Cover image for Non-linear predictive control : theory and practice
Title:
Non-linear predictive control : theory and practice
Series:
IEE control engineering series ; 61
Publication Information:
London : Institution of Electrical Engineers, 2001
Physical Description:
xiv, 261 p. : ill. ; 24 cm.
ISBN:
9780852969847
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30000010231305 TJ217.6 N664 2001 Open Access Book Book
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Summary

Summary

Model-based predictive control (MPC) has proved to be a fertile area of research. It has gained enormous success within industry, especially in the context of process control. Nonlinear model-based predictive control (NMPC) is of particular interest as this best represents the dynamics of most real plant. This book collects together the important results which have emerged in this field, illustrating examples by means of simulations on industrial models. In particular there are contributions on feedback linearisation, differential flatness, control Lyapunov functions, output feedback, and neural networks. The international contributors to the book are all respected leaders within the field, which makes for essential reading for advanced students, researchers and industrialists in the field of control of complex systems.


Table of Contents

T.A. Badgwell and S.J. QinR.S. Parker and E.P. Gatzke and R. Mahadevan and E.S. Meadows and F.J. Doyle IIIM. Sznaier and J. CloutierM. Niemiec and C. KravarisM. Cannon and B. KouvaritakisB. Kouvaritakis and J.A. Rossiter and M. CannonA. Zheng and Wei-hua ZhangM. Soroush and H.M. SoroushB.A. OgunnaikeS. Townsend and G.W. IrwinB. Lennox and G. Montague
Prefacep. xi
Contributorsp. xiii
Part I

p. 1

1 Review of nonlinear model predictive control applicationsp. 3
1.1 Introductionp. 3
1.2 Theoretical foundations of NMPCp. 6
1.3 Industrial implementations of NMPCp. 9
1.3.1 Modelsp. 9
1.3.2 Output feedbackp. 15
1.3.3 Steady-state optimisationp. 15
1.3.4 Dynamic optimisationp. 16
1.3.5 Constraint formulationsp. 16
1.3.6 Output trajectoriesp. 17
1.3.7 Output horizon and input parameterisationp. 18
1.3.8 Solution methodsp. 19
1.4 NMPC application examplesp. 19
1.4.1 PFC: application to batch reactorsp. 20
1.4.2 Aspen Target: application to a pulverised coal fired boilerp. 20
1.4.3 MVC: application to an ammonia plantp. 21
1.4.4 NOVA-NLC: application to a polymerisation processp. 22
1.4.5 Process Perfecter: application to a polypropylene processp. 24
1.5 Future needs for NMPC technology developmentp. 27
1.5.1 Model developmentp. 27
1.5.2 Output feedbackp. 28
1.5.3 Optimisation methodsp. 28
1.5.4 User interfacep. 29
1.5.5 Justification of NMPCp. 29
1.5.6 Other issuesp. 29
1.6 Conclusionsp. 29
1.7 Referencesp. 30
1.8 Notesp. 32
2 Nonlinear model predictive control: issues and applicationsp. 33
2.1 Introductionp. 33
2.2 Exploiting model structurep. 34
2.2.1 Motivationp. 34
2.2.2 Model identificationp. 35
2.2.3 Controller synthesisp. 36
2.2.4 Application: a continuous bioreactorp. 38
2.3 Efficient dynamic optimisation using differential flatnessp. 39
2.3.1 Motivationp. 39
2.3.2 Problem formulationp. 40
2.3.3 Application: biomass optimisationp. 41
2.4 Model-based control of population balance systemsp. 43
2.4.1 Motivation: emulsion polymerisationp. 43
2.4.2 Model developmentp. 44
2.4.3 Numerical solutions of the population balance equationp. 45
2.4.4 Approaches to controlp. 45
2.4.5 Measurement and feedbackp. 46
2.4.6 Batch polymerisation examplep. 47
2.5 Disturbance estimationp. 48
2.5.1 Motivationp. 48
2.5.2 Estimation formulationp. 49
2.5.3 Application: chemical reactor disturbance estimationp. 51
2.6 Conclusionsp. 51
2.7 Acknowledgmentsp. 53
2.8 Referencesp. 53
2.9 Notesp. 57
Part II

p. 59

3 Model predictive control: output feedback and tracking of nonlinear systemsL. Magni and G. De Nicolao and R. Scattolini
3.1 Introductionp. 61
3.2 Preliminaries and state-feedback controlp. 63
3.3 Output feedbackp. 66
3.4 Tracking and disturbance rejection for signals generated by an exosystemp. 68
3.5 Tracking 'asymptotically' constant referencesp. 72
3.5.1 State-space modelsp. 73
3.5.2 Nonlinear ARX modelsp. 75
3.6 Conclusionsp. 77
3.7 Acknowledgmentp. 77
3.8 Referencesp. 78
4 Model predictive control of nonlinear parameter varying systems via receding horizon control Lyapunov functionsp. 81
4.1 Introductionp. 81
4.2 Preliminariesp. 84
4.2.1 Notation and definitionsp. 84
4.2.2 Quadratic regulator problem for NLPV systemsp. 85
4.3 Equivalent finite horizon regulation problemp. 86
4.4 Modified receding horizon controllerp. 89
4.5 Selecting suitable CLFsp. 91
4.5.1 Autonomous systemsp. 92
4.5.2 Linear parameter varying systemsp. 93
4.6 Connections with other approachesp. 96
4.7 Incorporating constraintsp. 97
4.8 Illustrative examplesp. 98
4.9 Conclusionsp. 103
4.10 Acknowledgmentsp. 103
4.11 Referencesp. 103
4.12 Appendix: SDRE approach to nonlinear regulationp. 105
5 Nonlinear model-algorithmic control for multivariable nonminimum-phase processesp. 107
5.1 Introductionp. 107
5.2 Preliminariesp. 109
5.2.1 Relative orderp. 110
5.2.2 Zero dynamics and minimum-phase behaviourp. 111
5.3 Brief review of nonlinear model-algorithmic controlp. 112
5.4 Model-algorithmic control with nonminimum-phase compensation using synthetic outputsp. 114
5.5 Construction of statically equivalent outputs with pre-assigned transmission zerosp. 116
5.5.1 Construction of independent functions which vanish on the equilibrium manifoldp. 117
5.5.2 A class of statically equivalent outputsp. 119
5.5.3 Assignment of transmission zerosp. 120
5.6 Application: control of a nonminimum-phase chemical reactorp. 122
5.7 Conclusionp. 128
5.8 Referencesp. 128
5.9 Appendixp. 129
5.9.1 Proof of Proposition 1p. 129
5.9.2 Proof of Lemma 1p. 130
6 Open-loop and closed-loop optimality in interpolation MPCp. 131
6.1 Introductionp. 131
6.2 Problem statementp. 132
6.3 Predicted input/state trajectoriesp. 133
6.3.1 Unconstrained optimal control law u[superscript o]p. 134
6.3.2 Feasible control law u[superscript f]p. 136
6.4 Interpolation MPC algorithmsp. 138
6.4.1 Comparison of open-loop optimalityp. 140
6.4.2 Closed-loop optimality propertiesp. 141
6.5 Simulation examplep. 145
6.6 Conclusionsp. 148
6.7 Acknowledgmentp. 148
6.8 Referencesp. 149
Part III

p. 151

7 Closed-loop predictions in model based predictive control of linear and nonlinear systemsp. 153
7.1 Introductionp. 153
7.2 Review of earlier workp. 155
7.3 MPC for linear uncertain systemsp. 158
7.4 Invariance/feasibility for nonlinear systemsp. 161
7.5 Numerical examplesp. 165
7.5.1 Application of Algorithm 1p. 165
7.5.2 Application of Algorithm 2p. 167
7.6 Acknowledgmentp. 171
7.7 Referencesp. 171
8 Computationally efficient nonlinear predictive control algorithm for control of constrained nonlinear systemsp. 173
8.1 Introductionp. 173
8.2 Preliminariesp. 175
8.3 Computationally efficient algorithmp. 177
8.4 Examplesp. 179
8.4.1 Distillation dual composition controlp. 179
8.4.2 Tennessee-Eastman problemp. 181
8.5 Conclusionsp. 184
8.6 Acknowledgmentp. 184
8.7 Referencesp. 185
9 Long-prediction-horizon nonlinear model predictive controlp. 189
9.1 Introductionp. 189
9.2 Scope and preliminariesp. 191
9.3 Optimisation problem: model predictive control lawp. 191
9.4 Nonlinear feedforward/state feedback designp. 192
9.5 Nonlinear feedback controller designp. 194
9.6 Application to linear processesp. 195
9.7 Conclusionsp. 197
9.8 Acknowledgmentsp. 197
9.9 Referencesp. 197
9.10 Appendixp. 198
9.10.1 Proof of Theorem 1p. 198
9.10.2 Proof of Theorem 2p. 200
Part IV

p. 203

10 Nonlinear control of industrial processesp. 205
10.1 Introductionp. 205
10.2 Applying nonlinear control to industrial processesp. 206
10.2.1 Quantitative needs assessmentp. 207
10.2.2 Technological and implementation issuesp. 208
10.3 Model predictive control of a spent acid recovery converterp. 209
10.3.1 The processp. 209
10.3.2 Process operation objectivesp. 210
10.3.3 A control perspective of the processp. 211
10.3.4 Overall control strategyp. 212
10.3.5 Process model developmentp. 214
10.3.6 Control system design and implementationp. 215
10.3.7 Control system performancep. 216
10.4 Summary and conclusionsp. 219
10.5 Acknowledgmentp. 220
10.6 Referencesp. 220
11 Nonlinear model based predictive control using multiple local modelsp. 223
11.1 Introductionp. 224
11.2 Local model networksp. 225
11.3 Nonlinear model based predictive controlp. 228
11.3.1 Local controller generalised predictive control (LC-GPC)p. 229
11.3.2 Local model generalised predictive control (LM-GPC)p. 230
11.4 Applicationp. 232
11.4.1 pH neutralisation pilot plantp. 232
11.4.2 Identificationp. 232
11.4.3 Controlp. 234
11.5 Discussion and conclusionsp. 238
11.6 Referencesp. 241
12 Neural network control of a gasoline engine with rapid samplingp. 245
12.1 Introductionp. 245
12.2 Artificial neural networksp. 246
12.3 ANN engine model developmentp. 248
12.4 Neural network based controlp. 250
12.4.1 Application of the ANN model based controller to the gasoline enginep. 252
12.5 Conclusionsp. 253
12.6 Referencesp. 254
Indexp. 257