using UDUT Factorization
8.3.1 Gram‐Schmidt Orthogonalization Procedure
8.3.2 Basic Ideas
8.3.3 Sequential Kalman Filter with UDUT Decomposition
8.3.4 MATLAB Tutorial
8.3.5 Food for Thought
8.4 Summary
8.5 Further Reading
Problems
Bibliography
Index
EULA