Explicit Nonlinear Model Predictive Control [electronic resource] : Theory and Applications /
Grancharova, Alexandra.author.
Johansen, Tor Arne.author.
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.  .
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
Multi-parametric Programming -- Nonlinear Model Predictive Control -- Explicit NMPC Using mp-QP Approximations of mp-NLP -- Explicit NMPC via Approximate mp-NLP -- Explicit MPC of Constrained Nonlinear Systems with Quantized Inputs -- Explicit Min-Max MPC of Constrained Nonlinear Systems with Bounded Uncertainties -- Explicit Stochastic NMPC -- Explicit NMPC Based on Neural Network Models -- Semi-Explicit Distributed NMPC.
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.  .
Control engineering.
Computational complexity.
System theory.
Statistical physics.
Control and Systems Theory.
Complexity.
Systems Theory, Control.
Applications of Nonlinear Dynamics and Chaos Theory.
Lecture notes in control and information sciences