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PID and Predictive Control of Electrical Drives and Power Converters using MATLAB / Simulink [Kõva köide]

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  • Formaat: Hardback, 360 pages, kõrgus x laius x paksus: 250x175x23 mm, kaal: 708 g
  • Sari: IEEE Press
  • Ilmumisaeg: 24-Dec-2014
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 1118339444
  • ISBN-13: 9781118339442
Teised raamatud teemal:
PID and Predictive Control of Electrical Drives and Power Converters using MATLAB / SimulinkSuurem pilt
  • Formaat: Hardback, 360 pages, kõrgus x laius x paksus: 250x175x23 mm, kaal: 708 g
  • Sari: IEEE Press
  • Ilmumisaeg: 24-Dec-2014
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 1118339444
  • ISBN-13: 9781118339442
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781118339442.html
Märksõnad:

A timely introduction to current research on PID and predictive control by one of the leading authors on the subject

PID and Predictive Control of Electric Drives and Power Supplies using MATLAB/Simulink examines the classical control system strategies, such as PID control, feed-forward control and cascade control, which are widely used in current practice. The authors share their experiences in actual design and implementation of the control systems on laboratory test-beds, taking the reader from the fundamentals through to more sophisticated design and analysis. The book contains sections on closed-loop performance analysis in both frequency domain and time domain, presented to help the designer in selection of controller parameters and validation of the control system. Continuous-time model predictive control systems are designed for the drives and power supplies, and operational constraints are imposed in the design. Discrete-time model predictive control systems are designed based on the discretization of the physical models, which will appeal to readers who are more familiar with sampled-data control system. Soft sensors and observers will be discussed for low cost implementation. Resonant control of the electric drives and power supply will be discussed to deal with the problems of bias in sensors and unbalanced three phase AC currents.

  • Brings together both classical control systems and predictive control systems in a logical style from introductory through to advanced levels
  • Demonstrates how simulation and experimental results are used to support theoretical analysis and the proposed design algorithms
  • MATLAB and Simulink tutorials are given in each chapter to show the readers how to take the theory to applications.
  • Includes MATLAB and Simulink software using xPC Target for teaching purposes
  • A companion website is available

Researchers and industrial engineers; and graduate students on electrical engineering courses will find this a valuable resource.

About the Authors xiii
Preface xv
Acknowledgment xix
List of Symbols and Acronyms xxi
1 Modeling of AC Drives and Power Converter 1(26)
1.1 Space Phasor Representation
1(4)
1.1.1 Space Vector for Magnetic Motive Force
1(3)
1.1.2 Space Vector Representation of Voltage Equation
4(1)
1.2 Model of Surface Mounted PMSM
5(5)
1.2.1 Representation in Stationary Reference (α — β) Frame
5(2)
1.2.2 Representation in Synchronous Reference (d — q) Frame
7(1)
1.2.3 Electromagnetic Torque
8(2)
1.3 Model of Interior Magnets PMSM
10(1)
1.3.1 Complete Model of PMSM
11(1)
1.4 Per Unit Model and PMSM Parameters
11(2)
1.4.1 Per Unit Model and Physical Parameters
11(1)
1.4.2 Experimental Validation of PMSM Model
12(1)
1.5 Modeling of Induction Motor
13(8)
1.5.1 Space Vector Representation of Voltage Equation of Induction Motor
13(4)
1.5.2 Representation in Stationary α — β Reference Frame
17(1)
1.5.3 Representation in d — q Reference Frame
17(2)
1.5.4 Electromagnetic Torque of Induction Motor
19(1)
1.5.5 Model Parameters of Induction Motor and Model Validation
19(2)
1.6 Modeling of Power Converter
21(4)
1.6.1 Space Vector Representation of Voltage Equation for Power Converter
22(1)
1.6.2 Representation in α — β Reference Frame
22(1)
1.6.3 Representation in d — q Reference Frame
23(1)
1.6.4 Energy Balance Equation
24(1)
1.7 Summary
25(1)
1.8 Further Reading
25(1)
References
25(2)
2 Control of Semiconductor Switches via PWM Technologies 27(14)
2.1 Topology of IGBT Inverter
28(2)
2.2 Six-step Operating Mode
30(1)
2.3 Carrier Based PWM
31(4)
2.3.1 Sinusoidal PWM
31(1)
2.3.2 Carrier Based PWM with Zero-sequence Injection
32(3)
2.4 Space Vector PWM
35(2)
2.5 Simulation Study of the Effect of PWM
37(3)
2.6 Summary
40(1)
2.7 Further Reading
40(1)
References
40(1)
3 PID Control System Design for Electrical Drives and Power Converters 41(46)
3.1 Overview of PID Control Systems Using Pole-assignment Design Techniques
42(7)
3.1.1 PI Controller Design
42(1)
3.1.2 Selecting the Desired Closed-loop Performance
43(2)
3.1.3 Overshoot in Reference Response
45(1)
3.1.4 PID Controller Design
46(2)
3.1.5 Cascade PID Control Systems
48(1)
3.2 Overview of PID Control of PMSM
49(3)
3.2.1 Bridging the Sensor Measurements to Feedback Signals (See the lower part of Figure 3.6)
50(1)
3.2.2 Bridging the Control Signals to the Inputs to the PMSM (See the top part of Figure 3.6)
51(1)
3.3 PI Controller Design for Torque Control of PMSM
52(3)
3.3.1 Set-point Signals to the Current Control Loops
52(1)
3.3.2 Decoupling of the Current Control Systems
53(1)
3.3.3 PI Current Controller Design
54(1)
3.4 Velocity Control of PMSM
55(9)
3.4.1 Inner-loop Proportional Control of q-axis Current
55(2)
3.4.2 Cascade Feedback Control of Velocity:P Plus PI
57(2)
3.4.3 Simulation Example for P Plus PI Control System
59(2)
3.4.4 Cascade Feedback Control of Velocity: PI Plus PI
61(2)
3.4.5 Simulation Example for PI Plus PI Control System
63(1)
3.5 PID Controller Design for Position Control of PMSM
64(1)
3.6 Overview of PID Control of Induction Motor
65(3)
3.6.1 Bridging the Sensor Measurements to Feedback Signals
67(1)
3.6.2 Bridging the Control Signals to the Inputs to the Induction Motor
67(1)
3.7 PID Controller Design for Induction Motor
68(6)
3.7.1 PI Control of Electromagnetic Torque of Induction Motor
68(2)
3.7.2 Cascade Control of Velocity and Position
70(3)
3.7.3 Slip Estimation
73(1)
3.8 Overview of PID Control of Power Converter
74(2)
3.8.1 Bridging Sensor Measurements to Feedback Signals
75(1)
3.8.2 Bridging the Control Signals to the Inputs of the Power Converter
76(1)
3.9 PI Current and Voltage Controller Design for Power Converter
76(6)
3.9.1 P Control of d-axis Current
76(1)
3.9.2 PI Control of q-axis Current
77(2)
3.9.3 PI Cascade Control of Output Voltage
79(1)
3.9.4 Simulation Example
80(1)
3.9.5 Phase Locked Loop
80(2)
3.10 Summary
82(1)
3.11 Further Reading
83(1)
References
83(4)
4 PID Control System Implementation 87(18)
4.1 P and PI Controller Implementation in Current Control Systems
87(6)
4.1.1 Voltage Operational Limits in Current Control Systems
87(3)
4.1.2 Discretization of Current Controllers
90(2)
4.1.3 Anti-windup Mechanisms
92(1)
4.2 Implementation of Current Controllers for PMSM
93(2)
4.3 Implementation of Current Controllers for Induction Motors
95(2)
4.3.1 Estimation of ωs and θs
95(1)
4.3.2 Estimation of φrd
96(1)
4.3.3 The Implementation Steps
97(1)
4.4 Current Controller Implementation for Power Converter
97(1)
4.4.1 Constraints on the Control Variables
97(1)
4.5 Implementation of Outer-loop PI Control System
98(2)
4.5.1 Constraints in the Outer-loop
98(1)
4.5.2 Over Current Protection for AC Machines
99(1)
4.5.3 Implementation of Outer-loop PI Control of Velocity
100(1)
4.5.4 Over Current Protection for Power Converters
100(1)
4.6 MATLAB Tutorial on Implementation of PI Controller
100(2)
4.7 Summary
102(1)
4.8 Further Reading
103(1)
References
103(2)
5 Tuning PID Control Systems with Experimental Validations 105(66)
5.1 Sensitivity Functions in Feedback Control Systems
105(6)
5.1.1 Two-degrees of Freedom Control System Structure
105(4)
5.1.2 Sensitivity Functions
109(1)
5.1.3 Disturbance Rejection and Noise Attenuation
110(1)
5.2 Tuning Current-loop q-axis Proportional Controller (PMSM)
111(12)
5.2.1 Performance Factor and Proportional Gain
112(1)
5.2.2 Complementary Sensitivity Function
112(2)
5.2.3 Sensitivity and Input Sensitivity Functions
114(1)
5.2.4 Effect of PWM Noise on Current Proportional Control System
114(2)
5.2.5 Effect of Current Sensor Noise and Bias
116(2)
5.2.6 Experimental Case Study of Current Sensor Bias Using P Control
118(1)
5.2.7 Experimental Case Study of Current Loop Noise
119(4)
5.3 Tuning Current-loop PI Controller (PMSM)
123(5)
5.3.1 PI Controller Parameters in Relation to Performance Parameter γ
123(1)
5.3.2 Sensitivity in Relation to Performance Parameter γ
124(2)
5.3.3 Effect of PWM Error in Relation to γ
126(1)
5.3.4 Experimental Case Study of Current Loop Noise Using PI Control
126(2)
5.4 Performance Robustness in Outer-loop Controllers
128(8)
5.4.1 Sensitivity Functions for Outer-loop Control System
131(4)
5.4.2 Input Sensitivity Functions for the Outer-loop System
135(1)
5.5 Analysis of Time-delay Effects
136(2)
5.5.1 PI Control of q-axis Current
137(1)
5.5.2 P Control of q-axis Current
137(1)
5.6 Tuning Cascade PI Control Systems for Induction Motor
138(9)
5.6.1 Robustness of Cascade PI Control System
140(3)
5.6.2 Robustness Study Using Nyquist Plot
143(4)
5.7 Tuning PI Control Systems for Power Converter
147(10)
5.7.1 Overview of the Designs
147(2)
5.7.2 Tuning the Current Controllers
149(1)
5.7.3 Tuning Voltage Controller
150(4)
5.7.4 Experimental Evaluations
154(3)
5.8 Tuning P Plus PI Controllers for Power Converter
157(2)
5.8.1 Design and Sensitivity Functions
157(1)
5.8.2 Experimental Results
158(1)
5.9 Robustness of Power Converter Control System Using PI Current Controllers
159(8)
5.9.1 Variation of Inductance Using PI Current Controllers
160(3)
5.9.2 Variation of Capacitance on Closed-loop Performance
163(4)
5.10 Summary
167(2)
5.10.1 Current Controllers
167(1)
5.10.2 Velocity, Position and Voltage Controllers
168(1)
5.10.3 Choice between P Current Control and PI Current Control
169(1)
5.11 Further Reading
169(1)
References
169(2)
6 FCS Predictive Control in d — q Reference Frame 171(66)
6.1 States of IGBT Inverter and the Operational Constraints
172(3)
6.2 FCS Predictive Control of PMSM
175(2)
6.3 MATLAB Tutorial on Real-time Implementation of FCS-MPC
177(5)
6.3.1 Simulation Results
179(2)
6.3.2 Experimental Results of FCS Control
181(1)
6.4 Analysis of FCS-MPC System
182(5)
6.4.1 Optimal Control System
182(2)
6.4.2 Feedback Controller Gain
184(1)
6.4.3 Constrained Optimal Control
185(2)
6.5 Overview of FCS-MPC with Integral Action
187(4)
6.6 Derivation of I-FCS Predictive Control Algorithm
191(6)
6.6.1 Optimal Control without Constraints
191(3)
6.6.2 I-FCS Predictive Controller with Constraints
194(2)
6.6.3 Implementation of I-FCS-MPC Algorithm
196(1)
6.7 MATLAB Tutorial on Implementation of I-FCS Predictive Controller
197(4)
6.7.1 Simulation Results
198(3)
6.8 I-FCS Predictive Control of Induction Motor
201(8)
6.8.1 The Control Algorithm for an Induction Motor
202(2)
6.8.2 Simulation Results
204(1)
6.8.3 Experimental Results
205(4)
6.9 I-FCS Predictive Control of Power Converter
209(6)
6.9.1 I-FCS Predictive Control of a Power Converter
209(2)
6.9.2 Simulation Results
211(3)
6.9.3 Experimental Results
214(1)
6.10 Evaluation of Robustness of I-FCS-MPC via Monte-Carlo Simulations
215(3)
6.10.1 Discussion on Mean Square Errors
216(2)
6.11 Velocity and Position Control of PMSM Using I-FCS-MPC
218(6)
6.11.1 Choice of Sampling Rate for the Outer-loop Control System
219(4)
6.11.2 Velocity and Position Controller Design
223(1)
6.12 Velocity and Position Control of Induction Motor Using I-FCS-MPC
224(8)
6.12.1 I-FCS Cascade Velocity Control of Induction Motor
225(1)
6.12.2 I-FCS-MPC Cascade Position Control of Induction Motor
226(2)
6.12.3 Experimental Evaluation of Velocity Control
228(4)
6.13 Summary
232(2)
6.13.1 Selection of sampling interval Δt
233(1)
6.13.2 Selection of the Integral Gain
233(1)
6.14 Further Reading
234(1)
References
234(3)
7 FCS Predictive Control in α — β Reference Frame 237(28)
7.1 FCS Predictive Current Control of PMSM
237(4)
7.1.1 Predictive Control Using One-step-ahead Prediction
238(1)
7.1.2 FCS Current Control in α — β Reference Frame
239(1)
7.1.3 Generating Current Reference Signals in α — β Frame
240(1)
7.2 Resonant FCS Predictive Current Control
241(6)
7.2.1 Control System Configuration
241(1)
7.2.2 Outer-loop Controller Design
242(1)
7.2.3 Resonant FCS Predictive Control System
243(4)
7.3 Resonant FCS Current Control of Induction Motor
247(8)
7.3.1 The Original FCS Current Control of Induction Motor
247(3)
7.3.2 Resonant FCS Predictive Current Control of Induction Motor
250(2)
7.3.3 Experimental Evaluations of Resonant FCS Predictive Control
252(3)
7.4 Resonant FCS Predictive Power Converter Control
255(6)
7.4.1 FCS Predictive Current Control of Power Converter
255(5)
7.4.2 Experimental Results of Resonant FCS Predictive Control
260(1)
7.5 Summary
261(1)
7.6 Further Reading
262(1)
References
262(3)
8 Discrete-time Model Predictive Control (DMPC) of Electrical Drives and Power Converter 265(20)
8.1 Linear Discrete-time Model for PMSM
266(2)
8.1.1 Linear Model for PMSM
266(1)
8.1.2 Discretization of the Continuous-time Model
267(1)
8.2 Discrete-time MPC Design with Constraints
268(6)
8.2.1 Augmented Model
269(1)
8.2.2 Design without Constraints
270(2)
8.2.3 Formulation of the Constraints
272(1)
8.2.4 On-line Solution for Constrained MPC
272(2)
8.3 Experimental Evaluation of DMPC of PMSM
274(6)
8.3.1 The MPC Parameters
274(1)
8.3.2 Constraints
275(1)
8.3.3 Response to Load Disturbances
275(2)
8.3.4 Response to a Staircase Reference
277(1)
8.3.5 Tuning of the MPC controller
278(2)
8.4 Power Converter Control Using DMPC with Experimental Validation
280(1)
8.5 Summary
281(1)
8.6 -Further Reading
282(1)
References
283(2)
9 Continuous-time Model Predictive Control (CMPC) of Electrical Drives and Power Converter 285(30)
9.1 Continuous-time MPC Design
286(8)
9.1.1 Augmented Model
286(1)
9.1.2 Description of the Control Trajectories Using Laguerre Functions
287(2)
9.1.3 Continuous-time Predictive Control without Constraints
289(3)
9.1.4 Tuning of CMPC Control System Using Exponential Data Weighting and Prescribed Degree of Stability
292(2)
9.2 CMPC with Nonlinear Constraints
294(4)
9.2.1 Approximation of Nonlinear Constraint Using Four Linear Constraints
294(1)
9.2.2 Approximation of Nonlinear Constraint Using Sixteen Linear Constraints
294(3)
9.2.3 State Feedback Observer
297(1)
9.3 Simulation and Experimental Evaluation of CMPC of Induction Motor
298(3)
9.3.1 Simulation Results
298(2)
9.3.2 Experimental Results
300(1)
9.4 Continuous-time Model Predictive Control of Power Converter
301(4)
9.4.1 Use of Prescribed Degree of Stability in the Design
302(1)
9.4.2 Experimental Results for Rectification Mode
303(1)
9.4.3 Experimental Results for Regeneration Mode
303(1)
9.4.4 Experimental Results for Disturbance Rejection
304(1)
9.5 Gain Scheduled Predictive Controller
305(4)
9.5.1 The Weighting Parameters
305(2)
9.5.2 Gain Scheduled Predictive Control Law
307(2)
9.6 Experimental Results of Gain Scheduled Predictive Control of Induction Motor
309(3)
9.6.1 The First Set of Experimental Results
309(2)
9.6.2 The Second Set of Experimental Results
311(1)
9.6.3 The Third Set of Experimental Results
312(1)
9.7 Summary
312(1)
9.8 Further Reading
313(1)
References
313(2)
10 MATLAB®/Simulink® Tutorials on Physical Modeling and Test-bed Setup 315(24)
10.1 Building Embedded Functions for Park-Clarke Transformation
315(3)
10.1.1 Park-Clarke Transformation for Current Measurements
316(1)
10.1.2 Inverse Park-Clarke Transformation for Voltage Actuation
317(1)
10.2 Building Simulation Model for PMSM
318(2)
10.3 Building Simulation Model for Induction Motor
320(5)
10.4 Building Simulation Model for Power Converter
325(7)
10.4.1 Embedded MATLAB Function for Phase Locked Loop (PLL)
325(3)
10.4.2 Physical Simulation Model for Grid Connected Voltage Source Converter
328(4)
10.5 PMSM Experimental Setup
332(2)
10.6 Induction Motor Experimental Setup
334(1)
10.6.1 Controller
334(1)
10.6.2 Power Supply
334(1)
10.6.3 Inverter
335(1)
10.6.4 Mechanical Load
335(1)
10.6.5 Induction Motor and Sensors
335(1)
10.7 Grid Connected Power Converter Experimental Setup
335(2)
10.7.1 Controller
335(1)
10.7.2 Inverter
336(1)
10.7.3 Sensors
336(1)
10.8 Summary
337(1)
10.9 Further Reading
337(1)
References
337(2)
Index 339
Liuping Wang is Professor of Control Engineering at RMIT University, Melbourne, Australia. She has been working on PID control systems and system identification for over 20 years and, together with her research group, Professor Wang has generated the research outcomes that have significantly improved the performance of computer numerical control (CNC) machines, leading to a new understanding of electric motor control and regenerative power supplies.  She has published numerous articles on the subject.

Shan Chai, Dae Yoo, Lu Gan and Ki Ng are PhD students working under the supervision of Professor Wang and are part of the research team that has produced, and is producing, new approaches and new understanding of the electrical motor control and the control of regenerative power supplies.