This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: * complexity and structure in model predictive control (MPC); * collaborative MPC; * distributed MPC; * optimization-based analysis and design; and * applications to bioprocesses, multivehicle systems or energy management. The various contributions cover a subject spectrum including inverse optimality and more modern decentralized and cooperative formulations of receding-horizon optimal control. Readers will find fourteen chapters dedicated to optimization-based tools for robustness analysis, and decision-making in relation to feedback mechanisms-fault detection, for example-and three chapters putting forward applications where the model-based optimization brings a novel perspective. Developments in Model-Based Optimization and Control is a selection of contributions expanded and updated from the Optimisation-based Control and Estimation workshops held in November 2013 and November 2014. It forms a useful resource for academic researchers and graduate students interested in the state of the art in predictive control. Control engineers working in model-based optimization and control, particularly in its bioprocess applications will also find this collection instructive.

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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