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This book focuses on recent advances in and applications of constrained and nonlinear control. The first part concentrates on theoretical aspects, highlighting synergies between constrained and nonlinear control and explaining challenges and opportunities. The second part examines practical applications. This collection originated from a workshop at the Royal Academy of Belgium.
The volume features contributions from authors based in both academia and industry. Each chapter provides an introduction to a specific research area, reports new findings, and comments on challenges and future research opportunities. The book serves as an entry point for readers interested in recent developments in constrained and nonlinear control.
Sommario
An Introduction to this Volume: Challenges, Opportunities and Applications in Non-linear and Constrained Control.- Part I: Advanced Methodologies for Constrained Nonlinear Control.- Linear Embeddings of Switched Systems and Nonconvex Constraints: Opportunities for MPC.- Convex Lifting in Control. Applications on Constrained Control Design, Fragility Analysis and Path Planning.- Suboptimal Shrinking Horizon and Supervised Receding Horizon Model Predictive Control.- Explicit Reference Governor: Past, Present, and Future.- Recent Advancements on MPC for Tracking: Periodic and Harmonic Formulations.- Deep Subspace Prediction Networks for Nonlinear Data-Driven Predictive Control.- Integrating Learning-Based and MPC-Based Control for PWA Systems: Challenges and Opportunities.- Distributionally Robust and Risk-Averse MPC: Practical Issues and Application to Robotics.- Probabilistic Safety Analysis for Model Predictive Control, with a Case Study on Aircraft Upset Recovery.- An Interval Predictor-Based Robust Control for a Class of Constrained Nonlinear Systems.- Delay Compensation Using Predictive and Periodic Feedback.- Part II: Applications of Constrained Nonlinear Control.- Planar Prescribed-Time Control of a Spacecraft in the Presence of Constraints and Propellant Sloshing.- Robust Tube-Based Nonlinear Model Predictive Control of Multirotor Aircraft Using a Polynomial Zonotopic Framework.- Constrained Control of Manipulators and Aerial Robots with Explicit Reference Governors.- Parameterized Maneuver Governor for Decision Making for Automated Driving.- Accelerating the Solution of Nonlinear Model Predictive Control for Robotic Systems by Reducing the Function Evaluation Complexity.- A Unifying Algorithmic Perspective on Constrained Dynamics and Optimal Control Applied to Whole-Body Quadruped Trajectory Optimization.- The Future of Parabolic Trough Thermosolar Plants: Classic Control and Beyond.- Nonlinear and Constrained Control of Packed-Bed Energy Storage.- Systemsand Control Approaches for the Coevolution of Epidemics and Behavioral Response: Modeling, Identification, and Empirical Validation on a COVID-19 Case Study.
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Emanuele Garone received his Ph.D. in Systems Engineering in 2009 from the University of Calabria, Italy, where he also worked as a post-doc and appointed professor until 2010. In 2010, Prof. Garone became associate professor (Charge’ de Cours) with the Service d’Automatique et d’Analyse des Systemes (SAAS) of the Université Libre de Bruxelles, where he leads one of the two research groups of the department. Since 2022, he holds the title of Professeur in the same department. The main research focus of Prof. Garone is constrained control, with a particular emphasis on the development of low-computational cost schemes for nonlinear systems such as Reference Governors and Explicit Reference Governors. Notably, he co-authored the only survey paper on reference/command governor schemes and is actively involved in several dissemination activities (e.g., tutorials, seminars, workshops) on the subject.
Ilya Kolmanovsky received his Ph.D. degree in Aerospace Engineering in 1995, his M.S. degree in Aerospace Engineering in 1993 and his M.A. degree in Mathematics in 1995, all from the University of Michigan, Ann Arbor. He is presently a Pierre T. Kabamba Collegiate Professor of Aerospace Engineering at the University of Michigan. Professor Kolmanovsky’s research interests are in control theory for systems with state and control constraints and in control applications to automotive and aerospace systems. He is a fellow of IEEE, a fellow of IFAC, a fellow of US National Academy of Inventors, an associate fellow of AIAA, a recipient of the Donald P. Eckman Award of American Automatic Control Council, of 2002 and 2016 IEEE Transactions on Control Systems Technology Outstanding Paper Awards, of SICE Technology Award and of Huebner Research Excellence Award from the University of Michigan. He is the Editor-in-Chief of IEEE Transactions on Control Systems Technology.
Tam W. Nguyen received his M.S. degree in Mechatronics Engineering in 2014 and Ph.D. degree in Control Systems in 2018 from the Free University of Brussels, Belgium. He was a postdoctoral researcher in the Aerospace Engineering Department, University of Michigan, Ann Arbor, from 2019 to 2020. Since 2021, he has been an assistant professor with the Department of Electrical and Electronic Engineering at the University of Toyama, Japan. His interests include robotics and nonlinear and optimal control.
