Synopsis
Fundamentals.- Linear Systems in Discrete Time.- Robust Controller Synthesis is Convex for Systems without Control Channel Uncertainties.- Conservation Laws and Lumped System Dynamics.- Polynomial Optimization Problems are Eigenvalue Problems.- Bridging Theory and Applied Technology.- Designing Instrumentation for Control.- Uncertain Model Set Calculation from Frequency Domain Data.- Robust Estimation for Automatic Controller Tuning with Application to Active Noise Control.- Identification of Parameters in Large Scale Physical Model Structures, for the Purpose of Model-Based Operations.- Applications in Motion Control Systems and Industrial Process Control.- Recovering Data from Cracked Optical Discs using Hankel Iterative Learning Control.- Advances in Data-driven Optimization of Parametric and Non-parametric Feedforward Control Designs with Industrial Applications.- Incremental Identification of Hybrid Models of Dynamic Process Systems.- Front Controllability in Two-Phase Porous Media Flow.- PhD Supervision by Okko H. Bosgra.- Okko H. Bosgra, Bibliographic Record.
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From the Back Cover
Model-Based Control: Bridging Rigorous Theory and Advanced Technology contains a collection of contributions written in honor of Professor Okko Bosgra’s sixty-fifth birthday. The book follows his lifelong idea of establishing a unified research agenda that covers a wide cross-section of the model-based control field, ranging from modeling and identification to optimal and robust control. It presents a collection of advances in the aforementioned areas, contributed by a group of leading academics in systems and control. The book’s chapters connect major theoretical developments with practical applications across numerous subject areas, while utilizing clear and well-defined case-studies that help the reader relate theory to practice. Particular attention is given to the application domains of motion control systems, as well as large-scale industrial process control systems.
Model-Based Control is required reading for academic researchers and engineers actively working with process systems or mechatronics who are interested in learning more about model-based control.
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