Model Predictive Control Fundamentals and Practice by Jay H. Lee(.PDF)
File Size: 10 MB
Model Predictive Control: Fundamentals and Practice by Jay H. Lee, Niket S. Kaisare, Carlos E. García
Requirements: .PDF reader, 10 MB | True PDF
Overview: Master advanced control methods bridging academic theory and industrial practice. Model Predictive Control: Fundamentals and Practice 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. This 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. In addition, Appendix provides a concise treatment of linear transformations and stochastic processes, as well as introductory guidance on using SIMULINK and the MATLAB MPC Toolbox to support practical implementation and simulation. The MATLAB codes used in this book are available on a public GitHub repository. This 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.
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