Recent developments in model-predictive control promise remarkable
opportunities for designing multi-input, multi-output control systems
and improving the control of single-input, single-output systems. This
volume provides a definitive survey of the latest model-predictive
control methods available to engineers and scientists today.
The initial set of chapters present various methods for managing
uncertainty in systems, including stochastic model-predictive control.
With the advent of affordable and fast computation, control engineers
now need to think about using "computationally intensive controls," so
the second part of this book addresses the solution of optimization
problems in "real" time for model-predictive control. The theory and
applications of control theory often influence each other, so the last
section of Handbook of Model Predictive Control rounds out the book with
representative applications to automobiles, healthcare, robotics, and
finance.
The chapters in this volume will be useful to working engineers,
scientists, and mathematicians, as well as students and faculty
interested in the progression of control theory. Future developments in
MPC will no doubt build from concepts demonstrated in this book and
anyone with an interest in MPC will find fruitful information and
suggestions for additional reading.
