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<SUP>®</SUP> 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<SUP>®</SUP> programs and exercises to assist the student.
