Explicit Nonlinear Model Predictive Control
Theory and Applications
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Beschreibung
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;
Ø Nonlinear systems with continuous control inputs and nonlinear systems with quantized control inputs;
Ø Nonlinear systems without uncertainty and nonlinear systems with uncertainties (polyhedral description of uncertainty and stochastic description of uncertainty);
Ø 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.
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.
Presents an explicit solution of model predictive control problems for constrained nonlinear systems Recent research on various types of nonlinear systems resulting in various Nonlinear Model Predictive Control formulations Illustrated with applications to practical nonlinear systems
Autor*in
Alexandra Grancharova
Themen in »Explicit Nonlinear Model Predictive Control«
Constrained Nonlinear Systems Interconnected Nonlinear Systems Nonlinear Model Predictive Control Parametric Programming Systems with Quantized Inputs complexity