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:

O "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.

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