Modern Predictive Control

Ding (Chongqing University Baocang

ISBN 10: 1420085301 / ISBN 13: 9781420085303

Published by Taylor & Francis Inc Nov 2009, 2009

About this Item

Neuware - Model predictive control (MPC) differs from other control methods mainly in its implementation of a control action. Usually, MPC solves a finite-horizon optimal control problem at each sampling instant, so that the control actions for the current and a period of future time are obtained. However, only the current control move is applied to the plant. This work explains the how this useful model permits the flexibility of acting while optimizing. It details a variety of models for one and two-step control, explains state feedback synthesis approaches with and without a finite switching horizon, and open and closed-loop optimization, as well as output feedback synthesis approaches.

Seller Inventory # 9781420085303

Bibliographic Details

• Title: Modern Predictive Control
• Publisher: Taylor & Francis Inc Nov 2009
• Publication Date: 2009
• Language: English
• ISBN 10: 1420085301
• ISBN 13: 9781420085303
• Binding: Buch
• Condition: Neu
• Dimensions: 236W x 163H x 18D millimeters 236 millimeters width by 163 millimeters height by 18 millimeters depth
• Weight: 546 grams

About this title

Synopsis

Modern Predictive Control explains how MPC differs from other control methods in its implementation of a control action. Most importantly, MPC provides the flexibility to act while optimizing―which is essential to the solution of many engineering problems in complex plants, where exact modeling is impossible.

The superiority of MPC is in its numerical solution. Usually, MPC is employed to solve a finite-horizon optimal control problem at each sampling instant and obtain control actions for both the present time and a future period. However, only the current control move is applied to the plant.

This complete, step-by-step exploration of various approaches to MPC:

• Introduces basic concepts of systems, modeling, and predictive control, detailing development from classical MPC to synthesis approaches
• Explores use of Model Algorithmic Control (MAC), Dynamic Matrix Control (DMC), Generalized Predictive Control (GPC), and Two-Step Model Predictive Control
• Identifies important general approaches to synthesis
• Discusses open-loop and closed-loop optimization in synthesis approaches
• Covers output feedback synthesis approaches with and without a finite switching horizon

This book gives researchers a variety of models for use with one- and two-step control. The author clearly explains the variations between predictive control methods―and the root of these differences―to illustrate that there is no one ideal MPC and that one should remain open to selecting the best possible model in each unique circumstance.

About the Author

Ding Baocang