Jay H. Lee – författare

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. Readers will learn to: Effectively transition theoretical knowledge into practical control applications for complex processesUnderstand connections between MPC and classical optimal control methods through clear detailed explanationsMaster system identification techniques essential for developing accurate process modelsExplore nonlinear MPC and the innovative Repetitive MPC for advanced real-world control challengesApply concepts through curated sample problems designed to enhance practical understanding and implementation skillsThis 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.

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. Readers will learn to: Effectively transition theoretical knowledge into practical control applications for complex processes Understand connections between MPC and classical optimal control methods through clear detailed explanations Master system identification techniques essential for developing accurate process models Explore nonlinear MPC and the innovative Repetitive MPC for advanced real-world control challenges Apply concepts through curated sample problems designed to enhance practical understanding and implementation skills 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.