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Distributed Model Predictive Control Made Easy

ISCASE: DISTRIBUTED MODEL PREDICTIVE CONTROL MADE EASY

Engineering

Mathematics & Science

Gt Product Group

Binding Format

Marketing Imprint Name

B2B GT

CL Carton Count

Imprint Name

B2B & B2C

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.

POST SECONDARY FOUR YEAR

COLFR

COLSO

COLJR

COLSR

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Editorial Board.
Title Page.
Copyright Page.
Other Frontmatter.
Preface.
Contents.
Contributors.
1: On 35 Approaches for Distributed MPC Made Easy.
2: From Small-Scale to Large-Scale: The Group of Autonomous Systems Perspective.
3: Bargaining Game Based Distributed MPC.
4: Cooperative Tube-Based Distributed MPC for Linear Uncertain Systems Coupled Via Constraints.
5: Price-Driven Coordination for Distributed NMPC Using a Feedback Control Law.
6: Distributed MPC for Consensus and Synchronization.
7: Distributed MPC Under Coupled Constraints Based on Dantzig-Wolfe Decomposition.
8: Distributed MPC Via Dual Decomposition and Alternative Direction Method of Multipliers.
9: D-SIORHC, Distributed MPC with Stability Constraints Based on a Game Approach.
10: A Distributed-in-Time NMPC-Based Coordination Mechanism for Resource Sharing Problems.
11: Rate Analysis of Inexact Dual Fast Gradient Method for Distributed MPC.
12: Distributed MPC Via Dual Decomposition.
13: Distributed Optimization for MPC of Linear Dynamic Networks.
14: Adaptive Quasi-Decentralized MPC of Networked Process Systems.
15: Distributed Lyapunov-Based MPC.
16: A Distributed Reference Management Scheme in Presence of Non-Convex Constraints: An MPC Based Approach.
17: The Distributed Command Governor Approach in a Nutshell.
18: Mixed-Integer Programming Techniques in Distributed MPC Problems.
19: Distributed MPC of Interconnected Nonlinear Systems by Dynamic Dual Decomposition.
20: Generalized Accelerated Gradient Methods for Distributed MPC Based on Dual Decomposition.
21: Distributed Multiple Shooting for Large Scale Nonlinear Systems.
22: Nash-Based Distributed MPC for Multi-Rate Systems.
23: From Large-Scale to Small-Scale: The Decomposed Monolithic System Perspective.
24: Cooperative Dynamic MPC for Networked Control Systems.
25: Parallel Implementation of Hybrid MPC.
26: A Hierarchical MPC Approach with Guaranteed Feasibility for Dynamically Coupled Linear Systems.
27: Distributed MPC Based on a Team Game.
28: Distributed MPC: A Noncooperative Approach Based on Robustness Concepts.
29: Decompositions of Augmented Lagrange Formulations for Serial and Parallel Distributed MPC.
30: A Hierarchical Distributed MPC Approach: A Practical Implementation.
31: Distributed MPC Based on Agent Negotiation.
32: Lyapunov-Based Distributed MPC Schemes: Sequential and Iterative Approaches.
33: Multi-layer Decentralized MPC of Large-scale Networked Systems.
34: Distributed MPC Using Reinforcement Learning Based Negotiation: Application to Large Scale Systems.
35: Hierarchical MPC for Multiple Commodity Transportation Networks.
36: On the Use of Suboptimal Solvers for Efficient Cooperative Distributed Linear MPC.
37: Cooperative Distributed MPC Integrating a Steady State Target Optimizer.
38: Cooperative MPC with Guaranteed Exponential Stability.

Distributed Model Predictive Control Made Easy.css-1p8rvzy{position:absolute;padding-left:8px;}

Distributed Model Predictive Control Made Easy