Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behindexplicitNMPC is that anexplicitstate feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of anexplicitsolution, 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 toexplicitapproximate NMPC of constrainednonlinearsystems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types ofnonlinearsystems are considered, resulting in different NMPC problem formulations:ØNonlinearsystems described by first-principles models andnonlinearsystems described by black-box models;-Nonlinearsystems with continuous control inputs andnonlinearsystems with quantized control inputs;-Nonlinearsystems without uncertainty andnonlinearsystems with uncertainties (polyhedral description of uncertainty and stochastic description of uncertainty);-Nonlinearsystems, consisting of interconnectednonlinearsub-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. | Explicit Nonlinear Model Predictive Control by Alexandra Grancharova, Paperback | Indigo Chapters