This monograph introduces the authors' work on model predictive control system design using extended state space and extended
non-minimal state space approaches. It systematically describes model predictive control design for chemical processes, including
the basic control algorithms, the extension to predictive functional control, constrained control, closed-loop system analysis,
model predictive control optimization-based PID control, genetic algorithm optimization-based model predictive control, and
industrial applications. Providing important insights, useful methods and practical algorithms that can be used in chemical
process control and optimization, it offers a valuable resource for researchers, scientists and engineers in the field of
process system engineering and control engineering.