Modern Predictive Control  book cover
SAVE
$17.99
1st Edition

Modern Predictive Control





ISBN 9781138117693
Published October 6, 2017 by CRC Press
286 Pages 41 B/W Illustrations

 
SAVE ~ $17.99
was $89.95
USD $71.96

Prices & shipping based on shipping country


Preview

Book Description

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.

Table of Contents

Systems, modeling and model predictive control

Systems

Modeling

State space model and input/output model

Discretization of continuous-time systems

Model predictive control (MPC) and its basic properties

Three typical optimal control problems of MPC

Finite-horizon control: an example based on "three principles"

Infinite-horizon control: an example of dual-mode suboptimal control

Development from classical MPC to synthesis approaches

 

Model algorithmic control (MAC)

Principle of MAC

Constraint handling

The usual pattern for implementation of MPC

 

Dynamic matrix control (DMC)

Step response model and its identification

Principle of DMC

Constraint handling

 

Generalized predictive control (GPC)

Principle of GPC

Some basic properties

Stability results not related to the concrete model coefficients

Cases of multivariable systems and constrained systems

GPC with terminal equality constraint

 

Two-step model predictive control

Two-step GPC

Stability of two-step GPC

Region of attraction by using two-step GPC

Two-step state feedback MPC (TSMPC)

Stability of TSMPC

Design of the region of attraction of TSMPC based on semiglobal stability

Two-step output feedback model predictive control (TSOFMPC)

Stability of TSOFMPC

TSOFMPC: case where the intermediate variable is available

 

Sketch of synthesis approaches of MPC

General idea: case discrete-time systems

General idea: case continuous-time systems

Realizations

General idea: case uncertain systems (robust MPC)

Robust MPC based on closed-loop optimization

A concrete realization: case continuous-time nominal systems

 

State feedback synthesis approaches

System with polytopic description, linear matrix inequality

On-line approach based on min-max performance cost: case zero-horizon

Off-line approach based on min-max performance cost: case zero-horizon

Off-line approach based on min-max performance cost: case varying-horizon

Off-line approach based on nominal performance cost: case zero-horizon

Off-line approach based on nominal performance cost: case varying-horizon

 

Synthesis approaches with finite switching horizon

Standard approach for nominal systems

Optimal solution to infinite-horizon constrained linear quadratic control utilizing synthesis approach of MPC

On-line approach for nominal systems

Quasi-optimal solution to the infinite-horizon constrained linear time-varying quadratic regulation utilizing synthesis approach of MPC

On-line approach for systems with polytopic description

Parameter-dependent on-line approach for systems with polytopic description

 

Open-loop optimization and closed-loop optimization in synthesis approaches

A simple approach based on partial closed-loop optimization

Triple-mode approach

Mixed approach

Approach based on single-valued open-loop optimization and its deficiencies

Approach based on parameter-dependent open-loop optimization and its properties

Approach with unit switching horizon

 

Output feedback synthesis approaches

Optimization problem: case systems with input-output (I/O) nonlinearities

Conditions for stability and feasibility: case systems with I/O nonlinearities

Realization algorithm: case systems with I/O nonlinearities

Optimization problem: case systems with polytopic description

Optimality, invariance and constraint handling: case systems with polytopic description

Realization algorithm: case systems with polytopic description

 

Bibliography

 

Index

...
View More