JAY H. LEE, PhD, is the Choon Hoon Cho Chair and Professor of Chemical and Materials Science, Aerospace and Mechanical Engineering, Electrical and Computer Engineering, and Industrial and Systems Engineering at the University of Southern California. He has been an authoritative researcher on model predictive control, optimization, and AI applications.NIKET S. KAISARE, PhD, is a Professor in the Department of Chemical Engineering at the Indian Institute of Technology - Madras. He specializes in advanced process control, catalytic micro-reactors, and energy systems, and is an expert in model-based advanced process control.CARLOS E. GARCÍA, PhD, has retired as the Global Discipline Head for Process Control at Shell Oil Company following a 36-year career. He is widely recognized as one of the pioneers of model predictive control and is a member of the Control Process Automation Hall of Fame.

• Foreword xvPreface xxvAcknowledgments xxvii1 Introduction 11.1 What Is MPC? 11.2 Why MPC? 51.3 Historical Overview 101.4 Impact of MPC on Control Research 131.5 A Typical Industrial Control Problem 171.6 Organization of This Book 20Part I Early Industrial MPC Algorithms 232 Step Response Modeling and Identification 252.1 Linear Time-invariant Systems 262.2 Impulse/Step Response Models 282.3 Multi-step Prediction 342.4 Examples 392.5 Identification 413 Dynamic Matrix Control: The Basic Algorithm 513.1 The Concept of Moving Horizon Control 513.2 Multi-step Prediction 523.3 Objective Function 563.4 Constraints 573.5 Quadratic Programming Solution of the Control Problem 603.6 Implementation 623.7 Examples: Analysis and Guidelines 713.8 Case Study: Control of the "Shell Heavy Oil Fractionator" Using DMC 834 Dynamic Matrix Control—Extensions and Variations 994.1 Features Found in Other Industrial Algorithms 994.2 Connection with Internal Model Control 1044.3 Some Possible Enhancements to DMC 106Part II Basics of Linear Systems, Optimal Control, and System Identification 1175 Linear Time Invariant System Models 1195.1 Sampling and Reconstruction 1205.2 Introduction to z-transform 1245.3 Transfer Function Models 1255.4 State-space Model 1305.5 Conversion Between Discrete-time Models 1346 Discrete-time State Space Models 1456.1 State-coordinate Transformation 1456.2 Stability 1466.3 Controllability, Reachability, and Stabilizability 1486.4 Observability, Reconstructability, and Detectability 1566.5 Kalman Decomposition and Minimal Realization 1606.6 Disturbance Modeling 1637 State Estimation 1717.1 Linear Estimator Structure 1727.2 Observer Pole Placement 1737.3 Kalman Filter 1767.4 Extensions 1857.5 Least Squares Formulation of State Estimation 1928 Unconstrained Quadratic Optimal Control 2018.1 Linear State Feedback Controller Design 2028.2 Finite-horizon Quadratic Optimal Control 2038.3 Infinite-horizon Quadratic Optimal Control 2088.4 Analysis 2148.5 Stochastic LQ Control 2169 Constrained Quadratic Optimal Control 2239.1 Finite-horizon Problem 2239.2 Infinite-horizon Problem 2249.3 Constraint Softening 2319.4 Derivation of an Explicit Form of the Optimal Control Law via Multi-parametric Programming 2329.5 Analysis 2359.6 Stochastic Case (*) 24310 System Identification 24910.1 Problem Overview 24910.2 Model Structures 25010.3 Parametric Identification Methods 25510.4 Nonparametric Identification 27010.5 Subspace Identification 27510.6 Practice of System Identification: A User's Perspective 284Part III Advanced MPC 29711 Linear MPC: State-space Formulation 29911.1 Model Construction 30011.2 The Background: Deterministic State-space MPC 31011.3 The Workhorse: MPC with State Estimation 316xii Contents11.4 Inferential Control 33511.5 Sequential Linearization-based MPC (for Nonlinear Systems) 34112 Nonlinear Model Predictive Control 35912.1 NMPC Formulation 36012.2 Solution via NLP 36212.3 Stability and Other Properties 36812.4 Nonlinear State Estimation 37212.5 Case Study 37912.6 Conclusions and Future Directions 38213 Repetitive MPC for Batch and Periodic Systems 38713.1 Historical Background 38713.2 General Framework 38813.3 Iterative Learning Model Predictive Control for Batch Systems 39113.4 Repetitive Model Predictive Control for Continuous Systems with Periodic Operations 39713.5 Future Outlook 405Exercises 406Appendix A Review of Linear Transformation 409Appendix B Random Variables and Stochastic Processes 433Appendix C Model Reduction 455Appendix D Optimality of Kalman Filter and LQG Controller for Linear Gaussian Systems 463Appendix E Internal Model Control Basics 479Appendix F MPC Toolbox Tutorial: Shell Oil Fractionator 495Appendix G A Brief Tutorial on Simulink 513Bibliography 517Index 523

JAY H. LEE, PhD, is the Choon Hoon Cho Chair and Professor of Chemical and Materials Science, Aerospace and Mechanical Engineering, Electrical and Computer Engineering, and Industrial and Systems Engineering at the University of Southern California. He has been an authoritative researcher on model predictive control, optimization, and AI applications.NIKET S. KAISARE, PhD, is a Professor in the Department of Chemical Engineering at the Indian Institute of Technology - Madras. He specializes in advanced process control, catalytic micro-reactors, and energy systems, and is an expert in model-based advanced process control.CARLOS E. GARCÍA, PhD, has retired as the Global Discipline Head for Process Control at Shell Oil Company following a 36-year career. He is widely recognized as one of the pioneers of model predictive control and is a member of the Control Process Automation Hall of Fame.

• Foreword xvPreface xxvAcknowledgments xxvii1 Introduction 11.1 What Is MPC? 11.2 Why MPC? 51.3 Historical Overview 101.4 Impact of MPC on Control Research 131.5 A Typical Industrial Control Problem 171.6 Organization of This Book 20Part I Early Industrial MPC Algorithms 232 Step Response Modeling and Identification 252.1 Linear Time-invariant Systems 262.2 Impulse/Step Response Models 282.3 Multi-step Prediction 342.4 Examples 392.5 Identification 413 Dynamic Matrix Control: The Basic Algorithm 513.1 The Concept of Moving Horizon Control 513.2 Multi-step Prediction 523.3 Objective Function 563.4 Constraints 573.5 Quadratic Programming Solution of the Control Problem 603.6 Implementation 623.7 Examples: Analysis and Guidelines 713.8 Case Study: Control of the "Shell Heavy Oil Fractionator" Using DMC 834 Dynamic Matrix Control—Extensions and Variations 994.1 Features Found in Other Industrial Algorithms 994.2 Connection with Internal Model Control 1044.3 Some Possible Enhancements to DMC 106Part II Basics of Linear Systems, Optimal Control, and System Identification 1175 Linear Time Invariant System Models 1195.1 Sampling and Reconstruction 1205.2 Introduction to z-transform 1245.3 Transfer Function Models 1255.4 State-space Model 1305.5 Conversion Between Discrete-time Models 1346 Discrete-time State Space Models 1456.1 State-coordinate Transformation 1456.2 Stability 1466.3 Controllability, Reachability, and Stabilizability 1486.4 Observability, Reconstructability, and Detectability 1566.5 Kalman Decomposition and Minimal Realization 1606.6 Disturbance Modeling 1637 State Estimation 1717.1 Linear Estimator Structure 1727.2 Observer Pole Placement 1737.3 Kalman Filter 1767.4 Extensions 1857.5 Least Squares Formulation of State Estimation 1928 Unconstrained Quadratic Optimal Control 2018.1 Linear State Feedback Controller Design 2028.2 Finite-horizon Quadratic Optimal Control 2038.3 Infinite-horizon Quadratic Optimal Control 2088.4 Analysis 2148.5 Stochastic LQ Control 2169 Constrained Quadratic Optimal Control 2239.1 Finite-horizon Problem 2239.2 Infinite-horizon Problem 2249.3 Constraint Softening 2319.4 Derivation of an Explicit Form of the Optimal Control Law via Multi-parametric Programming 2329.5 Analysis 2359.6 Stochastic Case (*) 24310 System Identification 24910.1 Problem Overview 24910.2 Model Structures 25010.3 Parametric Identification Methods 25510.4 Nonparametric Identification 27010.5 Subspace Identification 27510.6 Practice of System Identification: A User's Perspective 284Part III Advanced MPC 29711 Linear MPC: State-space Formulation 29911.1 Model Construction 30011.2 The Background: Deterministic State-space MPC 31011.3 The Workhorse: MPC with State Estimation 316xii Contents11.4 Inferential Control 33511.5 Sequential Linearization-based MPC (for Nonlinear Systems) 34112 Nonlinear Model Predictive Control 35912.1 NMPC Formulation 36012.2 Solution via NLP 36212.3 Stability and Other Properties 36812.4 Nonlinear State Estimation 37212.5 Case Study 37912.6 Conclusions and Future Directions 38213 Repetitive MPC for Batch and Periodic Systems 38713.1 Historical Background 38713.2 General Framework 38813.3 Iterative Learning Model Predictive Control for Batch Systems 39113.4 Repetitive Model Predictive Control for Continuous Systems with Periodic Operations 39713.5 Future Outlook 405Exercises 406Appendix A Review of Linear Transformation 409Appendix B Random Variables and Stochastic Processes 433Appendix C Model Reduction 455Appendix D Optimality of Kalman Filter and LQG Controller for Linear Gaussian Systems 463Appendix E Internal Model Control Basics 479Appendix F MPC Toolbox Tutorial: Shell Oil Fractionator 495Appendix G A Brief Tutorial on Simulink 513Bibliography 517Index 523