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Robust and Adaptive Model Predictive Control of Nonlinear Systems [Hardback]

(Queens University, Faculty of Engineering and Applied Science, Canada), (LyondellBasell), (United Technologies Research Centre, USA)
  • Formāts: Hardback, 272 pages, height x width: 234x156 mm
  • Sērija : Control, Robotics and Sensors
  • Izdošanas datums: 30-Dec-2014
  • Izdevniecība: Institution of Engineering and Technology
  • ISBN-10: 1849195528
  • ISBN-13: 9781849195522
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Robust and Adaptive Model Predictive Control of Nonlinear SystemsPalielināt
  • Formāts: Hardback, 272 pages, height x width: 234x156 mm
  • Sērija : Control, Robotics and Sensors
  • Izdošanas datums: 30-Dec-2014
  • Izdevniecība: Institution of Engineering and Technology
  • ISBN-10: 1849195528
  • ISBN-13: 9781849195522
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781849195522.html
Keywords:
Most physical systems possess parametric uncertainties or unmeasurable parameters and, since parametric uncertainty may degrade the performance of model predictive control (MPC), mechanisms to update the unknown or uncertain parameters are desirable in application. One possibility is to apply adaptive extensions of MPC in which parameter estimation and control are performed online. This book proposes such an approach, with a design methodology for adaptive robust nonlinear MPC (NMPC) systems in the presence of disturbances and parametric uncertainties. One of the key concepts pursued is the concept of set-based adaptive parameter estimation, which provides a mechanism to estimate the unknown parameters as well as an estimate of the parameter uncertainty set. The knowledge of non-conservative uncertain set estimates is exploited in the design of robust adaptive NMPC algorithms that guarantee robustness of the NMPC system to parameter uncertainty.



Topics covered include: a review of nonlinear MPC; extensions for performance improvement; introduction to adaptive robust MPC; computational aspects of robust adaptive MPC; finite-time parameter estimation in adaptive control; performance improvement in adaptive control; adaptive MPC for constrained nonlinear systems; adaptive MPC with disturbance attenuation; robust adaptive economic MPC; setbased estimation in discrete-time systems; and robust adaptive MPC for discrete-time systems.
List of figures
x
List of tables
xiv
Acknowledgments xv
1 Introduction
1(2)
2 Optimal control
3(8)
2.1 Emergence of optimal control
3(1)
2.2 MPC as receding-horizon optimization
4(1)
2.3 Current limitations in MPC
4(1)
2.4 Notational and mathematical preliminaries
5(1)
2.5 Brief review of optimal control
6(5)
2.5.1 Variational approach: Euler, Lagrange & Pontryagin
6(2)
2.5.2 Dynamic programming: Hamilton, Jacobi, & Bellman
8(1)
2.5.3 Inverse-optimal control Lyapunov functions
9(2)
3 Review of nonlinear MPC
11(12)
3.1 Sufficient conditions for stability
12(1)
3.2 Sampled-data framework
12(2)
3.2.1 General nonlinear sampled-data feedback
12(1)
3.2.2 Sampled-data MPC
13(1)
3.2.3 Computational delay and forward compensation
14(1)
3.3 Computational techniques
14(6)
3.3.1 Single-step SQP with initial-value embedding
16(1)
3.3.2 Continuation methods
17(2)
3.3.3 Continuous-time adaptation for 2-stabilized systems
19(1)
3.4 Robustness considerations
20(3)
4 A real-time nonlinear MPC technique
23(24)
4.1 Introduction
23(1)
4.2 Problem statement and assumptions
24(3)
4.3 Preliminary results
27(2)
4.3.1 Incorporation of state constraints
27(1)
4.3.2 Parameterization of the input trajectory
28(1)
4.4 General framework for real-time MPC
29(4)
4.4.1 Description of algorithm
29(2)
4.4.2 A notion of closed-loop "solutions"
31(1)
4.4.3 Main result
32(1)
4.5 Flow and jump mappings
33(3)
4.5.1 Improvement by υ: the SD approach
33(1)
4.5.2 Improvement by ψ a real-time approach
34(2)
4.5.3 Other possible definitions for ψ and υ
36(1)
4.6 Computing the real-time update law
36(2)
4.6.1 Calculating gradients
36(1)
4.6.2 Selecting the descent metric
37(1)
4.7 Simulation examples
38(3)
4.7.1 Example 4.1
38(1)
4.7.2 Example 4.2
39(2)
4.8 Summary
41(1)
4.9 Proofs for
Chapter 4
42(5)
4.9.1 Proof of Claim 4.2.2
42(1)
4.9.2 Proof of Lemma 4.3.2
43(1)
4.9.3 Proof of Corollary 4.3.6
43(1)
4.9.4 Proof of Theorem 4.4.4
44(3)
5 Extensions for performance improvement
47(24)
5.1 General input parameterizations, and optimizing time support
47(15)
5.1.1 Revised problem setup
48(1)
5.1.2 General input parameterizations
49(1)
5.1.3 Requirements for the local stabilizer
49(3)
5.1.4 Closed-loop hybrid dynamics
52(2)
5.1.5 Stability results
54(1)
5.1.6 Simulation Example 5.1
55(1)
5.1.7 Simulation Example 5.2
56(6)
5.2 Robustness properties in overcoming locality
62(9)
5.2.1 Robustness properties of the real-time approach
62(3)
5.2.2 Robustly incorporating global optimization methods
65(2)
5.2.3 Simulation Example 5.3
67(4)
6 Introduction to adaptive robust MPC
71(26)
6.1 Review of NMPC for uncertain systems
71(5)
6.1.1 Explicit robust MPC using open-loop models
72(1)
6.1.2 Explicit robust MPC using feedback models
73(2)
6.1.3 Adaptive approaches to MPC
75(1)
6.2 An adaptive approach to robust MPC
76(2)
6.3 Minimally conservative approach
78(2)
6.3.1 Problem description
78(2)
6.4 Adaptive robust controller design framework
80(4)
6.4.1 Adaptation of parametric uncertainty sets
80(1)
6.4.2 Feedback-MPC framework
81(1)
6.4.3 Generalized terminal conditions
82(1)
6.4.4 Closed-loop stability
83(1)
6.5 Computation and performance issues
84(1)
6.5.1 Excitation of the closed-loop trajectories
84(1)
6.5.2 A practical design approach for W and Xf
84(1)
6.6 Robustness issues
85(3)
6.7 Example problem
88(1)
6.8 Conclusions
89(1)
6.9 Proofs for
Chapter 6
89(8)
6.9.1 Proof of Theorem 6.4.6
89(2)
6.9.2 Proof of Proposition 6.5.1
91(1)
6.9.3 Proof of Claim 6.6.1
92(1)
6.9.4 Proof of Proposition 6.6.2
93(4)
7 Computational aspects of robust adaptive MPC
97(30)
7.1 Problem description
97(1)
7.2 Adaptive robust design framework
98(9)
7.2.1 Method for closed-loop adaptive control
98(4)
7.2.2 Finite-horizon robust MPC design
102(3)
7.2.3 Stability of the underlying robust MPC
105(2)
7.3 Internal model of the identifier
107(3)
7.4 Incorporating asymptotic filters
110(1)
7.5 Simulation example
111(6)
7.5.1 System description
112(1)
7.5.2 Terminal penalty
112(2)
7.5.3 Simulation results
114(2)
7.5.4 Discussion
116(1)
7.6 Summary
117(1)
7.7 Proofs for
Chapter 7
117(10)
7.7.1 Proof of Proposition 7.2.2
117(2)
7.7.2 Proof of Theorem 7.2.8
119(3)
7.7.3 Proof of Claim 7.3.5
122(1)
7.7.4 Proof of Proposition 7.3.6
123(2)
7.7.5 Proof of Corollary 7.3.8
125(2)
8 Finite-time parameter estimation in adaptive control
127(12)
8.1 Introduction
127(1)
8.2 Problem description and assumptions
128(1)
8.3 FT parameter identification
129(3)
8.3.1 Absence of PE
131(1)
8.4 Robustness property
132(2)
8.5 Dither signal design
134(1)
8.5.1 Dither signal removal
135(1)
8.6 Simulation examples
135(3)
8.6.1 Example 1
135(1)
8.6.2 Example 2
135(3)
8.7 Summary
138(1)
9 Performance improvement in adaptive control
139(8)
9.1 Introduction
139(1)
9.2 Adaptive compensation design
139(2)
9.3 Incorporating adaptive compensator for performance improvement
141(1)
9.4 Dither signal update
142(1)
9.5 Simulation example
143(3)
9.6 Summary
146(1)
10 Adaptive MPC for constrained nonlinear systems
147(18)
10.1 Introduction
147(1)
10.2 Problem description
148(1)
10.3 Estimation of uncertainty
148(3)
10.3.1 Parameter adaptation
148(1)
10.3.2 Set adaptation
149(2)
10.4 Robust adaptive MPC---a min--max approach
151(2)
10.4.1 Implementation algorithm
151(1)
10.4.2 Closed-loop robust stability
152(1)
10.5 Robust adaptive MPC---a Lipschitz-based approach
153(3)
10.5.1 Prediction of state error bound
154(1)
10.5.2 Lipschitz-based finite horizon optimal control problem
154(1)
10.5.3 Implementation algorithm
155(1)
10.6 Incorporating FTI
156(2)
10.6.1 FTI-based min--max approach
156(1)
10.6.2 FTI-based Lipshitz-bound approach
157(1)
10.7 Simulation example
158(2)
10.8 Conclusions
160(1)
10.9 Proofs of main results
160(5)
10.9.1 Proof of Theorem 10.4.4
160(3)
10.9.2 Proof of Theorem 10.5.3
163(2)
11 Adaptive MPC with disturbance attenuation
165(12)
11.1 Introduction
165(1)
11.2 Revised problem set-up
165(1)
11.3 Parameter and uncertainty set estimation
166(3)
11.3.1 Preamble
166(1)
11.3.2 Parameter adaptation
166(2)
11.3.3 Set adaptation
168(1)
11.4 Robust adaptive MPC
169(2)
11.4.1 Min--max approach
169(1)
11.4.2 Lipschitz-based approach
170(1)
11.5 Closed-loop robust stability
171(1)
11.5.1 Main results
172(1)
11.6 Simulation example
172(1)
11.7 Conclusions
173(4)
12 Robust adaptive economic MPC
177(24)
12.1 Introduction
177(2)
12.2 Problem description
179(1)
12.3 Set-based parameter estimation routine
180(3)
12.3.1 Adaptive parameter estimation
180(1)
12.3.2 Set adaptation
181(2)
12.4 Robust adaptive economic MPC implementation
183(9)
12.4.1 Alternative stage cost in economic MPC
183(3)
12.4.2 A min--max approach
186(2)
12.4.3 Main result
188(2)
12.4.4 Lipschitz-based approach
190(2)
12.5 Simulation example
192(7)
12.5.1 Terminal penalty and terminal set design
193(6)
12.6 Conclusions
199(2)
13 Set-based estimation in discrete-time systems
201(14)
13.1 Introduction
201(1)
13.2 Problem description
202(1)
13.3 FT parameter identification
203(1)
13.4 Adaptive compensation design
204(1)
13.5 Parameter uncertainty set estimation
205(5)
13.5.1 Parameter update
205(3)
13.5.2 Set update
208(2)
13.6 Simulation examples
210(3)
13.6.1 FT parameter identification
211(1)
13.6.2 Adaptive compensation design
211(2)
13.6.3 Parameter uncertainty set estimation
213(1)
13.7 Summary
213(2)
14 Robust adaptive MPC for discrete-time systems
215(22)
14.1 Introduction
215(1)
14.2 Problem description
215(1)
14.3 Parameter and uncertainty set estimation
216(2)
14.3.1 Parameter adaptation
216(1)
14.3.2 Set update
217(1)
14.4 Robust adaptive MPC
218(3)
14.4.1 A min--max approach
218(1)
14.4.2 Lipschitz-based approach
219(2)
14.5 Closed-loop robust stability
221(2)
14.5.1 Main results
221(2)
14.6 Simulation example
223(12)
14.6.1 Open-loop tests of the parameter estimation routine
225(3)
14.6.2 Closed-loop simulations
228(3)
14.6.3 Closed-loop simulations with disturbances
231(4)
14.7 Summary
235(2)
Bibliography 237(12)
Index 249
Martin Guay is a Professor at the Faculty of Engineering and Applied Science at Queens University, Canada, where his research interests include process control, statistical modeling of dynamical systems, extremum seeking control, observation and adaptation in nonlinear systems, and supervisory control design for flexible manufacturing systems. He is Deputy Editor-in-Chief of the Journal of Process Control, and Associate Editor of Automatica, IEEE Transactions on Control Systems Technology and Canadian Journal of Chemical Engineering.



Veronica Adetola is a Research Engineer at the United Technologies Research Centre, USA. Her research interests include model-based design and control of complex dynamical systems, model predictive control of constrained uncertain systems, real-time optimization, adaptive control, parameter estimation and system identification.



Darryl DeHaan is currently a Senior Process Control Engineer with LyondellBasell and has been engaged in both industrial controller implementation and research since 2006. He has a Ph.D. in Chemical Engineering from Queens University, Canada, where his research efforts focused on model predictive control techniques for nonlinear uncertain systems.