Model Predictive Control 1st Edition by Baocang Ding ,Yuanqing Yang ISBN 1119471397 978-1119471394

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Product details: 

ISBN 10:1119471397 

ISBN 13:978-1119471394

Author:Baocang Ding ,Yuanqing Yang

Model Predictive Control

Understand the practical side of controlling industrial processes

Model Predictive Control (MPC) is a method for controlling a process according to given parameters, derived in many cases from empirical models. It has been widely applied in industrial units to increase revenue and promoting sustainability. Systematic overviews of this subject, however, are rare, and few draw on direct experience in industrial settings.

Assuming basic knowledge of the relevant mathematical and algebraic modeling techniques, the book’s title combines foundational theories of MPC with a thorough sense of its practical applications in an industrial context. The result is a presentation uniquely suited to rapid incorporation in an industrial workplace.

Model Predictive Control readers will also find:

• Two-part organization to balance theory and applications
• Selection of topics directly driven by industrial demand
• An author with decades of experience in both teaching and industrial practice

This book is ideal for industrial control engineers and researchers looking to understand MPC technology, as well as advanced undergraduate and graduate students studying predictive control and related subjects.

Table of contents: 

1. Concepts
   PID and Model Predictive Control
   Two-Layered Model Predictive Control
   Hierarchical Model Predictive Control
2. Parameter Estimation and Output Prediction
   Test Signal for Model Identification
   Step Test
   White Noise
   Pseudo-Random Binary Sequence
   Generalized Binary Noise
   Step Response Model Identification
   Model
   Data Processing
   Marking or Interpolation of Bad Data
   Smoothing Data
   Model Identification
   Case Grouping
   Cased Data Preparation for Stable Dependent Variables
   Cased Data Preparation for Integral Dependent Variables
   Least Square Solution to Parameter Regression
   Least Square Solution by SVD Decomposition
   Filtering Pulse Response Coefficients
   Numerical Example
   Prediction Based on Step Response Model and Kalman Filter
   Steady-State Kalman Filter and Predictor
   Steady-State Kalman Filter and Predictor Based on Step Response Model
   Open-Loop Prediction of Stable CV
   Open-Loop Prediction of Integral CV
3. Steady-State Target Calculation
   RTO and External Target
   Economic Optimization and Target Tracking Problem
   Economic Optimization
   Optimization Problem
   Minimum-Move Problem
   Target Tracking Problem
   Judging Feasibility and Adjusting Soft Constraint
   Weight Method
   An Illustrative Example
   Weight Method
   Priority-Rank Method
   Ascending-Number Method
   Descending-Number Method
   Compromise Between Adjusting Soft Constraints and Economic Optimization
4. Two-Layered DMC for Stable Processes
   Open-Loop Prediction Module
   Steady-State Target Calculation Module
   Hard and Soft Constraints
   Priority Rank of Soft Constraints
   Feasibility Stage
   Economic Stage
   Dynamic Calculation Module
   Numerical Example
5. Two-Layered DMC for Stable and Integral Processes
   Open-Loop Prediction Module
   Steady-State Target Calculation Module
   Hard and Soft Constraints
   Priority Rank of Soft Constraints
   Feasibility Stage
   Economic Stage
   Dynamic Calculation Module
   Numerical Example
6. Two-Layered DMC for State-Space Model
   Artificial Disturbance Model
   Basic Model
   Controlled Variable as Additional State
   Manipulated Variable as Additional State
   Kalman Filter
   Open-Loop Prediction Module
   Steady-State Target Calculation Module
   Constraints on Steady-State Perturbation Increment
   Feasibility Stage
   Economic Stage Without Soft Constraint
   Dynamic Calculation Module
   Numerical Example
7. Offset-Free, Nonlinearity and Variable Structure in Two-Layered MPC
   State Space Steady-State Target Calculation with Target Tracking
   Case all External Targets Having Equal Importance
   Case CV External Target Being More Important Than MV External Target
   QP-Based Dynamic Control and Offset-Free
   Static Nonlinear Transformation
   Principle of Nonlinear Transformation
   Usual Nonlinear Transformations
   Nonlinear Transformation of Valve Output
   Piecewise Linear Transformation
   Two-Layered MPC with Varying Degree of Freedom
   Numerical Example Without Varying Structure
   Numerical Example with Varying Number of Manipulated Variables
   Numerical Example with Output Collinearity
8. Two-Step Model Predictive Control for Hammerstein Model
   Two-Step State Feedback MPC
   Stability of Two-Step State Feedback MPC
   Region of Attraction for Two-Step MPC: Semi-Global Stability
   System Matrix Having No Eigenvalue Outside of Unit Circle
   System Matrix Having Eigenvalues Outside of Unit Circle
   Numerical Example
   Two-Step Output Feedback Model Predictive Control
   Generalized Predictive Control: Basics
   Output Prediction
   Receding Horizon Optimization
   Dead-Beat Property of Generalized Predictive Control
   On-line Identification and Feedback Correction
   Two-Step Generalized Predictive Control
   Unconstrained Algorithm
   Algorithm with Input Saturation
   Stability Results Based on Popov’s Theorem
   Region of Attraction for Two-Step Generalized Predictive Control
   State Space Description
   Stability with Region of Attraction
   Computation of Region of Attraction
   Numerical Example
9. Heuristic Model Predictive Control for LPV Model
   A Heuristic Approach Based on Open-Loop Optimization
   Open-Loop MPC for Unmeasurable State
10. Robust Model Predictive Control
    A Cornerstone Method
    KBM Formula
    KBM Controller
    Example: Generalizing to Networked Control
    Closed-Loop Model for Double-Sided, Finite-Bounded, Arbitrary Packet Loss
    MPC for Double-Sided, Arbitrary Packet Loss
    Solution of MPC for Double-Sided Packet Loss
    Invariant Set Trap
    Prediction Horizon: Zero or One
    One Over Zero
    One: Generalizing to Networked Control
    Algorithm
    A Numerical Example
    Variant Feedback MPC
    About Optimality
    Constrained Linear Time-Varying Quadratic Regulation with Near-Optimal Solution
    Solving KBM Controller
    Solving Problem Without Terminal Cost
    Solving Problem with Terminal Cost
    Overall Algorithm and Analysis
    Numerical Example
    Alternatives with Nominal Performance Cost
    Problem Formulation
    Robust MPC Based on Partial Feedback Control
    Introducing Vertex Control Moves
    Numerical Example
    More Discussions
11. Output Feedback Robust Model Predictive Control
    Model and Controller Descriptions
    Controller for LPV Model
    Controller for Quasi-LPV Model
    Characterization of Stability and Optimality
    Review of Quadratic Boundedness
    Stability Condition
    Optimality Condition
    A Paradox for State Convergence
    General Optimization Problem
    Handling Physical Constraints
    Current Augmented State
    Some Usual Transformations
    Handling Double Convex Combinations
    Solutions to Output Feedback MPC
    Full Online Method for LPV
    Partial Online Method for LPV
    Relaxed Variables in Optimization Problem
    Alternative Forms Based on Congruence Transformation
    Description of Bound on True State

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Tags: Baocang Ding, Yuanqing Yang, Model, Predictive, Control