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Model Predictive Control System Design and Implementation Using MATLAB® [electronic resource] /by Liuping Wang.

by Wang, Liuping [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Advances in Industrial Control: Publisher: London : Springer London, 2009.Description: online resource.ISBN: 9781848823310.Subject(s): Engineering | Chemical engineering | Computer aided design | Engineering mathematics | Control engineering systems | Industrial engineering | Computer engineering | Engineering | Control , Robotics, Mechatronics | Computer-Aided Engineering (CAD, CAE) and Design | Industrial and Production Engineering | Appl.Mathematics/Computational Methods of Engineering | Industrial Chemistry/Chemical Engineering | Electrical EngineeringDDC classification: 629.8 Online resources: Click here to access online
Contents:
Discrete-time MPC for Beginners -- Discrete-time MPC with Constraints -- Discrete-time MPC Using Laguerre Functions -- Discrete-time MPC with Prescribed Degree of Stability -- Continuous-time Orthonormal Basis Functions -- Continuous-time MPC -- Continuous-time MPC with Constraints -- Continuous-time MPC with Prescribed Degree of Stability -- Classical MPC Systems in State-space Formulation -- Implementation of Predictive Control Systems.
In: Springer eBooksSummary: Model Predictive Control (MPC) is unusual in receiving on-going interest in both industrial and academic circles. Issues such as plant optimization and constrained control which are critical to industrial engineers are naturally embedded in its designs. 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, detailed coverage is given to three industrial applications: a food extruder, a motor and a magnetic bearing system. 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, mainly based on advances in MPC using state-space models and basis functions – to which the author is a major contributor, will be of interest to control researchers and practitioners, especially of process control. From a pedagogical standpoint, this volume includes numerous simple analytical examples and every chapter contains problems and MATLAB® programs and exercises to assist the student.
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Discrete-time MPC for Beginners -- Discrete-time MPC with Constraints -- Discrete-time MPC Using Laguerre Functions -- Discrete-time MPC with Prescribed Degree of Stability -- Continuous-time Orthonormal Basis Functions -- Continuous-time MPC -- Continuous-time MPC with Constraints -- Continuous-time MPC with Prescribed Degree of Stability -- Classical MPC Systems in State-space Formulation -- Implementation of Predictive Control Systems.

Model Predictive Control (MPC) is unusual in receiving on-going interest in both industrial and academic circles. Issues such as plant optimization and constrained control which are critical to industrial engineers are naturally embedded in its designs. 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, detailed coverage is given to three industrial applications: a food extruder, a motor and a magnetic bearing system. 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, mainly based on advances in MPC using state-space models and basis functions – to which the author is a major contributor, will be of interest to control researchers and practitioners, especially of process control. From a pedagogical standpoint, this volume includes numerous simple analytical examples and every chapter contains problems and MATLAB® programs and exercises to assist the student.

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