Model Predictive Control System Design and Implementation Using MATLAB® (Advances in Industrial Control) - Tapa dura

Acerca del autor

Liuping Wang received her PhD in 1989 from the University of Sheffield, UK; subsequently, she was an adjunct associate professor in the Dept. of Chemical Engineering at the University of Toronto, Canada. From 1998 to 2002, she was a senior lecturer and research coordinator in the Center for Integrated Dynamics and Control, University of Newcastle, Australia before joining RMIT University where she is a professor and Head of Discipline of Electrical Engineering. She is the author of two books, joint editor of one book, and has published over 130 papers.

Liuping Wang has been actively engaged in industry-oriented research and development since the completion of her PhD studies. Whilst working at the University of Toronto, Canada, she was a co-founder of an industry consortium for the identification of chemical processes. Since her arrival in Australia in 1998, she has been working with Australian government organisations and companies in the areas of food manufacturing, mining, automotive and power services, including Food Science Australia, Uncle Ben’s Australia, CSR, BHP-Billiton, Pacific Group Technologies, Holden Innovation, Alinta, and ANCA. She leads the Control Systems program at the Australian Advanced Manufacturing Cooperative Research Center (AMCRC) that develops next generation technology platforms for the manufacturing industry. She is also on the Board of Directors of the Australian Power Academy that promotes power-engineering education and raises scholarships from the power industry to support undergraduate students.

De la contraportada

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.