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...
Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-...
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 conference focused on the identification, analysis and description of the dynamics of logistics processes and networks. Topics covered range from the modeling and planning of processes, to innovative methods like autonomous control and knowledge management, to the latest technologies provided by radio frequency identification, mobile communication, and networking. The growing dynamic poses wholly new challenges:...
This contributed volume brings together research papers presented at the 4th International Conference on Dynamics in Logistics, held in Bremen, Ger...
These proceedings contain research papers presented at the 5th International Conference on Dynamics in Logistics, held in Bremen, Germany, February 2016.
These proceedings contain research papers presented at the 5th International Conference on Dynamics in Logistics, held in Bremen, Germany, February 20...
Nonlinear model predictive control (NMPC) is widely used in the process and chemical industries and increasingly for applications, such as those in the automotive industry, which use higher data sampling rates.
Nonlinear Model Predictive Control is a thorough and rigorous introduction to 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...
Nonlinear model predictive control (NMPC) is widely used in the process and chemical industries and increasingly for applications, such as those in...
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.
These proceedings contain research presented at the 6th International Conference on Dynamics in Logistics, held in February 2018.The integration of dy...