1 Concepts 1 -- 1.1 PID and Model Predictive Control -- 1.2 Two-Layered Model Predictive Control -- 1.3 Hierarchical Model Predictive Control -- 2 Parameter Estimation and Output Prediction -- 2.1 Test Signal for Model Identification -- 2.1.1 Step Test -- 2.1.2 White Noise -- 2.1.3 Pseudo-Random Binary Sequence -- 2.1.4 Generalized Binary Noise -- 2.2 Step Response Model Identification -- 2.2.1 Model -- 2.2.2 Data Processing -- 2.2.3 Model Identification -- 2.2.4 Numerical Example -- 2.3 Prediction Based on Step Response Model and Kalman Filter -- 2.3.1 Steady-State Kalman Filter and Predictor -- 2.3.2 Steady-State Kalman Filter and Predictor Based on -- Step Response Model -- 3 Steady-State Target Calculation -- 3.1 RTO and External Target -- 3.2 Economic Optimization and Target Tracking Problem -- 3.2.1 Economic Optimization -- 3.2.2 Target Tracking Problem -- 3.3 Judging Feasibility and Adjusting Soft Constraint -- 3.3.1 Weight Method -- 3.3.2 Priority-Rank Method -- 3.3.3 Compromise between Adjusting Soft Constraints and Economic Optimization -- 4 Two-Layered DMC for Stable Processes -- 4.1 Open-Loop Prediction Module -- 4.2 Steady-State Target Calculation Module -- 4.2.1 Hard and Soft Constraints -- 4.2.2 Priority-rank of Soft Constraints -- 4.2.3 Feasibility Stage -- 4.2.4 Economic Stage -- 4.3 Dynamic Calculation Module -- 4.4 Numerical Example -- 5 Two-Layered DMC for Stable and Integrating Processes 125 -- 5.1 Open-Loop Prediction Module -- 5.2 Steady-State Target Calculation Module -- 5.2.1 Hard and Soft Constraints -- 5.2.2 Priority-rank of Soft Constraints -- 5.2.3 Feasibility Stage -- 5.2.4 Economic Stage -- 5.3 Dynamic Calculation Module -- 5.4 Numerical Example -- 6 Two-Layered DMC for State-Space Model -- 6.1 Artificial Disturbance Model -- 6.1.1 Basic Model -- 6.1.2 Controlled Variable as Additional State -- 6.1.3 Manipulated Variable as Additional State -- 6.1.4 Kalman Filter -- 6.2 Open-Loop Prediction Module -- 6.3 Steady-State Target Calculation Module -- 6.3.1 Constraints on Steady-State Perturbation Increment -- 6.3.2 Feasibility Stage -- 6.3.3 Economic Stage without Soft Constraint -- 6.4 Dynamic Calculation Module -- 6.5 Numerical Example -- 7 Offset-Free, Nonlinearity and Variable Structure in Two-Layered MPC -- 7.1 State Space Steady-State Target Calculation with Target Tracking 196 -- 7.1.1 Case All External Targets Having Equal Importance 198 -- 7.1.2 Case CV External Target Being More Important -- Than MV External Target -- 7.2 QP-based Dynamic Control and Offset-Free -- 7.3 Static Nonlinear Transformation -- 7.3.1 Principle of Nonlinear Transformation -- 7.3.2 Usual Nonlinear Transformations -- 7.4 Two-Layered MPC with Varying Degree of Freedom -- 7.4.1 Numerical Example without Varying Structure -- 7.4.2 Numerical Example with Varying Number of Manipulated Variables -- 7.5 Numerical Example with Output Collinearity -- 8 Two-Step Model Predictive Control for Hammerstein Model -- 8.1 Two-Step State Feedback MPC -- 8.2 Stability of Two-Step State Feedback MPC -- 8.3 Region of Attraction for Two-Step MPC: Semi-global Stability -- 8.3.1 System Matrix Having No Eigenvalue Outside of Unit Circle -- 8.3.2 System Matrix Having Eigenvalues Outside of Unit Circle -- 8.3.3 Numerical Example -- 8.4 Two-Step Output Feedback Model Predictive Control -- 8.5 Generalized Predictive Control: Basics -- 8.5.1 Output Prediction -- 8.5.2 Receding Horizon Optimization -- 8.5.3 Dead-Beat Property of Generalized Predictive Control 273 -- 8.5.4 On-line Identification and Feedback Correction -- 8.6 Two-Step Generalized Predictive Control -- 8.6.1 Unconstrained Algorithm -- 8.6.2 Algorithm with Input Saturation -- 8.6.3 Stability Results Based on Popov's Theorem -- 8.7 Region of Attraction for Two-Step Generalized Predictive Control 289 -- 8.7.1 State Space Description -- 8.7.2 Stability with Region of Attraction -- 8.7.3 Computation of Region of Attraction -- 8.7.4 Numerical Example -- 9 Heuristic Model Predictive Control for LPV Model 299 -- 9.1 A Heuristic Approach Based-on Open-Loop Optimization -- 9.2 Open-Loop MPC for Unmeasurable State -- 10 Robust Model Predictive Control 323 -- 10.1 A Cornerstone Method -- 10.1.1 KBM (Kothare-Balakrishnan-Morari) Formula -- 10.1.2 KBM (Kothare-Balakrishnan-Morari Controller) -- 10.1.3 Example: Generalizing to Networked Control -- 10.2 Invariant Set Trap -- 10.3 Prediction Horizon: Zero or One -- 10.3.1 One over Zero -- 10.3.2 One: Generalizing to Networked Control -- 10.4 Variant Feedback MPC -- 10.5 About Optimality -- 10.5.1 Constrained Linear Time Varying Quadratic Regulation with Near-Optimal Solution -- 10.5.2 Alternatives with Nominal Performance Cost -- 10.5.3 More Discussions -- 11 Output Feedback Robust Model Predictive Control -- 11.1 Model and Controller Descriptions -- 11.1.1 Controller for LPV Model -- 11.1.2 Controller for Quasi-LPV Model -- 11.2 Characterization of Stability and Optimality -- 11.2.1 Review of Quadratic Boundedness -- 11.2.2 Stability Condition -- 11.2.3 Optimality Condition -- 11.2.4 A Paradox for State Convergence -- 11.3 General Optimization Problem -- 11.3.1 Handling Physical Constraints -- 11.3.2 Current Augmented State -- 11.3.3 Some Usual Transformations -- 11.3.4 Handling Double Convex Combinations -- 11.4 Solutions to Output Feedback MPC -- 11.4.1 Full Online Method for LPV -- 11.4.2 Partial Online Method for LPV -- 11.4.3 Relaxed Variables in Optimization Problem -- 11.4.4 Alternative Forms Based on Congruence Transformation -- 11.4.5 Description of Bound on True State -- References -- Index.