Non-Linear Predictive Control : Theory and Practice

Theory and Practice

By: Basil Kouvaritakis (Editor), Mark Cannon (Editor)

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Hardcover | 1 December 2000

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276 Pages

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23.5 x 15.88 x 1.91

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This book collates the important results which have emerged in the field of non-linear model based predictive control. Feedback linearization, differential flatness, control. Feedback linearization, differential flatness, control Lyapunov functions, output feedback and neural networks are all discussed.

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Non-Fiction Science Physics Electricity Mathematics Algebra Engineering & Technology Electronics & Communications Engineering Electronics Engineering Automatic Control Engineering Robotics Technology in General

Engineering in General

Preface	p. xi
Contributors	p. xiii
	p. 1
Review of nonlinear model predictive control applications	p. 3
Introduction	p. 3
Theoretical foundations of NMPC	p. 6
Industrial implementations of NMPC	p. 9
Models	p. 9
Output feedback	p. 15
Steady-state optimisation	p. 15
Dynamic optimisation	p. 16
Constraint formulations	p. 16
Output trajectories	p. 17
Output horizon and input parameterisation	p. 18
Solution methods	p. 19
NMPC application examples	p. 19
PFC: application to batch reactors	p. 20
Aspen Target: application to a pulverised coal fired boiler	p. 20
MVC: application to an ammonia plant	p. 21
NOVA-NLC: application to a polymerisation process	p. 22
Process Perfecter: application to a polypropylene process	p. 24
Future needs for NMPC technology development	p. 27
Model development	p. 27
Output feedback	p. 28
Optimisation methods	p. 28
User interface	p. 29
Justification of NMPC	p. 29
Other issues	p. 29
Conclusions	p. 29
References	p. 30
Notes	p. 32
Nonlinear model predictive control: issues and applications	p. 33
Introduction	p. 33
Exploiting model structure	p. 34
Motivation	p. 34
Model identification	p. 35
Controller synthesis	p. 36
Application: a continuous bioreactor	p. 38
Efficient dynamic optimisation using differential flatness	p. 39
Motivation	p. 39
Problem formulation	p. 40
Application: biomass optimisation	p. 41
Model-based control of population balance systems	p. 43
Motivation: emulsion polymerisation	p. 43
Model development	p. 44
Numerical solutions of the population balance equation	p. 45
Approaches to control	p. 45
Measurement and feedback	p. 46
Batch polymerisation example	p. 47
Disturbance estimation	p. 48
Motivation	p. 48
Estimation formulation	p. 49
Application: chemical reactor disturbance estimation	p. 51
Conclusions	p. 51
Acknowledgments	p. 53
References	p. 53
Notes	p. 57
	p. 59
Model predictive control: output feedback and tracking of nonlinear systems
Introduction	p. 61
Preliminaries and state-feedback control	p. 63
Output feedback	p. 66
Tracking and disturbance rejection for signals generated by an exosystem	p. 68
Tracking 'asymptotically' constant references	p. 72
State-space models	p. 73
Nonlinear ARX models	p. 75
Conclusions	p. 77
Acknowledgment	p. 77
References	p. 78
Model predictive control of nonlinear parameter varying systems via receding horizon control Lyapunov functions	p. 81
Introduction	p. 81
Preliminaries	p. 84
Notation and definitions	p. 84
Quadratic regulator problem for NLPV systems	p. 85
Equivalent finite horizon regulation problem	p. 86
Modified receding horizon controller	p. 89
Selecting suitable CLFs	p. 91
Autonomous systems	p. 92
Linear parameter varying systems	p. 93
Connections with other approaches	p. 96
Incorporating constraints	p. 97
Illustrative examples	p. 98
Conclusions	p. 103
Acknowledgments	p. 103
References	p. 103
Appendix: SDRE approach to nonlinear regulation	p. 105
Nonlinear model-algorithmic control for multivariable nonminimum-phase processes	p. 107
Introduction	p. 107
Preliminaries	p. 109
Relative order	p. 110
Zero dynamics and minimum-phase behaviour	p. 111
Brief review of nonlinear model-algorithmic control	p. 112
Model-algorithmic control with nonminimum-phase compensation using synthetic outputs	p. 114
Construction of statically equivalent outputs with pre-assigned transmission zeros	p. 116
Construction of independent functions which vanish on the equilibrium manifold	p. 117
A class of statically equivalent outputs	p. 119
Assignment of transmission zeros	p. 120
Application: control of a nonminimum-phase chemical reactor	p. 122
Conclusion	p. 128
References	p. 128
Appendix	p. 129
Proof of Proposition 1	p. 129
Proof of Lemma 1	p. 130
Open-loop and closed-loop optimality in interpolation MPC	p. 131
Introduction	p. 131
Problem statement	p. 132
Predicted input/state trajectories	p. 133
Unconstrained optimal control law u[superscript o]	p. 134
Feasible control law u[superscript f]	p. 136
Interpolation MPC algorithms	p. 138
Comparison of open-loop optimality	p. 140
Closed-loop optimality properties	p. 141
Simulation example	p. 145
Conclusions	p. 148
Acknowledgment	p. 148
References	p. 149
	p. 151
Closed-loop predictions in model based predictive control of linear and nonlinear systems	p. 153
Introduction	p. 153
Review of earlier work	p. 155
MPC for linear uncertain systems	p. 158
Invariance/feasibility for nonlinear systems	p. 161
Numerical examples	p. 165
Application of Algorithm 1	p. 165
Application of Algorithm 2	p. 167
Acknowledgment	p. 171
References	p. 171
Computationally efficient nonlinear predictive control algorithm for control of constrained nonlinear systems	p. 173
Introduction	p. 173
Preliminaries	p. 175
Computationally efficient algorithm	p. 177
Examples	p. 179
Distillation dual composition control	p. 179
Tennessee-Eastman problem	p. 181
Conclusions	p. 184
Acknowledgment	p. 184
References	p. 185
Long-prediction-horizon nonlinear model predictive control	p. 189
Introduction	p. 189
Scope and preliminaries	p. 191
Optimisation problem: model predictive control law	p. 191
Nonlinear feedforward/state feedback design	p. 192
Nonlinear feedback controller design	p. 194
Application to linear processes	p. 195
Conclusions	p. 197
Acknowledgments	p. 197
References	p. 197
Appendix	p. 198
Proof of Theorem 1	p. 198
Proof of Theorem 2	p. 200
	p. 203
Nonlinear control of industrial processes	p. 205
Introduction	p. 205
Applying nonlinear control to industrial processes	p. 206
Quantitative needs assessment	p. 207
Technological and implementation issues	p. 208
Model predictive control of a spent acid recovery converter	p. 209
The process	p. 209
Process operation objectives	p. 210
A control perspective of the process	p. 211
Overall control strategy	p. 212
Process model development	p. 214
Control system design and implementation	p. 215
Control system performance	p. 216
Summary and conclusions	p. 219
Acknowledgment	p. 220
References	p. 220
Nonlinear model based predictive control using multiple local models	p. 223
Introduction	p. 224
Local model networks	p. 225
Nonlinear model based predictive control	p. 228
Local controller generalised predictive control (LC-GPC)	p. 229
Local model generalised predictive control (LM-GPC)	p. 230
Application	p. 232
pH neutralisation pilot plant	p. 232
Identification	p. 232
Control	p. 234
Discussion and conclusions	p. 238
References	p. 241
Neural network control of a gasoline engine with rapid sampling	p. 245
Introduction	p. 245
Artificial neural networks	p. 246
ANN engine model development	p. 248
Neural network based control	p. 250
Application of the ANN model based controller to the gasoline engine	p. 252
Conclusions	p. 253
References	p. 254
Index	p. 257
Table of Contents provided by Syndetics. All Rights Reserved.

Non-Linear Predictive Control : Theory and Practice

Theory and Practice

At a Glance

Hardcover

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