Eduardo F. Camacho

This title focuses on two significant problems in the field of automatic control, in particular state estimation and robust Model Predictive Control under input and state constraints, bounded disturbances and measurement noises. The authors build upon previous results concerning zonotopic set-membership state estimation and output feedback tube-based Model Predictive Control. Various existing zonotopic set-membership estimation methods are investigated and their advantages and drawbacks are discussed, making this book suitable both for researchers working in automatic control and industrial partners interested in applying the proposed techniques to real systems. The authors proceed to focus on a new method based on the minimization of the P-radius of a zonotope, in order to obtain a good trade-off between the complexity and the accuracy of the estimation. They propose a P-radius based set-membership estimation method to compute a zonotope containing the real states of a system, which are consistent with the disturbances and measurement noise. The problem of output feedback control using a zonotopic set-membership estimation is also explored. Among the approaches from existing literature on the subject, the implementation of robust predictive techniques based on tubes of trajectories is developed. Contents 1. Uncertainty Representation Based on Set Theory. 2. Several Approaches on Zonotopic Guaranteed Set-Membership Estimation. 3. Zonotopic Guaranteed State Estimation Based on P-Radius Minimization. 4. Tube Model Predictive Control Based on Zonotopic Set-Membership Estimation. About the Authors Vu Tuan Hieu Le is a Research Engineer at the IRSEEM/ESIGELEC Technopôle du Madrillet, Saint Etienne du Rouvray, France. Cristina Stoica is Assistant Professor in the Automatic Control Department at SUPELEC Systems Sciences (E3S), France. Teodoro Alamo is Professor in the Department of Systems Engineering and Automatic Control at the University of Seville, Spain. Eduardo F. Camacho is Professor in the Department of Systems Engineering and Automatic Control at the University of Seville, Spain. Didier Dumur is Professor in the Automatic Control Department, SUPELEC Systems Sciences (E3S), France.

Model Predictive Control (MPC), the classic textbook for students and practitioners seeking deep understanding of advanced control systems, is now revised, updated and reorganized in a streamlined third edition. The authors, renowned researchers in the field, cover an extensive range of topics that embraces the basic and the advanced, the theoretical and the applied. The book offers advanced undergraduate and graduate students an accessible, step-by-step approach that enables them progressively to grasp and apply the concepts they are studying. For instructors, this is an invaluable curriculum resource packed with examples and case studies. The text features material on: commercial MPC: convolution models, transfer functions, state-space models, and constraints;advanced topics: robust and stochastic MPC and MPC for nonlinear, hybrid, large-scale, and distributed systems; andapplications: a series of case studies in solar energy generation, hospital stock control, copper mining and aviation; along withexercises to help readers assess their progress, many with full or partial solutions in a solutions manual downloadable by adopting instructors. MATLAB® programs to assist with the design aspects of the book and with reproducing some of the examples are included. Model Predictive Control (third edition)’s distinctive strength is its real-world relevance. It is an essential tool for future engineers; its focus on practical implementation, bridging the gap between academic theory and industrial practice and supplemented by exploration of optimization- and algorithm-related aspects of MPC, ensures a holistic treatment of the subject.

Control of Solar Energy Systems details the main solar energy systems, problems involved with their control, and how control systems can help in increasing their efficiency. Thermal energy systems are explored in depth, as are photovoltaic generation and other solar energy applications such as solar furnaces and solar refrigeration systems. This second and updated edition of Advanced Control of Solar Plants includes new material on: solar towers and solar tracking; heliostat calibration, characterization and offset correction; solar radiation, estimation, prediction, and computation; and integrated control of solar plants. This new edition contains worked examples in the text as well as proposed exercises and simulation models and so will be of great use to the student and academic, as well as the industrial practitioner.

Control of Solar Energy Systems details the main solar energy systems, problems involved with their control, and how control systems can help in increasing their efficiency. Thermal energy systems are explored in depth, as are photovoltaic generation and other solar energy applications such as solar furnaces and solar refrigeration systems. This second and updated edition of Advanced Control of Solar Plants includes new material on: solar towers and solar tracking; heliostat calibration, characterization and offset correction; solar radiation, estimation, prediction, and computation; and integrated control of solar plants. This new edition contains worked examples in the text as well as proposed exercises and simulation models and so will be of great use to the student and academic, as well as the industrial practitioner.

Model Predictive Control is an important technique used in the process control industries. It has developed considerably in the last few years, because it is the most general way of posing the process control problem in the time domain. The Model Predictive Control formulation integrates optimal control, stochastic control, control of processes with dead time, multivariable control and future references. The finite control horizon makes it possible to handle constraints and non linear processes in general which are frequently found in industry. Focusing on implementation issues for Model Predictive Controllers in industry, it fills the gap between the empirical way practitioners use control algorithms and the sometimes abstractly formulated techniques developed by researchers. The text is firmly based on material from lectures given to senior undergraduate and graduate students and articles written by the authors.

From power plants to sugar refining, model predictive control (MPC) schemes have established themselves as the preferred control strategies for a wide variety of processes. The second edition of "Model Predictive Control" provides a thorough introduction to theoretical and practical aspects of the most commonly used MPC strategies. It bridges the gap between the powerful but often abstract techniques of control researchers and the more empirical approach of practitioners. "Model Predictive Control" demonstrates that a powerful technique does not always require complex control algorithms. The text features material on the following subjects: general MPC elements and algorithms; commercial MPC schemes; generalized predictive control multivariable, robust, constrained nonlinear and hybrid MPC; fast methods for MPC implementation; applications. All of the material is thoroughly updated for the second edition with the chapters on nonlinear MPC, MPC and hybrid systems and MPC implementation being entirely new. Many new exercises and examples have also have also been added throughout and MATLAB® programs to aid in their solution can be downloaded from extras.springer.com. The text is an excellent aid for graduate and advanced undergraduate students and will also be of use to researchers and industrial practitioners wishing to keep abreast of a fast-moving field.