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Model predictive control is an indispensable part of industrial control engineering and is increasingly the "method of choice" for advanced control applications. Jan Maciejowski's book provides a systematic and comprehensive course on predictive control suitable for final year students and professional engineers. The first book to cover constrained predictive control, the text reflects the true use of the topic in industry.
Model Predictive Control System Design and Implementation Using MATLAB® proposes methods for design and implementation of MPC systems using basis functions that confer the following advantages: - continuous- and discrete-time MPC problems solved in similar design frameworks; - a parsimonious parametric representation of the control trajectory gives rise to computationally efficient algorithms and better on-line performance; and - a more general discrete-time representation of MPC design that becomes identical to the traditional approach for an appropriate choice of parameters. After the theoretical presentation, coverage is given to three industrial applications. The subject of quadratic programming, often associated with the core optimization algorithms of MPC is also introduced and explained. The technical contents of this book is mainly based on advances in MPC using state-space models and basis functions. This volume includes numerous analytical examples and problems and MATLAB® programs and exercises.
The object of a predictive controller is to move the states of a plant from some finite initial conditions to some finite and conditions in a time-optimal, or sub-time-optimal way. To do this the plant is driven in an on-off manner; the controller calculates when the plant drive direction should be switched to give suitable control. The constraint algorithms allow such control to proceed while simultaneously enabling any state or combination of states to be restricted to a prescribed level. Algorithms are outlined for putting hard constraints on the values of the state variables in a system controlled using predictive (fast model) techniques and are applied to an air flight trajectory problem. Two algorithms are described, the first dealing with restrictions on the N-1th state of an N'th order system and the second dealing with restrictions on the remaining state variables. To comprehend the constraint procedure, the principles of predictive control are briefly described with the aid of time history plots and phase plane portraits. Results are presented for constraints simultaneously applied to variables in a third order system and for trajectory problem. The constraint algorithms are independent of the predictive control strategy. Keywords: Control theory; Automatic control; Controller characteristics.
A comprehensive examination of DMPC theory and its technological applications A comprehensive examination of DMPC theory and its technological applications from basic through to advanced level A systematic introduction to DMPC technology providing classic DMPC coordination strategies, analysis of their performance, and design methods for both unconstraint and constraint systems Includes the system partition methods, coordination strategies, the performance analysis and how to design stabilized DMPC under different coordination strategies Presents useful theories and technologies which can be used in many different industrial fields, such as the metallurgical process and high speed transport, helping readers to grasp the procedure of using the DMPC Reflects the authors combined research in the area, providing a wealth of and current and background information
Over the past few years significant progress has been achieved in the field of nonlinear model predictive control (NMPC), also referred to as receding horizon control or moving horizon control. More than 250 papers have been published in 2006 in ISI Journals. With this book we want to bring together the contributions of a diverse group of internationally well recognized researchers and industrial practitioners, to critically assess the current status of the NMPC field and to discuss future directions and needs. The book consists of selected papers presented at the International Workshop on Assessment an Future Directions of Nonlinear Model Predictive Control that took place from September 5 to 9, 2008, in Pavia, Italy.
Predictive control is a powerful tool in dealing with those processes with large time delays. Generalized Predictive Control GPC is the most popular approach to the subject, and this text discusses the application of GPC starting with the concept of long-range predictive control and its need in medicine particularly automated drug deliveries.; The concept of adaptation is also emphasized with respect to patient-to-patient parameter variations. Subsequent chapters discuss interactions, comparisons and various aspects of GPC. The book concludes by putting into perspective the generic nature of the architecture built around GPC and which provides model-based fault diagnosis with control.

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