Jürgen Pannek - Böcker

Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants. An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine – the core of any NMPC controller – works. An appendix covering NMPC software and accompanying software in MATLAB® and C++(downloadable from www.springer.com/ISBN) enables readers to perform computer experiments exploring the possibilities and limitations of NMPC.

Since 2007, the biennial International Conferences on Dynamics in Logistics (LDIC) offers researchers and practitioners from logistics, operations research, production, industrial and electrical engineering as well as from computer science an opportunity to meet and to discuss the latest developments in this particular research domain.

Since 2007, the biennial International Conferences on Dynamics in Logistics (LDIC) offers researchers and practitioners from logistics, operations research, production, industrial and electrical engineering as well as from computer science an opportunity to meet and to discuss the latest developments in this particular research domain.

This contributed volume brings together research papers presented at the 4th International Conference on Dynamics in Logistics, held in Bremen, Germany in February 2014. The growing dynamic poses wholly new challenges: logistics processes and networks must be(come) able to rapidly and flexibly adapt to constantly changing conditions.

These proceedings contain research papers presented at the 5th International Conference on Dynamics in Logistics, held in Bremen, Germany, February 2016. The conference is concerned with dynamic aspects of logistic processes and networks. The spectrum of topics reaches from modeling, planning and control of processes over supply chain management and maritime logistics to innovative technologies and robotic applications for cyber-physical production and logistic systems. The growing dynamic confronts the area of logistics with completely new challenges: it must become possible to describe, identify and analyze the process changes. Moreover, logistic processes and networks must be redevised to be rapidly and flexibly adaptable to continuously changing conditions. The book primarily addresses researchers and practitioners from the field of industrial engineering and logistics, but it may also be beneficial for graduate students.

This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring thepossibilities and limitations of NMPC.The second edition has been substantially rewritten, edited and updated to reflect the significant advances that have been made since the publication of its predecessor, including:•a new chapter on economic NMPC relaxing the assumption that the running cost penalizes the distance to a pre-defined equilibrium;•a new chapter on distributed NMPC discussing methods which facilitate the control of large-scale systems by splitting up the optimization into smaller subproblems;•an extended discussion of stability and performance using approximate updates rather than full optimization;•replacement of the pivotal sufficient condition for stability without stabilizing terminal conditions with a weaker alternative and inclusion of an alternative and much simpler proof in the analysis; and•further variations and extensions in response to suggestions from readers of the first edition.Though primarily aimed at academic researchers and practitioners working in control and optimization, the text is self-contained, featuring background material on infinite-horizon optimal control and Lyapunov stability theory that also makes it accessible for graduate students in control engineering and applied mathematics.

These proceedings contain research presented at the 6th International Conference on Dynamics in Logistics, held in February 2018.The integration of dynamics within the modeling, planning and control of logistic processes and networks has shown to contribute massively to the improvement of the latter.

This contributed volume brings together research papers presented at the 4th International Conference on Dynamics in Logistics, held in Bremen, Germany in February 2014. The growing dynamic poses wholly new challenges: logistics processes and networks must be(come) able to rapidly and flexibly adapt to constantly changing conditions.

These proceedings contain research papers presented at the 5th International Conference on Dynamics in Logistics, held in Bremen, Germany, February 2016.

This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring thepossibilities and limitations of NMPC.The second edition has been substantially rewritten, edited and updated to reflect the significant advances that have been made since the publication of its predecessor, including:•a new chapter on economic NMPC relaxing the assumption that the running cost penalizes the distance to a pre-defined equilibrium;•a new chapter on distributed NMPC discussing methods which facilitate the control of large-scale systems by splitting up the optimization into smaller subproblems;•an extended discussion of stability and performance using approximate updates rather than full optimization;•replacement of the pivotal sufficient condition for stability without stabilizing terminal conditions with a weaker alternative and inclusion of an alternative and much simpler proof in the analysis; and•further variations and extensions in response to suggestions from readers of the first edition.Though primarily aimed at academic researchers and practitioners working in control and optimization, the text is self-contained, featuring background material on infinite-horizon optimal control and Lyapunov stability theory that also makes it accessible for graduate students in control engineering and applied mathematics.