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E-grāmata: Practical Design and Application of Model Predictive Control: MPC for MATLAB(R) and Simulink(R) Users

(Assistant Professor, Mechanical Engineering, Prince Mohammad Bin Fahd University, KSA.), (Technical Advisor with KPIT Infosystems Inc, Columbus, IN, USA)
  • Formāts: PDF+DRM
  • Izdošanas datums: 04-May-2018
  • Izdevniecība: Butterworth-Heinemann Inc
  • Valoda: eng
  • ISBN-13: 9780128139196
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Practical Design and Application of Model Predictive Control: MPC for MATLAB(R) and Simulink(R) UsersPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 04-May-2018
  • Izdevniecība: Butterworth-Heinemann Inc
  • Valoda: eng
  • ISBN-13: 9780128139196
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Practical Design and Application of Model Predictive Control is a self-learning resource on how to design, tune and deploy an MPC using MATLAB® and Simulink®. This reference is one of the most detailed publications on how to design and tune MPC controllers. Examples presented range from double-Mass spring system, ship heading and speed control, robustness analysis through Monte-Carlo simulations, photovoltaic optimal control, and energy management of power-split and air-handling control. Readers will also learn how to embed the designed MPC controller in a real-time platform such as Arduino®.

The selected problems are nonlinear and challenging, and thus serve as an excellent experimental, dynamic system to show the reader the capability of MPC. The step-by-step solutions of the problems are thoroughly documented to allow the reader to easily replicate the results. Furthermore, the MATLAB® and Simulink® codes for the solutions are available for free download. Readers can connect with the authors through the dedicated website which includes additional free resources (www.practicalmpc.com).

  • Illustrates how to design, tune and deploy MPC for projects in a quick manner.
  • Demonstrates a variety of applications that are solved using MATLAB® and Simulink®.
  • Bridges the gap in providing a number of realistic problems with very hands-on training.
  • Provides MATLAB® and Simulink® code solutions. This includes nonlinear plant models that the reader can use for other projects and research work.
  • Presents application problems with solutions to help reinforce the information learned.
Preface xi
Acknowledgments xiii
1 Introducing the Book
1(4)
1.1 Introducing the Authors
1(1)
1.2 Practical Approach to MPC
1(2)
1.3 Organization of the Book
3(1)
1.4 Software and Hardware Requirements
4(1)
1.5 Downloading the Source Codes
4(1)
Reference
4(1)
Further Reading
4(1)
2 Theoretical Foundation of MPC
5(12)
2.1 Introduction
5(1)
2.2 PID or MPC
5(1)
2.3 Hypothetical PID With a Prediction Horizon
6(4)
2.4 Hypothetical PID With a Prediction and Control Horizon
10(1)
2.5 Introduction to MPC
11(4)
2.6 Solving the Real-Time Optimization Problem in MPC
15(1)
2.7 Mathworks and MPC
15(2)
Reference
16(1)
Further Reading
16(1)
3 MPC Design of a Double-Mass Spring System
17(36)
3.1 Introduction
17(1)
3.2 Model-Based Design Framework
18(1)
3.3 System Identification Process
18(6)
3.4 Double-Mass Spring System
24(8)
3.5 System Identification for a Double-Mass Spring Plant
32(2)
3.6 MPC Control Design
34(10)
3.7 Integrating MPC With Simulink Model
44(6)
3.8 Application Problem
50(3)
References
51(2)
4 System Identification for a Ship
53(12)
4.1 Introduction
53(1)
4.2 Plant Model of a Ship
54(2)
4.3 Data-Based Linear Approximation of the Ship's Dynamics
56(6)
4.4 Application Problem: System Identification of Ship Dynamics
62(3)
References
63(2)
5 Single MPC Design for a Ship
65(32)
5.1 Introduction
65(1)
5.2 Understanding the Requirements for the Controller
66(1)
5.3 Requirements for the Ship Controller
67(2)
5.4 Physical Constraints of the Ship
69(1)
5.5 Handling Constraints in MPC
69(1)
5.6 Designing a MPC Controller for the Ship Using MATLAB
70(18)
5.7 Integrating MPC With Simulink Model
88(6)
5.8 Application Problem: Impact of Tuning on Robustness
94(3)
References
95(2)
6 Multiple MPC Design for a Ship
97(36)
6.1 Introduction
97(1)
6.2 Defining the Operating Regions for the System
98(1)
6.3 Steady State Simulations for the Operating Points
99(2)
6.4 Analysis of Steady State Simulations
101(3)
6.5 Creating Linear Models for the Entire Operating Space
104(10)
6.6 Designing a Multimode MPC
114(3)
6.7 Simulink Model for Multiple MPC
117(6)
6.8 Multiple MPC Controller Simulation Results
123(7)
6.9 Application Problem
130(3)
References
131(2)
7 Monte-Carlo Simulations and Robustness Analysis for a Multiple MPC of a Ship
133(8)
7.1 Introduction
133(1)
7.2 Introducing Uncertainties in Weather Conditions
133(2)
7.3 Monte-Carlo Simulations Process
135(1)
7.4 Monte-Carlo Simulation Results for Original MPC Tune
136(1)
7.5 Impact of Tuning on Robustness of MPC
137(2)
7.6 Application Problem
139(2)
Reference
139(2)
8 MPC Design for Photovoltaic Cells
141(40)
8.1 Introduction
141(1)
8.2 Introducing the Photovoltaic Thermoelectrical Model
142(2)
8.3 Controller Reference Generation
144(3)
8.4 System Identification for Photovoltaic Module
147(3)
8.5 Physical Constraints of the System
150(1)
8.6 Designing a MPC Controller for the PV Module
151(17)
8.7 Integrating MPC With the Simulink Model
168(6)
8.8 Controller Performance
174(7)
References
180(1)
9 Real Time Embedded Target Application of MPC
181(40)
9.1 Introduction
181(1)
9.2 Control Problem
181(1)
9.3 Hardware Requirements and Familiarization
182(1)
9.4 Simulink Support Package for Arduino
183(2)
9.5 Hardware Setup for the DC Motor Control
185(3)
9.6 Data Collection for Response Curve Generation
188(7)
9.7 Analyzing System Nonlinearity
195(3)
9.8 System Identification
198(3)
9.9 MPC Controller Design
201(8)
9.10 Integrating MPC Controllers With Simulink Model
209(6)
9.11 Multimode MPC Controller Deployment on the Hardware
215(1)
9.12 Single MPC Controller Deployment on the Hardware
216(3)
9.13 Application Problem
219(2)
References
219(2)
10 MPC Design for Air-Handling Control of a Diesel Engine
221(20)
10.1 Introduction
221(1)
10.2 Air-Handling Control Survey
221(3)
10.3 Engine Architecture
224(1)
10.4 Air-Handling Architecture
224(2)
10.5 Torque Curve and Duty Cycle
226(1)
10.6 System Identification
226(2)
10.7 MPC Controller Structure
228(2)
10.8 Controller Deployment
230(1)
10.9 Experimental Results
231(4)
10.10 Robustness Analysis
235(2)
10.11 Summary
237(4)
References
238(3)
Index 241
Dr. Khaled has extensive industrial and academic experience in the field of dynamics, controls and IoT solutions. He is currently an Assistant professor in Prince Mohammad Bin Fahd University. He is an innovator with more than 30 patents and patent applications in the fields of smart systems and energy. He is the author of "Practical Design and Application of Model Predictive Control". He also has numerous publications in the field of controls and autonomous navigation. Dr. Khaled is a green-belt six sigma certified. He received the status of "Outstanding Researcher" granted by the U.S Government in 2012. Bibin has a Master of Science in Mechanical engineering and 12 years of industrial experience in the field of Controls Design, Software Development and Rapid Prototyping. He is currently working as a Technical Advisor with KPIT Technologies Inc, USA. Bibin has worked on vehicle, aftertreatment, air-handling and engine modelling and controls and on board diagnostic development. He is an expert in Matlab and Simulink as well as Hardware and Software solutions for the control of vehicle and powertrain systems. He has 7 patents and several patent applications and published 5 journal and conference papers. Bibin is the co-author of "Practical Design and Application of Model Predictive Control".
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