Nonlinear model predictive control: theory and algorihms
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
London
Springer
2013
|
| Edition: | [pbk ed.] |
| Series: | Communications and control engineering
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | XI, 359 S. Ill., graph. Darst. |
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Record in the Search Index
| _version_ | 1804152871694893056 |
|---|---|
| adam_text | Contents
introduction
................................
I
1 1
What Is Nonlinear Model Predictive Control?
........... 1
1.2
Where Did NMPC Come from?
................... 3
¡.3
How Is This Book Organized?
.................... 3
і
.4
What Is Not Covered in This Book?
................. 9
References
.............................. ] 0
Discrete Time and Sampled Data Systems
............... 13
2.1
Discrete Time Systems
........................ 13
2.2
Sampled Data Systems
........................ 16
23
Stability of Discrete Time Systems
................. 28
2.4
Stability of Sampled Data Systems
................. 35
2.5
Notes and Extensions
........................ 39
2.6
Problems
............................... 39
References
.............................. 41
Nonlinear Model Predictive Control
.................. 43
3.1
The Basic NMPC Algorithm
.................... 43
3.2
Constraints
.............................. 45
3.3
Variants of the Basic NMPC Algorithms
.............. 50
3.4
The Dynamic Programming Principle
................ 56
3.5
Notes and Extensions
........................ 6?.
v6 Problems
.......................,....,.. 64
References
....................,.........
6.J
Infinite Horizon Optimal Control
.................... 67
1.1
Definition and Well Posedness of the Problem
...... , , . . 67
4.2
The Dynamic Programming Principle
................ /0
1-3
Relaxed Dynamic Programming
...................
f j
4.4
Notes and Extensions
........................ 8/
4.5
Problems
............................... 83
References
.............................. 84
ix
* Contents
5
Stability and Suboptimality Using Stabilizing Constraints
...... 87
5.1
The Relaxed Dynamic Programming Approach
........... 87
5.2
Equilibrium
Endpoint
Constraint
.................. 88
5.3
Lyapunov Function Terminal Cost
.................. 95
5.4
Suboptimality and Inverse Optimality
................ 101
5.5
Notes and Extensions
........................ 109
5.6
Problems
............................... 110
References
.............................. 112
6
Stability and Suboptimality Without Stabilizing Constraints
..... 113
6.1
Setting and Preliminaries
...................... 113
6.2
Asymptotic Controllability with Respect to
í
............ 116
6.3
Implications of the Controllability Assumption
........... 119
6.4
Computation of a
.......................... 121
6.5
Main Stability and Performance Results
............... 125
6.6
Design of Good Running Costs
í
.................. 133
6.7 Semiglobal
and Practical Asymptotic Stability
........... 142
6.8
Proof of Proposition
6.17 ...................... 150
6.9
Notes and Extensions
........................ 159
6.10
Problems
............................... ¡61
References
.............................. 162
7
Variants and Extensions
......................... 165
7.1
Mixed Constrained-Unconstrained Schemes
............ 165
7.2
Unconstrained NMPC with Terminal Weights
........... 168
7.3
Nonpositive
Definite Running Cost
................. 170
7.4
MuJtistep NMPC-Feedback Laws
.................. 174
7.5
Fast Sampling
............................ 176
7.6
Compensation of Computation Times
................ 180
7.7
Online Measurement of a
...................... 183
7.8
Adaptive Optimization Horizon
................... 191
7.9
Nonoptima] NMPC
......................... 198
7.10
Beyond Stabilization and Tracking
................. 207
References
.............................. 209
8
Feasibility and Robustness
........................ 211
8.1
The Feasibility Problem
....................... 211
8.2
Feasibility of Unconstrained NMPC Using Exit Sets
........ 214
8.3
Feasibility of Unconstrained NMPC Using Stability
........ 217
8.4
Comparing Terminal Constrained vs. Unconstrained NMPC
.... 222
8.5
Robustness: Basic Definition and Concepts
............. 225
8.6
Robustness Without Slate Constraints
................ 227
8.7
Examples
loi Non
robustness Under Staic Constraints
....... 232
8.8
Robustness with State Constraints via Robust-optima! Feasibility
. 237
8.9
Robustness with State Constraints via Continuity
оГ Уд/......
241
8.10
Notes and Extensions
........................ 246
8.1 1
Problems
............................... 249
References
.............................. 249
Conicnb X1
і)
Numerical Discretization
......................... 251
9.1
Basic Solution Methods
....................... 251
9.2
Convergence Theory
......................... 256
9.3
Adaptive Step Size Control
..................... 260
9.4
Using the Methods Within the NMPC Algorithms
......... 264
9.3
NHimerical Approximation Errors and Stability
........... 266
9.6
Notes and Extensions
........................269
9.7
Problems
...............................271
References
..............................272
10
Numerical Optimal Control of Nonlinear Systems
...........275
[O.I Discretization of the NMPC Problem
................275
10.2
Unconstrained Optimization
.....................288
0.3
Constrained Optimization
......................292
0.4
Implementation Issues in NMPC
..................313
0.5
Warm Start of the NMPC Optimization
...............324
o.à
Nonoptimal NMPC
.........................33
j
0.7
Notes and Extensions
.........................333
o S
Problems
...............................33/
References
..............................33 ■/
Vppeiulix NMPC
3oiì,4Viì,ve
ЗщцлогкЇАЩ
TjDS
Bcoiii
............34
A.I The VIATLAB NMPC Routine
.................. 34 ]
Л.2
Additional
MATLAB
and MAPLE Routines
............343
. 3 The
C++
NMPC Software
......................345
(i Io.ssary
............................... 347
Index
.............
Communications and Control Engineering
Lars Grüne· Jürgen
Pannek
Nonlinear Model Predictive Control
Theory and Algorithms
Nonlinear model predictive control (NMPC) is widely used in the process and chemical
industries and increasingly for applications, such as those in the automotive industry,
which use higher data sampling rates.
Nonlinear Model Predictive Control is a thorough and rigorous introduction to NMPC
for discrete-time and sampled-data systems. NMPC is interpreted as an approximation
of infinite-horizon optimal control so that important properties like closed-loop stabi¬
lity, inverse optimality and suboptimality can be derived in a uniform manner. These
results are complemented by discussions of feasibility and robustness. NMPC schemes
with and without stabilizing terminal constraints are detailed and intuitive examples
illustrate the performance of different NMPC variants. An introduction to nonlinear
optimal control algorithms gives insight into how the nonlinear optimisation routine
-
the core of any NMPC controller
-
works. An appendix covering NMPC software
and accompanying software in
MATLAB*
and
C++
(downloadable from www.springer.
com/978-0-85729-500-2) enables readers to perform computer experiments exploring
the possibilities and limitations of NMPC.
Nonlinear Model Predictive Control is primarily aimed at academic researchers and
practitioners working in control and optimisation but the text is self-contained fea¬
turing background material on infinite-horizon optimal control and Lyapunov stability
theory which makes the book accessible to graduate of control engineering and applied
mathematics.
The Communications and Control Engineering series reports major technological
advances which have potential for great impact in the fields of communication and
control. It reflects research in industrial and academic institutions around the world
so that the readership can exploit new possibilities as they become available.
|
| any_adam_object | 1 |
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| author_GND | (DE-588)133659097 (DE-588)139861610 |
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| discipline | Mathematik |
| edition | [pbk ed.] |
| format | Book |
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| spelling | Grüne, Lars 1967- Verfasser (DE-588)133659097 aut Nonlinear model predictive control theory and algorihms Lars Grüne ; Jürgen Pannek [pbk ed.] London Springer 2013 XI, 359 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Communications and control engineering Prädiktive Regelung (DE-588)4271836-3 gnd rswk-swf Nichtlineare Regelung (DE-588)4132964-8 gnd rswk-swf Prädiktive Regelung (DE-588)4271836-3 s Nichtlineare Regelung (DE-588)4132964-8 s DE-604 Pannek, Jürgen Sonstige (DE-588)139861610 oth Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027734084&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027734084&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Grüne, Lars 1967- Nonlinear model predictive control theory and algorihms Prädiktive Regelung (DE-588)4271836-3 gnd Nichtlineare Regelung (DE-588)4132964-8 gnd |
| subject_GND | (DE-588)4271836-3 (DE-588)4132964-8 |
| title | Nonlinear model predictive control theory and algorihms |
| title_auth | Nonlinear model predictive control theory and algorihms |
| title_exact_search | Nonlinear model predictive control theory and algorihms |
| title_full | Nonlinear model predictive control theory and algorihms Lars Grüne ; Jürgen Pannek |
| title_fullStr | Nonlinear model predictive control theory and algorihms Lars Grüne ; Jürgen Pannek |
| title_full_unstemmed | Nonlinear model predictive control theory and algorihms Lars Grüne ; Jürgen Pannek |
| title_short | Nonlinear model predictive control |
| title_sort | nonlinear model predictive control theory and algorihms |
| title_sub | theory and algorihms |
| topic | Prädiktive Regelung (DE-588)4271836-3 gnd Nichtlineare Regelung (DE-588)4132964-8 gnd |
| topic_facet | Prädiktive Regelung Nichtlineare Regelung |
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