His research is focused on optimal control methods and machine-learning-based control systems.Īssociate Professor Michal Kvasnica received his diploma in process control from the Slovak University of Technology in Bratislava (STUBA), Slovakia in 2000 and Ph.D. Klaučo published 7 peer-reviewed current-contents papers and more than 15 conference papers in the field of optimal process control. He graduated summa cum laude in 2017 at the Slovak University of Technology in Bratislava and obtained the Ph.D. degree obtained from process control in 2013 from the Slovak University of Technology in Bratislava.
degree from the Denmark University of Technology in automatic control in 2012.
The detailed mathematical treatments will attract the attention of academic researchers interested in the applications of MPC. The case studies and practical considerations of constraints will help control engineers working in various industries in the use of MPC at the supervisory level. stabilization of objects in a magnetic field andĮach chapter includes precise mathematical formulations of the corresponding MPC-based governor, reformulation of the control problem into an optimization problem, and a detailed presentation and comparison of results.Experimental and simulation case studies from four applications are discussed in depth: The second part of the book is devoted to practical aspects of MPC-based reference governor schemes. cases where the lower-level controllers are themselves model predictive controllers.įor each case the authors provide a thorough theoretical derivation of the corresponding reference governor, followed by illustrative examples.The design procedure depends on the type of lower-level controller involved and four practical cases are covered: The first part of the monograph introduces the concept of optimization-based reference governors, provides an overview of the fundamentals of convex optimization and MPC, and discusses a rigorous design procedure for MPC-based reference governors. These MPC-based governors serve as a supervisory control layer that generates optimal trajectories for lower-level controllers such that the safety of the system is enforced while optimizing the overall performance of the closed-loop system. This monograph focuses on the design of optimal reference governors using model predictive control (MPC) strategies.
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