Eduardo F. Camacho – författare

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.

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.

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.

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 been added throughout and Matlab programs to aid in their solution can be downloaded from the authors' website at http://www esi.us.es/MPCBOOK.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.