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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
Preface | p. xi |
Contributors | p. xiii |
Part I p. 1 | |
1 Review of nonlinear model predictive control applications | p. 3 |
1.1 Introduction | p. 3 |
1.2 Theoretical foundations of NMPC | p. 6 |
1.3 Industrial implementations of NMPC | p. 9 |
1.3.1 Models | p. 9 |
1.3.2 Output feedback | p. 15 |
1.3.3 Steady-state optimisation | p. 15 |
1.3.4 Dynamic optimisation | p. 16 |
1.3.5 Constraint formulations | p. 16 |
1.3.6 Output trajectories | p. 17 |
1.3.7 Output horizon and input parameterisation | p. 18 |
1.3.8 Solution methods | p. 19 |
1.4 NMPC application examples | p. 19 |
1.4.1 PFC: application to batch reactors | p. 20 |
1.4.2 Aspen Target: application to a pulverised coal fired boiler | p. 20 |
1.4.3 MVC: application to an ammonia plant | p. 21 |
1.4.4 NOVA-NLC: application to a polymerisation process | p. 22 |
1.4.5 Process Perfecter: application to a polypropylene process | p. 24 |
1.5 Future needs for NMPC technology development | p. 27 |
1.5.1 Model development | p. 27 |
1.5.2 Output feedback | p. 28 |
1.5.3 Optimisation methods | p. 28 |
1.5.4 User interface | p. 29 |
1.5.5 Justification of NMPC | p. 29 |
1.5.6 Other issues | p. 29 |
1.6 Conclusions | p. 29 |
1.7 References | p. 30 |
1.8 Notes | p. 32 |
2 Nonlinear model predictive control: issues and applications | p. 33 |
2.1 Introduction | p. 33 |
2.2 Exploiting model structure | p. 34 |
2.2.1 Motivation | p. 34 |
2.2.2 Model identification | p. 35 |
2.2.3 Controller synthesis | p. 36 |
2.2.4 Application: a continuous bioreactor | p. 38 |
2.3 Efficient dynamic optimisation using differential flatness | p. 39 |
2.3.1 Motivation | p. 39 |
2.3.2 Problem formulation | p. 40 |
2.3.3 Application: biomass optimisation | p. 41 |
2.4 Model-based control of population balance systems | p. 43 |
2.4.1 Motivation: emulsion polymerisation | p. 43 |
2.4.2 Model development | p. 44 |
2.4.3 Numerical solutions of the population balance equation | p. 45 |
2.4.4 Approaches to control | p. 45 |
2.4.5 Measurement and feedback | p. 46 |
2.4.6 Batch polymerisation example | p. 47 |
2.5 Disturbance estimation | p. 48 |
2.5.1 Motivation | p. 48 |
2.5.2 Estimation formulation | p. 49 |
2.5.3 Application: chemical reactor disturbance estimation | p. 51 |
2.6 Conclusions | p. 51 |
2.7 Acknowledgments | p. 53 |
2.8 References | p. 53 |
2.9 Notes | p. 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 Introduction | p. 61 |
3.2 Preliminaries and state-feedback control | p. 63 |
3.3 Output feedback | p. 66 |
3.4 Tracking and disturbance rejection for signals generated by an exosystem | p. 68 |
3.5 Tracking 'asymptotically' constant references | p. 72 |
3.5.1 State-space models | p. 73 |
3.5.2 Nonlinear ARX models | p. 75 |
3.6 Conclusions | p. 77 |
3.7 Acknowledgment | p. 77 |
3.8 References | p. 78 |
4 Model predictive control of nonlinear parameter varying systems via receding horizon control Lyapunov functions | p. 81 |
4.1 Introduction | p. 81 |
4.2 Preliminaries | p. 84 |
4.2.1 Notation and definitions | p. 84 |
4.2.2 Quadratic regulator problem for NLPV systems | p. 85 |
4.3 Equivalent finite horizon regulation problem | p. 86 |
4.4 Modified receding horizon controller | p. 89 |
4.5 Selecting suitable CLFs | p. 91 |
4.5.1 Autonomous systems | p. 92 |
4.5.2 Linear parameter varying systems | p. 93 |
4.6 Connections with other approaches | p. 96 |
4.7 Incorporating constraints | p. 97 |
4.8 Illustrative examples | p. 98 |
4.9 Conclusions | p. 103 |
4.10 Acknowledgments | p. 103 |
4.11 References | p. 103 |
4.12 Appendix: SDRE approach to nonlinear regulation | p. 105 |
5 Nonlinear model-algorithmic control for multivariable nonminimum-phase processes | p. 107 |
5.1 Introduction | p. 107 |
5.2 Preliminaries | p. 109 |
5.2.1 Relative order | p. 110 |
5.2.2 Zero dynamics and minimum-phase behaviour | p. 111 |
5.3 Brief review of nonlinear model-algorithmic control | p. 112 |
5.4 Model-algorithmic control with nonminimum-phase compensation using synthetic outputs | p. 114 |
5.5 Construction of statically equivalent outputs with pre-assigned transmission zeros | p. 116 |
5.5.1 Construction of independent functions which vanish on the equilibrium manifold | p. 117 |
5.5.2 A class of statically equivalent outputs | p. 119 |
5.5.3 Assignment of transmission zeros | p. 120 |
5.6 Application: control of a nonminimum-phase chemical reactor | p. 122 |
5.7 Conclusion | p. 128 |
5.8 References | p. 128 |
5.9 Appendix | p. 129 |
5.9.1 Proof of Proposition 1 | p. 129 |
5.9.2 Proof of Lemma 1 | p. 130 |
6 Open-loop and closed-loop optimality in interpolation MPC | p. 131 |
6.1 Introduction | p. 131 |
6.2 Problem statement | p. 132 |
6.3 Predicted input/state trajectories | p. 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 algorithms | p. 138 |
6.4.1 Comparison of open-loop optimality | p. 140 |
6.4.2 Closed-loop optimality properties | p. 141 |
6.5 Simulation example | p. 145 |
6.6 Conclusions | p. 148 |
6.7 Acknowledgment | p. 148 |
6.8 References | p. 149 |
Part III p. 151 | |
7 Closed-loop predictions in model based predictive control of linear and nonlinear systems | p. 153 |
7.1 Introduction | p. 153 |
7.2 Review of earlier work | p. 155 |
7.3 MPC for linear uncertain systems | p. 158 |
7.4 Invariance/feasibility for nonlinear systems | p. 161 |
7.5 Numerical examples | p. 165 |
7.5.1 Application of Algorithm 1 | p. 165 |
7.5.2 Application of Algorithm 2 | p. 167 |
7.6 Acknowledgment | p. 171 |
7.7 References | p. 171 |
8 Computationally efficient nonlinear predictive control algorithm for control of constrained nonlinear systems | p. 173 |
8.1 Introduction | p. 173 |
8.2 Preliminaries | p. 175 |
8.3 Computationally efficient algorithm | p. 177 |
8.4 Examples | p. 179 |
8.4.1 Distillation dual composition control | p. 179 |
8.4.2 Tennessee-Eastman problem | p. 181 |
8.5 Conclusions | p. 184 |
8.6 Acknowledgment | p. 184 |
8.7 References | p. 185 |
9 Long-prediction-horizon nonlinear model predictive control | p. 189 |
9.1 Introduction | p. 189 |
9.2 Scope and preliminaries | p. 191 |
9.3 Optimisation problem: model predictive control law | p. 191 |
9.4 Nonlinear feedforward/state feedback design | p. 192 |
9.5 Nonlinear feedback controller design | p. 194 |
9.6 Application to linear processes | p. 195 |
9.7 Conclusions | p. 197 |
9.8 Acknowledgments | p. 197 |
9.9 References | p. 197 |
9.10 Appendix | p. 198 |
9.10.1 Proof of Theorem 1 | p. 198 |
9.10.2 Proof of Theorem 2 | p. 200 |
Part IV p. 203 | |
10 Nonlinear control of industrial processes | p. 205 |
10.1 Introduction | p. 205 |
10.2 Applying nonlinear control to industrial processes | p. 206 |
10.2.1 Quantitative needs assessment | p. 207 |
10.2.2 Technological and implementation issues | p. 208 |
10.3 Model predictive control of a spent acid recovery converter | p. 209 |
10.3.1 The process | p. 209 |
10.3.2 Process operation objectives | p. 210 |
10.3.3 A control perspective of the process | p. 211 |
10.3.4 Overall control strategy | p. 212 |
10.3.5 Process model development | p. 214 |
10.3.6 Control system design and implementation | p. 215 |
10.3.7 Control system performance | p. 216 |
10.4 Summary and conclusions | p. 219 |
10.5 Acknowledgment | p. 220 |
10.6 References | p. 220 |
11 Nonlinear model based predictive control using multiple local models | p. 223 |
11.1 Introduction | p. 224 |
11.2 Local model networks | p. 225 |
11.3 Nonlinear model based predictive control | p. 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 Application | p. 232 |
11.4.1 pH neutralisation pilot plant | p. 232 |
11.4.2 Identification | p. 232 |
11.4.3 Control | p. 234 |
11.5 Discussion and conclusions | p. 238 |
11.6 References | p. 241 |
12 Neural network control of a gasoline engine with rapid sampling | p. 245 |
12.1 Introduction | p. 245 |
12.2 Artificial neural networks | p. 246 |
12.3 ANN engine model development | p. 248 |
12.4 Neural network based control | p. 250 |
12.4.1 Application of the ANN model based controller to the gasoline engine | p. 252 |
12.5 Conclusions | p. 253 |
12.6 References | p. 254 |
Index | p. 257 |