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This book is indexed in
Engineering » Control Engineering
Advanced Model Predictive Control
Edited by Tao Zheng, ISBN 978-953-307-298-2, Hard cover, 418 pages, Publisher: InTech, Chapters published July 05, 2011 under CC BY-NC-SA 3.0 license
DOI: 10.5772/685
Model Predictive Control (MPC) refers to a class of control algorithms in which a dynamic process model is used to predict and optimize process performance. From lower request of modeling accuracy and robustness to complicated process plants, MPC has been widely accepted in many practical fields. As the guide for researchers and engineers all over the world concerned with the latest developments of MPC, the purpose of "Advanced Model Predictive Control" is to show the readers the recent achievements in this area. The first part of this exciting book will help you comprehend the frontiers in theoretical research of MPC, such as Fast MPC, Nonlinear MPC, Distributed MPC, Multi-Dimensional MPC and Fuzzy-Neural MPC. In the second part, several excellent applications of MPC in modern industry are proposed and efficient commercial software for MPC is introduced. Because of its special industrial origin, we believe that MPC will remain energetic in the future.
- Chapter 1
Fast Model Predictive Control and its Application to Energy Management of Hybrid Electric Vehicles - Chapter 2
Fast Nonlinear Model Predictive Control using Second Order Volterra Models Based Multi-agent Approach - Chapter 3
Improved Nonlinear Model Predictive Control Based on Genetic Algorithm - Chapter 4
Distributed Model Predictive Control Based on Dynamic Games - Chapter 5
Efficient Nonlinear Model Predictive Control for Affine System - Chapter 6
Implementation of Multi-dimensional Model Predictive Control for Critical Process with Stochastic Behavior - Chapter 7
Fuzzy–neural Model Predictive Control of Multivariable Processes - Chapter 8
Using Subsets Sequence to Approach the Maximal Terminal Region for MPC - Chapter 9
Model Predictive Control for Block-oriented Nonlinear Systems with Input Constraints - Chapter 10
A General Lattice Representation for Explicit Model Predictive Control - Chapter 11
Model Predictive Control Strategies for Batch Sugar Crystallization Process - Chapter 12
Predictive Control for Active Model and Its Applications on Unmanned Helicopters - Chapter 13
Nonlinear Autoregressive with Exogenous Inputs Based Model Predictive Control for Batch Citronellyl Laurate Esterification Reactor - Chapter 14
Using Model Predictive Control for Local Navigation of Mobile Robots - Chapter 15
Model Predictive Control and Optimization for Papermaking Processes - Chapter 16
Gust Alleviation Control Using Robust MPC - Chapter 17
MBPC – Theoretical Development for Measurable Disturbances and Practical Example of Air-path in a Diesel Engine - Chapter 18
BrainWave®: Model Predictive Control for the Process Industries
