Nonlinear Model Predictive Control (NMPC) has become the accepted
methodology to solve complex control problems related to process
industries. The main motivation behind explicit NMPC is that an
explicit state feedback law avoids the need for executing a numerical
optimization algorithm in real time. The benefits of an explicit
solution, in addition to the efficient on-line computations, include
also verifiability of the implementation and the possibility to design
embedded control systems with low software and hardware complexity.
This book considers the multi-parametric Nonlinear Programming (mp-NLP)
approaches to explicit approximate NMPC of constrained nonlinear
systems, developed by the authors, as well as their applications to
various NMPC problem formulations and several case studies. The
following types of nonlinear systems are considered, resulting in
different NMPC problem formulations:
Ø Nonlinear systems described by first-principles models and
nonlinear systems described by black-box models;
- Nonlinear systems with continuous control inputs and nonlinear
systems with quantized control inputs;
- Nonlinear systems without uncertainty and nonlinear systems with
uncertainties (polyhedral description of uncertainty and stochastic
description of uncertainty);
- Nonlinear systems, consisting of interconnected nonlinear
sub-systems.
The proposed mp-NLP approaches are illustrated with applications to
several case studies, which are taken from diverse areas such as
automotive mechatronics, compressor control, combustion plant control,
reactor control, pH maintaining system control, cart and spring system
control, and diving computers.
