Handbook of Model Predictive Control / edited by Saša V. Raković, William S. Levine.

Format:

Book

Contributor:

Raković, Saša V., editor.

Levine, W. S., editor.

SpringerLink (Online service)

Series:

Engineering (Springer-11647)

Control Engineering,. 2373-7719

Control Engineering, 2373-7719

Language:

English

Subjects (All):

System theory.

Automatic control.

Applied mathematics.

Engineering mathematics.

Engineering design.

Computer simulation.

Systems Theory, Control.

Control and Systems Theory.

Mathematical and Computational Engineering.

Engineering Design.

Simulation and Modeling.

Local Subjects:

Systems Theory, Control.

Control and Systems Theory.

Mathematical and Computational Engineering.

Engineering Design.

Simulation and Modeling.

Physical Description:

1 online resource (XXI, 692 pages) : 154 illustrations, 97 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Birkhäuser, 2019.

System Details:

text file PDF

Summary:

Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today. The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using "computationally intensive controls," so the second part of this book addresses the solution of optimization problems in "real" time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance. The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading.

Contents:

Theory

The Essentials of Model Predictive Control (MPC)

Dynamic Programming and MPC

Set-valued and Lyapunov Methods for MPC

Stochastic Model Predictive Control

Moving Horizon Estimation

Probing and Duality in Stochastic Model Predictive Control

Economic Model Predictive Control: Some Design Tools and Analysis Techniques

Nonlinear Predictive Control for Trajectory Tracking and Path Following: An Introduction and Perspective

Hybrid Model Predictive Control

Model Predictive Control of Polynomial Systems

Distributed MPC for Large-Scale Systems

Scalable MPC Design

Computations

Efficient Convex Optimization for Linear MPC

Implicit Non-Convex Model Predictive Control

Convexification and Real-Time Optimization for MPC with Aerospace Applications

Explicit (Offline) Optimization for MPC

Real-Time Implementation of Explicit Model Predictive Control

Robust Optimization for MPC

Scenario Optimization for MPC

Nonlinear Programming Formulations for Nonlinear and Economic Model Predictive Control

Applications

Automotive Applications of Model Predictive Control

Applications of MPC in the Area of Health Care

Model Predictive Control for Power Electronics Applications

Learning-based Fast Nonlinear Model Predictive Control for Custom-made 3D Printed Ground and Aerial Robots

Applications of MPC to Building HVAC Systems

Toward Multi-Layered MPC for Complex Electric Energy Systems

Applications of MPC to Finance.

Other Format:

Printed edition:

ISBN:

978-3-319-77489-3

9783319774893

OCLC:

1050448474

Access Restriction:

Restricted for use by site license.