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Distributed Model Predictive Control Made Easy - José M. Maestre & Rudy R. Negenborn

Written By Alexis Llontop on lunes, 12 de enero de 2015 | 18:55

The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers.

The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems.

This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those who want to gain a deeper insight in the wide range of distributed MPC techniques available.

Table of Contents: [601 Pag.]

1. On 35 Approaches for Distributed MPC Made Easy

Part I: From Small-Scale to Large-Scale: The Group of Autonomous Systems Perspective
2. Bargaining Game Based Distributed MPC
3. Cooperative Tube-Based Distributed MPC for Linear Uncertain Systems Coupled Via Constraints
4. Price-Driven Coordination for Distributed NMPC Using a Feedback Control Law
5. Distributed MPC for Consensus and Synchronization
6. Distributed MPC Under Coupled Constraints Based on Dantzig-Wolfe Decomposition
7. Distributed MPC Via Dual Decomposition and Alternative Direction Method of Multipliers
8. D-SIORHC, Distributed MPC with Stability Constraints Based on a Game Approach
9. A Distributed-in-Time NMPC-Based Coordination Mechanism for Resource Sharing Problems
10. Rate Analysis of Inexact Dual Fast Gradient Method for Distributed MPC
11. Distributed MPC Via Dual Decomposition
12. Distributed Optimization for MPC of Linear Dynamic Networks
13. Adaptive Quasi-Decentralized MPC of Networked Process Systems
14. Distributed Lyapunov-Based MPC
15. A Distributed Reference Management Scheme in Presence of Non-Convex Constraints: An MPC Based Approach
16. The Distributed Command Governor Approach in a Nutshell
17. Mixed-Integer Programming Techniques in Distributed MPC Problems
18. Distributed MPC of Interconnected Nonlinear Systems by Dynamic Dual Decomposition
19. Generalized Accelerated Gradient Methods for Distributed MPC Based on Dual Decomposition
20. Distributed Multiple Shooting for Large Scale Nonlinear Systems
21. Nash-Based Distributed MPC for Multi-Rate Systems

Part II From Large-Scale to Small-Scale: The Decomposed Monolithic System Perspective

22. Cooperative Dynamic MPC for Networked Control Systems
23. Parallel Implementation of Hybrid MP
24. A Hierarchical MPC Approach with Guaranteed Feasibility for Dynamically Coupled Linear Systems
25. Distributed MPC Based on a Team Game
26. Distributed MPC: A Noncooperative Approach Based on Robustness Concepts
27. Decompositions of Augmented Lagrange Formulations for Serial and Parallel Distributed MPC
28. A Hierarchical Distributed MPC Approach: A Practical Implementation
29. Distributed MPC Based on Agent Negotiation .
30. Lyapunov-based Distributed MPC Schemes: Sequential and Iterative Approaches
31. Multi-layer Decentralized MPC of Large-Scale Networked Systems
32. Distributed MPC Using Reinforcement Learning Based Negotiation: Application to Large Scale Systems
33. Hierarchical MPC for Multiple Commodity Transportation Networks
34. On the Use of Suboptimal Solvers for Efficient Cooperative Distributed Linear MPC
35. Cooperative Distributed MPC Integrating a Steady State Target Optimizer
36. Cooperative MPC with Guaranteed Exponential Stability

Capture:
Distributed Model Predictive Control Made Easy (Intelligent Systems, Control and Automation: Science and Engineering) by José M. Maestre and Rudy R. Negenborn
English | 2014 | ISBN: 9400770057 | 600 pages | PDF | 14,7 MB


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