Modern predictive control:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
CRC Press
2010
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XV, 276 S. graph. Darst. |
| ISBN: | 1420085301 9781420085303 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
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| 003 | DE-604 | ||
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| 008 | 090630s2010 d||| |||| 00||| eng d | ||
| 020 | |a 1420085301 |c (hbk.) : £85.00 |9 1-420-08530-1 | ||
| 020 | |a 9781420085303 |c (hbk.) : £85.00 |9 978-1-4200-8530-3 | ||
| 035 | |a (OCoLC)212843918 | ||
| 035 | |a (DE-599)GBV59362324X | ||
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| 100 | 1 | |a Bao-Cang, Ding |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Modern predictive control |c Ding Baocang |
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 2010 | |
| 300 | |a XV, 276 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Predictive control | |
| 650 | 0 | 7 | |a Prädiktive Regelung |0 (DE-588)4271836-3 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Prädiktive Regelung |0 (DE-588)4271836-3 |D s |
| 689 | 0 | |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017646434&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-017646434 | ||
Datensatz im Suchindex
| _version_ | 1804139253698920448 |
|---|---|
| adam_text | Contents
Abstract
v
Preface
vii
1
Systems,
modeling
and model
predictive control
1
1.1
Systems
............................... 2
1.2
Modeling
.............................. 4
1.3
State space model and input/output model
........... 6
1.3.1
State space model
..................... 6
1.3.2
Transfer function model
.................. 8
1.3.3
Impulse response and convolution model
......... 9
1.4
Discretization of continuous-time systems
............ 9
1.4.1
State space model
..................... 10
1.4.2
Impulse transfer function model
............. 11
1.4.3
Impulse response and convolution model
......... 12
1.5
Model predictive control
(MPC)
and its basic properties
.... 12
1.5.1
Streams and history
.................... 12
1.5.2
Basic properties
...................... 13
1.5.3
Three principles of industrial
MPC
.......... 17
1.6
Three typical optimal control problems of
MPC
......... 18
1.6.1
Infinite-horizon
....................... 19
1.6.2
Finite-horizon: classical
MPC
............... 20
1.6.3
Finite-horizon: synthesis approaches
........... 20
1.7
Finite-horizon control: an example based on three principles
22
1.8
Infinite-horizon control: an example of dual-mode
suboptimal
control
................................ 23
1.8.1
Three related control problems
.............. 24
1.8.2
Suboptimal
solution
.................... 25
1.8.3
Feasibility and stability analysis
............. 28
1.8.4
Numerical example
..................... 29
1.9
Development from classical
MPC
to synthesis approaches
... 32
xi
xji Contents
2
Model algorithmic control (MAC)
37
2.1
Principle of MAC
.......................... 37
2.1.1
Impulse response model
.................. 38
2.1.2
Prediction model and feedback correction
........ 39
2.1.3
Optimal control: case single input single output
..... 40
2.1.4
Optimal control: case multi-input multi-output
..... 42
2.2
Constraint handling
........................ 45
2.3
The usual pattern for implementation of
MPC
......... 47
3
Dynamic matrix control (DMC)
51
3.1
Step response model and its identification
............ 51
3.2
Principle of DMC
.......................... 53
3.2.1
Case single input single output
.............. 53
3.2.2
Case single input single output: alternative procedure
of deduction
......................... 56
3.2.3
Case multi-input multi-output
.............. 59
3.2.4
Remarks on
Matlab
MPC
Toolbox
............ 62
3.3
Constraint handling
........................ 63
4
Generalized predictive control (GPC)
65
4.1
Principle of GPC
.......................... 66
4.1.1
Prediction model
...................... 66
4.1.2
Solution to the Diophantine equation
........... 67
4.1.3
Receding horizon optimization
.............. 69
4.1.4
On-line identification and feedback correction
...... 72
4.2
Some basic properties
....................... 73
4.3
Stability results not related to the concrete model coefficients
. 76
4.3.1
Transformation to the linear quadratic control problem
76
4.3.2
Tool for stability proof: Kleinman s controller
...... 77
4.3.3
GPC law resembling Kleinman s controller
....... 79
4.3.4
Stability based on Kleinman s controller
......... 82
4.4
Cases of
multivariable
systems and constrained systems
.... 85
4.4.1 Multivariable GPC..................... 85
4.4.2
Constraint handling
.................... 87
4.5
GPC with terminal equality constraint
.............. 89
5
Two-step model predictive control
97
5.1
Two-step GPC
........................... 98
5.1.1
Case unconstrained systems
................ 98
5.1.2
Case with input saturation constraint
.......... 99
5.2
Stability of two-step GPC
..................... 101
5.2.1
Results based on Popov s Theorem
............ 101
5.2.2
Two algorithms for finding controller parameters
.... 104
5.2.3
Determination of bounds for the real nonlinearity
.... 106
5.3
Region of attraction by using two-step GPC
........... 106
Contents xiii
5.3.1 State
space
description
of the controller
......... 107
5.3.2
Stability relating with the region of attraction
...... 108
5.3.3
Computation of the region of attraction
......... 110
5.3.4
Numerical example
..................... 112
5.4
Two-step state feedback
MPC (TSMPC)
.............113
5.5
Stability of TSMPC
........................117
5.6
Design of the region of attraction of TSMPC based on semi-
global stability
...........................122
5.6.1
Case system matrix has no eigenvalue outside of the
unit circle
.......................... 122
5.6.2
Case system matrix has eigenvalues outside of the unit
circle
............................. 125
5.6.3
Numerical example
..................... 126
5.7
Two-step output feedback model predictive control (TSOFMPC)129
5.8
Stability of TSOFMPC
...................... 131
5.9
TSOFMPC: case where the intermediate variable is available
. 138
6
Sketch of synthesis approaches of
MPC
141
6.1
General idea: case discrete-time systems
............. 141
6.1.1
Modified optimization problem
.............. 141
6.1.2
Three ingredients and the uniform ideas for stability
proof
............................. 143
6.1.3
Direct method for stability proof
............. 143
6.1.4
Monotonicity method for stability proof
......... 145
6.1.5
Inverse optimality
..................... 146
6.2
General idea: case continuous-time systems
........... 147
6.3
Realizations
............................. 150
6.3.1
Using terminal equality constraint
............ 150
6.3.2
Using terminal cost function
............... 151
6.3.3
Using terminal constraint set
............... 151
6.3.4
Using terminal cost function and terminal constraint set
152
6.4
General idea: case uncertain systems (robust
MPC)
....... 153
6.4.1
Uniform idea for stability proof
.............. 154
6.4.2
Open-loop
min-max
MPC.................
155
6.5
Robust
MPC
based on closed-loop optimization
......... 156
6.6
A concrete realization: case continuous-time nominal systems
. 157
6.6.1
Determination of the three ingredients
.......... 158
6.6.2
Asymptotic stability
.................... 160
7
State feedback synthesis approaches
163
7.1
System with polytopic description, linear matrix inequality
. . 163
7.2
On-line approach based on
min-max
performance cost: case
zero-horizon
.............................165
7.2.1
Performance cost handling and unconstrained
MPC
. . 166
7.2.2
Constraint handling
....................167
xiv Contents
7.3
Off-line approach based on
min-max
performance cost: case
zero-horizon
.............................170
7.4
Off-line approach based on
min-max
performance cost: case
varying-horizon
...........................173
7.5
Off-line approach based on nominal performance cost: case
zero-horizon
.............................178
7.6
Off-line approach based on nominal performance cost: case
varying-horizon
...........................183
8
Synthesis approaches with finite switching horizon
189
8.1
Standard approach for nominal systems
.............189
8.2
Optimal solution to infinite-horizon constrained linear quadratic
control utilizing synthesis approach of
MPC
...........192
8.3
On-line approach for nominal systems
..............195
8.4
Quasi-optimal solution to the infinite-horizon constrained lin¬
ear time-varying quadratic regulation utilizing synthesis ap¬
proach of
MPC
...........................199
8.4.1
Overall idea
.........................200
8.4.2
Solution to the
min-max
constrained linear quadratic
control
............................202
8.4.3
Case finite-horizon without terminal weighting
.....203
8.4.4
Case finite-horizon with terminal weighting
.......204
8.4.5
Quasi-optimality, algorithm and stability
........205
8.4.6
Numerical example
.....................207
8.4.7
A comparison with another approach
...........207
8.5
On-line approach for systems with polytopic description
.... 210
8.6
Parameter-dependent on-line approach for systems with poly¬
topic description
..........................215
9
Open-loop optimization and closed-loop optimization in syn¬
thesis approaches
221
9.1
A simple approach based on partial closed-loop optimization
. 222
9.1.1
Aim: achieving larger region of attraction
........222
9.1.2
Efficient algorithm
.....................224
9.2
Triple-mode approach
.......................227
9.3
Mixed approach
...........................230
9.3.1
Algorithm
..........................230
9.3.2
Joint superiorities
.....................234
9.3.3
Numerical example
.....................235
9.4
Approach based on single-valued open-loop optimization and
its deficiencies
............................238
9.5
Approach based on parameter-dependent open-loop optimiza¬
tion and its properties
.......................241
9.6
Approach with unit switching horizon
..............244
Contents xv
10 Output
feedback synthesis approaches
247
10.1
Optimization problem: case systems with input-output (I/O)
nonlinearities
............................247
10.2
Conditions for stability and feasibility: case systems with I/O
nonlinearities
............................250
10.3
Realization algorithm: case systems with I/O nonlinearities
. . 254
10.3.1
General optimization problem
...............254
10.3.2
Linear matrix inequality optimization problem
.....256
10.3.3
Summary of the idea
....................258
10.4
Optimization problem: case systems with polytopic description
259
10.5
Optimality,
invariance
and constraint handling: case systems
with polytopic description
........ .............261
10.6
Realization algorithm: case systems with polytopic description
264
Bibliography
267
Index
273
|
| any_adam_object | 1 |
| author | Bao-Cang, Ding |
| author_facet | Bao-Cang, Ding |
| author_role | aut |
| author_sort | Bao-Cang, Ding |
| author_variant | d b c dbc |
| building | Verbundindex |
| bvnumber | BV035591216 |
| classification_rvk | ZQ 5290 |
| ctrlnum | (OCoLC)212843918 (DE-599)GBV59362324X |
| dewey-full | 629.8 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 629 - Other branches of engineering |
| dewey-raw | 629.8 |
| dewey-search | 629.8 |
| dewey-sort | 3629.8 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Book |
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| indexdate | 2024-07-09T21:41:09Z |
| institution | BVB |
| isbn | 1420085301 9781420085303 |
| language | English |
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| physical | XV, 276 S. graph. Darst. |
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| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | CRC Press |
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| spelling | Bao-Cang, Ding Verfasser aut Modern predictive control Ding Baocang Boca Raton [u.a.] CRC Press 2010 XV, 276 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Predictive control Prädiktive Regelung (DE-588)4271836-3 gnd rswk-swf Prädiktive Regelung (DE-588)4271836-3 s DE-604 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017646434&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bao-Cang, Ding Modern predictive control Predictive control Prädiktive Regelung (DE-588)4271836-3 gnd |
| subject_GND | (DE-588)4271836-3 |
| title | Modern predictive control |
| title_auth | Modern predictive control |
| title_exact_search | Modern predictive control |
| title_full | Modern predictive control Ding Baocang |
| title_fullStr | Modern predictive control Ding Baocang |
| title_full_unstemmed | Modern predictive control Ding Baocang |
| title_short | Modern predictive control |
| title_sort | modern predictive control |
| topic | Predictive control Prädiktive Regelung (DE-588)4271836-3 gnd |
| topic_facet | Predictive control Prädiktive Regelung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017646434&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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