Dynamic modeling predictive control and perfemance monitoring

The aim of this book is: 1) to provide an introduction to conventional system

identification, model predictive control, and control performance monitoring,

and 2) to present a novel subspace framework for closed-loop identification, data-

driven predictive control and control performance monitoring.

Dynamic modeling, control and monitoring are three central themes in sys-

tems and control. Under traditional design frameworks, dynamic models are the

prerequisite of control and monitoring. However, models are only vehicles to-

wards achieving these design objectives. Once the design of a controller or a

monitor is completed, the model often ceases to exist. The use of models serves

well for the design purpose as most traditional designs are model based; it also

introduces unavoidable modeling error and complexity in building the model. If

a model is identified from data, it is obvious that information contained in the

model is no more than that within the original data. Can a controller or monitor

be designed directly from input-output data bypassing the modeling step?

This book aims to present novel subspace methods to address these questions.

In addition, as necessary background material, this book also provides an intro-

duction to the conventional system identification methods for both open-loop

and closed-loop processes, conventional model predictive control design, con-

ventional control loop performance assessment techniques, and state-of-the-art

model predictive control performance monitoring algorithms. Thus, readers who

are interested in conventional approaches to system identification, model predic-

tive control, and control loop performance assessment will also find the book a

useful tutorial-style reference.

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