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Developments in Model-based Optimization and Control : Distributed Control and Industrial Applications
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This book deals with optimization methods as tools for decision making and control in the presence of uncertainty. It is oriented to the use of these tools in engineering and, specifically, in automatic control design with all its components: analysis of dynamical systems, estimation 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
The various contributions cover a subject spectrum including explicit and more modern decentralized and cooperative formulations of receding-horizon optimal control. Readers will find thirteen chapters dedicated to optimization-based tools for robustness analysis, and decision-making in relation to feedback mechanisms—fault detection, for example—and three to applications.
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 2012 and November 2013. 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 predictive control, particularly in its bioprocess applications will also find this collection instructive.