1st Edition

Modern Predictive Control

By Ding Baocang Copyright 2010
    286 Pages 41 B/W Illustrations
    by CRC Press

    286 Pages 41 B/W Illustrations
    by CRC Press

    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.

    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

    Biography

    Ding Baocang