Hybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed integer optimization problems dynamically, is readily extendable to other industrial processes.The main topics of this book are:· hybrid predictive control (HPC) design based on evolutionary multiobjective optimization (EMO);· HPC based on EMO for dial-a-ride systems; and· HPC based on EMO for operational decisions in public transport systems.Hybrid Predictive Control for Dynamic Transport Problems is a comprehensive analysis of HPC and its application to dynamic transport systems. Introductory material on evolutionary algorithms is presented in summary in an appendix. The text will be of interest to control and transport engineers working on the operational optimization of transport systems and to academic researchers working with hybrid systems. The potential applications of the generic methods presented here to other process fields will make the book of interest to a wider group of researchers, scientists and graduate students working in other control-related disciplines.
This book develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems.
|
|
|
1 | (20) |
|
|
|
1 | (3) |
|
1.2 Hybrid Predictive Control Framework |
|
|
4 | (4) |
|
1.2.1 Hybrid Predictive Control (HPC) |
|
|
5 | (1) |
|
1.2.2 Multi-objective Optimization for Control |
|
|
6 | (2) |
|
1.3 The Optimization of Transport Systems |
|
|
8 | (13) |
|
1.3.1 Dial-a-Ride Systems |
|
|
8 | (6) |
|
1.3.2 Public Transport Systems |
|
|
14 | (7) |
|
2 Hybrid Predictive Control: Mono-objective and Multi-objective Design |
|
|
21 | (24) |
|
2.1 Hybrid Predictive Control Design |
|
|
21 | (12) |
|
2.1.1 Objective Functions for Hybrid Predictive Control |
|
|
23 | (3) |
|
2.1.2 Hybrid Predictive Control Based on a PWA Model |
|
|
26 | (1) |
|
2.1.3 Hybrid Predictive Control Based on Hybrid Fuzzy Models |
|
|
27 | (1) |
|
2.1.4 Optimization Methods for Hybrid Predictive Control |
|
|
28 | (5) |
|
2.2 Hybrid Predictive Control Based on Multi-objective Optimization |
|
|
33 | (9) |
|
2.2.1 Multi-objective Hybrid Predictive Control (MO-HPC) |
|
|
34 | (3) |
|
2.2.2 Dispatcher Criteria |
|
|
37 | (2) |
|
2.2.3 MO-HPC Solved Using Evolutionary Algorithms |
|
|
39 | (3) |
|
|
|
42 | (3) |
|
3 Hybrid Predictive Control for a Dial-a-Ride System |
|
|
45 | (50) |
|
3.1 Modeling a Dial-a-Ride System |
|
|
45 | (1) |
|
3.2 The State-Space Model |
|
|
45 | (5) |
|
3.3 The Objective Function |
|
|
50 | (5) |
|
3.4 The Demand Prediction Method |
|
|
55 | (4) |
|
3.5 Evolutionary Algorithms for Solving HPC in the Context of the Dial-a-Ride System |
|
|
59 | (11) |
|
3.5.1 The Reduction of Feasible Search Space: The No-Swapping Case |
|
|
61 | (2) |
|
3.5.2 HPC Based on GA for a Dial-a-Ride System |
|
|
63 | (7) |
|
3.6 Simulation Results for HPC Applied to a Dial-a-Ride System |
|
|
70 | (8) |
|
3.6.1 HPC with Demand Prediction |
|
|
70 | (5) |
|
3.6.2 HPC with Demand and Congestion Predictions |
|
|
75 | (3) |
|
3.7 Fault-Tolerant Control for a Dial-a-Ride System |
|
|
78 | (5) |
|
3.7.1 An FTC Procedure Based on Fuzzy Rules |
|
|
78 | (2) |
|
|
|
80 | (3) |
|
3.8 Multi-objective Hybrid Predictive Control for a Dial-a-Ride System |
|
|
83 | (6) |
|
3.8.1 MO-HPC for the Dial-a-Ride System |
|
|
86 | (2) |
|
|
|
88 | (1) |
|
|
|
89 | (6) |
|
4 Hybrid Predictive Control for Operational Decisions in Public Transport Systems |
|
|
95 | (32) |
|
4.1 Modeling a Public Transport System |
|
|
95 | (2) |
|
|
|
97 | (4) |
|
4.3 The Objective Function |
|
|
101 | (1) |
|
4.4 Evolutionary Algorithms for Solving HPC in the Context of the Public Transport System |
|
|
102 | (3) |
|
4.5 The Expert Control Algorithm |
|
|
105 | (2) |
|
4.6 Simulation Results for HPC Applied to a Public Transport System |
|
|
107 | (9) |
|
4.6.1 An Analysis of the Weighting Parameters in the Objective Function |
|
|
108 | (1) |
|
4.6.2 Illustrative Results |
|
|
109 | (7) |
|
4.7 Multi-objective Hybrid Predictive Control for a Public Transport System |
|
|
116 | (6) |
|
4.7.1 Description of the MO-HPC Strategy |
|
|
117 | (1) |
|
|
|
118 | (4) |
|
|
|
122 | (5) |
|
|
|
127 | (4) |
|
|
|
127 | (2) |
|
5.1.1 Evolutionary Algorithms for Hybrid Predictive Control |
|
|
127 | (1) |
|
5.1.2 HPC for a Dial-a-Ride System |
|
|
128 | (1) |
|
5.1.3 HPC for a Public Transport System |
|
|
129 | (1) |
|
|
|
129 | (2) |
|
|
|
131 | (26) |
|
A.1 Hybrid Predictive Control for Benchmark Systems: A Batch Reactor |
|
|
131 | (10) |
|
A.2 Hybrid Predictive Control for Benchmark Systems: A Tank System |
|
|
141 | (9) |
|
A.3 MO-HPC for Benchmark Systems: A Tank System |
|
|
150 | (7) |
| References |
|
157 | (8) |
| Index |
|
165 | |
Alfredo Nśńez received the M.Sc. and Dr. degrees in electrical engineering, from the Electrical Engineering Department, Universidad de Chile, Santiago, Chile, in 2007 and 2010, respectively. He is currently a postdoc researcher at Delft Center for Systems and Control, Delft University of Technology. His main research interests are in predictive control, hybrid systems and control of transport systems. Cristiįn Cortés obtained the M.Sc. degree in Civil Engineering at University of Chile in 1995, and his Ph.D. degree in Civil Engineering at University of California, Irvine in 2003. He is currently Assistant Professor at Civil Engineering Department, University of Chile. His research interests include public transport optimization, network optimization and equilibrium; simulation of transport system, control applied to dynamic transport problems. Dr. Cortés has published 22 papers in indexed ISI journals, and reports more than 50 citations in Conferences from different areas. From 2004, he has been a member of the Directory of the Chilean Society in Transport Engineering, and currently participates in several research projects at University of Chile funded by Government Agencies. Doris Sįez received the M.Sc. and Dr. degrees in electrical engineering from the Pontificia Universidad Católica de Chile, Santiago, in 1995 and 2000, respectively. She is currently an Assistant Professor at the Electrical Engineering Department, Universidad de Chile, Santiago. Her current research interests include fuzzy systems control design, fuzzy identification, predictive control, control of power generation plants, and control of transport systems. Dr. Sįez has authored and coauthored more than 50 technical papers in international journals and conferences, and is author of the book Optimization of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants (London: Springer-Verlag, 2002). Dr. Sįez is the Vice-president of the IEEE ChileanSection and a Co-Founder of the Chilean chapter of the IEEE Neural Networks Society.
