{"product_id":"model-predictive-control-fundamentals-and-practice-hardback-9781394333295","title":"Model Predictive Control; Fundamentals and Practice (Hardback) 9781394333295","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eModel Predictive Control\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eFundamentals and Practice\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eJay H. Lee (Author), Niket S. Kaisare (Author), Carlos E. García (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394333295, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 10 June 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e560 pages\u003cbr\u003e25.4 x 17.8 x 3.4 cm, 1.161 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eMaster advanced control methods bridging academic theory and industrial practice\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eModel Predictive Control: Fundamentals and Practice\u003c\/i\u003e walks engineers through the transition from academic study to industrial application of advanced process control. This comprehensive text connects current model predictive control (MPC) theory to its industrial origins and classical linear control methods, providing the foundations necessary for effective real-world application. \u003c\/p\u003e\n\u003cp\u003eThis book’s three-part structure guides readers from basic industrial algorithms through linear systems fundamentals to advanced MPC topics. It clarifies equivalences between MPC and Linear-Quadratic optimal control, and between Moving Horizon Estimation and Kalman filtering. It also includes practical coverage of system identification. The book balances up-to-date theory with hands-on applications and maintains accessibility without sacrificing mathematical rigor. \u003c\/p\u003e\n\u003cp\u003eReaders will learn to: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eEffectively transition theoretical knowledge into practical control applications for complex processes\u003c\/li\u003e \u003cli\u003eUnderstand connections between MPC and classical optimal control methods through clear detailed explanations\u003c\/li\u003e \u003cli\u003eMaster system identification techniques essential for developing accurate process models\u003c\/li\u003e \u003cli\u003eExplore nonlinear MPC and the innovative Repetitive MPC for advanced real-world control challenges\u003c\/li\u003e \u003cli\u003eApply concepts through curated sample problems designed to enhance practical understanding and implementation skills\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThis is an ideal graduate-level textbook and essential reference for practicing engineers seeking to master advanced control strategies. It balances authoritative theoretical explanation with practical application, preparing readers to solve real-world control problems.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eForeword xv\u003cbr\u003ePreface xxv\u003cbr\u003eAcknowledgments xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003cbr\u003e1.1 What Is MPC? 1\u003cbr\u003e1.2 Why MPC? 5\u003cbr\u003e1.3 Historical Overview 10\u003cbr\u003e1.4 Impact of MPC on Control Research 13\u003cbr\u003e1.5 A Typical Industrial Control Problem 17\u003cbr\u003e1.6 Organization of This Book 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Early Industrial MPC Algorithms 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Step Response Modeling and Identification 25\u003c\/b\u003e\u003cbr\u003e2.1 Linear Time-invariant Systems 26\u003cbr\u003e2.2 Impulse\/Step Response Models 28\u003cbr\u003e2.3 Multi-step Prediction 34\u003cbr\u003e2.4 Examples 39\u003cbr\u003e2.5 Identification 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Dynamic Matrix Control: The Basic Algorithm 51\u003c\/b\u003e\u003cbr\u003e3.1 The Concept of Moving Horizon Control 51\u003cbr\u003e3.2 Multi-step Prediction 52\u003cbr\u003e3.3 Objective Function 56\u003cbr\u003e3.4 Constraints 57\u003cbr\u003e3.5 Quadratic Programming Solution of the Control Problem 60\u003cbr\u003e3.6 Implementation 62\u003cbr\u003e3.7 Examples: Analysis and Guidelines 71\u003cbr\u003e3.8 Case Study: Control of the \"Shell Heavy Oil Fractionator\" Using DMC 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Dynamic Matrix Control—Extensions and Variations 99\u003c\/b\u003e\u003cbr\u003e4.1 Features Found in Other Industrial Algorithms 99\u003cbr\u003e4.2 Connection with Internal Model Control 104\u003cbr\u003e4.3 Some Possible Enhancements to DMC 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Basics of Linear Systems, Optimal Control, and System Identification 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Linear Time Invariant System Models 119\u003c\/b\u003e\u003cbr\u003e5.1 Sampling and Reconstruction 120\u003cbr\u003e5.2 Introduction to z-transform 124\u003cbr\u003e5.3 Transfer Function Models 125\u003cbr\u003e5.4 State-space Model 130\u003cbr\u003e5.5 Conversion Between Discrete-time Models 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Discrete-time State Space Models 145\u003c\/b\u003e\u003cbr\u003e6.1 State-coordinate Transformation 145\u003cbr\u003e6.2 Stability 146\u003cbr\u003e6.3 Controllability, Reachability, and Stabilizability 148\u003cbr\u003e6.4 Observability, Reconstructability, and Detectability 156\u003cbr\u003e6.5 Kalman Decomposition and Minimal Realization 160\u003cbr\u003e6.6 Disturbance Modeling 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 State Estimation 171\u003c\/b\u003e\u003cbr\u003e7.1 Linear Estimator Structure 172\u003cbr\u003e7.2 Observer Pole Placement 173\u003cbr\u003e7.3 Kalman Filter 176\u003cbr\u003e7.4 Extensions 185\u003cbr\u003e7.5 Least Squares Formulation of State Estimation 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Unconstrained Quadratic Optimal Control 201\u003c\/b\u003e\u003cbr\u003e8.1 Linear State Feedback Controller Design 202\u003cbr\u003e8.2 Finite-horizon Quadratic Optimal Control 203\u003cbr\u003e8.3 Infinite-horizon Quadratic Optimal Control 208\u003cbr\u003e8.4 Analysis 214\u003cbr\u003e8.5 Stochastic LQ Control 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Constrained Quadratic Optimal Control 223\u003c\/b\u003e\u003cbr\u003e9.1 Finite-horizon Problem 223\u003cbr\u003e9.2 Infinite-horizon Problem 224\u003cbr\u003e9.3 Constraint Softening 231\u003cbr\u003e9.4 Derivation of an Explicit Form of the Optimal Control Law via Multi-parametric Programming 232\u003cbr\u003e9.5 Analysis 235\u003cbr\u003e9.6 Stochastic Case (*) 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 System Identification 249\u003c\/b\u003e\u003cbr\u003e10.1 Problem Overview 249\u003cbr\u003e10.2 Model Structures 250\u003cbr\u003e10.3 Parametric Identification Methods 255\u003cbr\u003e10.4 Nonparametric Identification 270\u003cbr\u003e10.5 Subspace Identification 275\u003cbr\u003e10.6 Practice of System Identification: A User's Perspective 284\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Advanced MPC 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Linear MPC: State-space Formulation 299\u003c\/b\u003e\u003cbr\u003e11.1 Model Construction 300\u003cbr\u003e11.2 The Background: Deterministic State-space MPC 310\u003cbr\u003e11.3 The Workhorse: MPC with State Estimation 316xii Contents\u003cbr\u003e11.4 Inferential Control 335\u003cbr\u003e11.5 Sequential Linearization-based MPC (for Nonlinear Systems) 341\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Nonlinear Model Predictive Control 359\u003c\/b\u003e\u003cbr\u003e12.1 NMPC Formulation 360\u003cbr\u003e12.2 Solution via NLP 362\u003cbr\u003e12.3 Stability and Other Properties 368\u003cbr\u003e12.4 Nonlinear State Estimation 372\u003cbr\u003e12.5 Case Study 379\u003cbr\u003e12.6 Conclusions and Future Directions 382\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Repetitive MPC for Batch and Periodic Systems 387\u003c\/b\u003e\u003cbr\u003e13.1 Historical Background 387\u003cbr\u003e13.2 General Framework 388\u003cbr\u003e13.3 Iterative Learning Model Predictive Control for Batch Systems 391\u003cbr\u003e13.4 Repetitive Model Predictive Control for Continuous Systems with Periodic Operations 397\u003cbr\u003e13.5 Future Outlook 405\u003cbr\u003eExercises 406\u003c\/p\u003e \u003cp\u003eAppendix A Review of Linear Transformation 409\u003cbr\u003eAppendix B Random Variables and Stochastic Processes 433\u003cbr\u003eAppendix C Model Reduction 455\u003cbr\u003eAppendix D Optimality of Kalman Filter and LQG Controller for Linear Gaussian Systems 463\u003cbr\u003eAppendix E Internal Model Control Basics 479\u003cbr\u003eAppendix F MPC Toolbox Tutorial: Shell Oil Fractionator 495\u003cbr\u003eAppendix G A Brief Tutorial on Simulink 513\u003c\/p\u003e \u003cp\u003eBibliography 517\u003cbr\u003eIndex 523\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Industrial chemistry \u0026amp; manufacturing technologies [\u003ca title=\"See our other books on Industrial chemistry \u0026amp; manufacturing technologies\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Industrial%20chemistry%20\u0026amp;%20manufacturing%20technologies%20%5BTD%5D%22\"\u003eTD\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52433817633048,"sku":"9781394333295","price":102.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394333295.jpg?v=1784853933","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/model-predictive-control-fundamentals-and-practice-hardback-9781394333295","provider":"Freshly Printed Books","version":"1.0","type":"link"}