A Pragmatic Story of Model Predictive Control: Self-Contained Algorithms and Case-Studies
暫譯: 模型預測控制的實用故事：自包含演算法與案例研究

Nowadays, optimality is a major concern in modern controlled systems, and since optimality generally steers the systems to the boundary of their admissible operational domain, no control designer can afford ignoring Model Predictive Control (MPC). MPC is the only control design methodology that enables systematic handling of constraints and optimality concerns. This book is addressed to under-graduate students in engineering who are interested in control design issues. It can also be used by control researchers or practitioners that are not specialized in MPC but who are willing to acquire a concise and concrete knowledge of this advanced control methodology. Researchers in Robotics, Mechatronics, Process control, Automotive control, Aerospace, Power Systems and so many other application domains where the control plays a crucial role can find in this book answers to their challenging problems. The book covers both linear and nonlinear constrained MPC with many case-studies. All the scripts that are used to produce the results are explicitly and exhaustively given in the book. These scripts can therefore serve as templates to engineers when facing real-life control problems. The book uses the Matlab scientific programming language and shows systematically how to use the Matlab Coder toolbox in order to produce real-time implementable compiled MPC solutions. Several reading advices are also given regarding the state of the art and the major current mainstream research directions on this hot and challenging topic.