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Practical Design and Application of Model Predictive Control

Practical Design and Application of Model Predictive Control
  • Author : Nassim Khaled
  • Publsiher : Butterworth-Heinemann
  • Release : 04 May 2018
  • ISBN : 0128139196
  • Pages : 262 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKPractical Design and Application of Model Predictive Control

Summary:
Practical Design and Application of Model Predictive Control is a self-learning resource on how to design, tune and deploy an MPC using MATLAB® and Simulink®. This reference is one of the most detailed publications on how to design and tune MPC controllers. Examples presented range from double-Mass spring system, ship heading and speed control, robustness analysis through Monte-Carlo simulations, photovoltaic optimal control, and energy management of power-split and air-handling control. Readers will also learn how to embed the designed MPC controller in a real-time platform such as Arduino®. The selected problems are nonlinear and challenging, and thus serve as an excellent experimental, dynamic system to show the reader the capability of MPC. The step-by-step solutions of the problems are thoroughly documented to allow the reader to easily replicate the results. Furthermore, the MATLAB® and Simulink® codes for the solutions are available for free download. Readers can connect with the authors through the dedicated website which includes additional free resources at www.practicalmpc.com. Illustrates how to design, tune and deploy MPC for projects in a quick manner Demonstrates a variety of applications that are solved using MATLAB® and Simulink® Bridges the gap in providing a number of realistic problems with very hands-on training Provides MATLAB® and Simulink® code solutions. This includes nonlinear plant models that the reader can use for other projects and research work Presents application problems with solutions to help reinforce the information learned


Practical Design and Application of Model Predictive Control

Practical Design and Application of Model Predictive Control
  • Author : Nassim Khaled,Bibin Pattel
  • Publisher : Butterworth-Heinemann
  • Release : 04 May 2018
GET THIS BOOKPractical Design and Application of Model Predictive Control

Practical Design and Application of Model Predictive Control is a self-learning resource on how to design, tune and deploy an MPC using MATLAB® and Simulink®. This reference is one of the most detailed publications on how to design and tune MPC controllers. Examples presented range from double-Mass spring system, ship heading and speed control, robustness analysis through Monte-Carlo simulations, photovoltaic optimal control, and energy management of power-split and air-handling control. Readers will also learn how to embed the designed MPC


Practical Design and Application of Model Predictive Control

Practical Design and Application of Model Predictive Control
  • Author : Nassim Khaled,Bibin Pattel
  • Publisher : Butterworth-Heinemann
  • Release : 09 May 2018
GET THIS BOOKPractical Design and Application of Model Predictive Control

Practical Design and Application of Model Predictive Control is a self-learning resource on how to design, tune and deploy an MPC using MATLAB(R) and Simulink(R). This reference is one of the most detailed publications on how to design and tune MPC controllers. Examples presented range from double-Mass spring system, ship heading and speed control, robustness analysis through Monte-Carlo simulations, photovoltaic optimal control, and energy management of power-split and air-handling control. Readers will also learn how to embed the


Model-Based Predictive Control

Model-Based Predictive Control
  • Author : J.A. Rossiter
  • Publisher : CRC Press
  • Release : 12 July 2017
GET THIS BOOKModel-Based Predictive Control

Model Predictive Control (MPC) has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. Until now, no book has addressed in detail all key issues in the field including apriori stability and robust stability results. Engineers and MPC researchers now have a volume that provides a complete overview of the theory and practice of MPC as it relates to process and control engineering. Model-Based Predictive Control, A Practical Approach, analyzes predictive


Multivariable Predictive Control

Multivariable Predictive Control
  • Author : Sandip K. Lahiri
  • Publisher : John Wiley & Sons
  • Release : 23 October 2017
GET THIS BOOKMultivariable Predictive Control

A guide to all practical aspects of building, implementing, managing, and maintaining MPC applications in industrial plants Multivariable Predictive Control: Applications in Industry provides engineers with a thorough understanding of all practical aspects of multivariate predictive control (MPC) applications, as well as expert guidance on how to derive maximum benefit from those systems. Short on theory and long on step-by-step information, it covers everything plant process engineers and control engineers need to know about building, deploying, and managing MPC applications



Predictive Control

Predictive Control
  • Author : Yugeng Xi,Dewei Li
  • Publisher : John Wiley & Sons
  • Release : 16 September 2019
GET THIS BOOKPredictive Control

This book is a comprehensive introduction to model predictive control (MPC), including its basic principles and algorithms, system analysis and design methods, strategy developments and practical applications. The main contents of the book include an overview of the development trajectory and basic principles of MPC, typical MPC algorithms, quantitative analysis of classical MPC systems, design and tuning methods for MPC parameters, constrained multivariable MPC algorithms and online optimization decomposition methods. Readers will then progress to more advanced topics such as


Nonlinear Model Predictive Control

Nonlinear Model Predictive Control
  • Author : Frank Allgöwer,Alex Zheng
  • Publisher : Springer Science & Business Media
  • Release : 01 March 2000
GET THIS BOOKNonlinear Model Predictive Control

During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC).


Model Predictive Control

Model Predictive Control
  • Author : Eduardo F. Camacho,Carlos Bordons,Carlos Bordons Alba
  • Publisher : Boom Koninklijke Uitgevers
  • Release : 05 March 2021
GET THIS BOOKModel Predictive Control

The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. The book demonstrates that a powerful technique does not always require complex control algorithms. Many new exercises and examples have also been added throughout. Solutions available for download from the authors' website save the tutor time


Economic Model Predictive Control

Economic Model Predictive Control
  • Author : Matthew Ellis,Jinfeng Liu,Panagiotis D. Christofides
  • Publisher : Springer
  • Release : 27 July 2016
GET THIS BOOKEconomic Model Predictive Control

This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear


New Directions on Model Predictive Control

New Directions on Model Predictive Control
  • Author : Jinfeng Liu,Helen E Durand
  • Publisher : Anonim
  • Release : 05 March 2021
GET THIS BOOKNew Directions on Model Predictive Control

Model predictive control (MPC) is an advanced control design used in many industries worldwide. An MPC selects control actions which are optimal with respect to a given performance metric as well as any physically-motivated constraints. MPC has therefore gained significant research attention over the past several decades. Advances in MPC continue to unlock its potential to solve a wide variety of practical issues. This book presents some of the state-of-the-art in MPC design from theoretical and applications perspectives. It covers


Model Predictive Control

Model Predictive Control
  • Author : Ridong Zhang,Anke Xue,Furong Gao
  • Publisher : Springer
  • Release : 14 August 2018
GET THIS BOOKModel Predictive Control

This monograph introduces the authors’ work on model predictive control system design using extended state space and extended non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis, model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical process control and


Handbook of Model Predictive Control

Handbook of Model Predictive Control
  • Author : Saša V. Raković,William S. Levine
  • Publisher : Springer
  • Release : 01 September 2018
GET THIS BOOKHandbook of Model Predictive Control

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


Networked and Distributed Predictive Control

Networked and Distributed Predictive Control
  • Author : Panagiotis D. Christofides,Jinfeng Liu,David Muñoz de la Peña
  • Publisher : Springer Science & Business Media
  • Release : 07 April 2011
GET THIS BOOKNetworked and Distributed Predictive Control

Networked and Distributed Predictive Control presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems – the first book to do so. The design of model predictive control systems using Lyapunov-based techniques accounting for the influence of asynchronous and delayed measurements is followed by a treatment of networked control architecture development. This shows how networked control can augment dedicated control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to


Nonlinear Model Predictive Control

Nonlinear Model Predictive Control
  • Author : Lars Grüne,Jürgen Pannek
  • Publisher : Springer Science & Business Media
  • Release : 11 April 2011
GET THIS BOOKNonlinear Model Predictive Control

Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants. An