NMPC 2018 - 6th IFAC International Conference on Nonlinear Model Predictive Control - NMPC 2018

Model predictive control (MPC) is indisputably one of the advanced control techniques that has significantly affected control engineering practice with thousands of controllers implemented in various fields, spanning from process industry to automotive and robotics. This is due to its unique ability to handle constraints, take preview information about disturbances and set point changes into account and optimize performance. Furthermore, MPC allows the consideration of various types of dynamics, from linear to nonlinear, discrete to continuous, as well as on-line adaptation of models and parameters. The research community on MPC is very active, with hundreds of papers published every year. Over the past decades many theoretical, as well as implementation results have been achieved.
The IFAC Conference on Nonlinear Model Predictive Control (NMPC 2018) aims at bringing together researchers interested and working in the field of MPC, from both academia and industry. This allows to reflect and establish the current state-of-the-art and focus the future development of the MPC field towards relevant directions.
Major conference topics are:
Uncertainty and model predictive control
Stability and recursive feasibility of nonlinear model predictive control
Economic model predictive control
Model predictive control in the cloud
IoT and model predictive control
Learning and model predictive control
Stochastic model predictive control
Model predictive control for hybrid systems
Model predictive control for large scale systems
Hierarchical and decentralized predictive control
Embedded and real-time feasible predictive control
Optimization and model predictive control
Model predictive control of mechatronic systems
Heating, ventilation, air conditioning (HVAC) control
Energy systems and model predictive control
Autonomous systems and model predictive control
Model predictive control for autonomous driving
Model predictive control for aeronautic applications
Model predictive control for automotive systems
Process control and model predictive control
Model predictive control for medical applications
Biotechnology and model predictive control
Model predictive control for smart manufacturing and industry 4.0