| 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 |
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