Sergio Lucia

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This work presents an optimization-based scheme for the predictive control of systems under uncertainty using multi-stage stochastic optimization and its efficient solution applying scenario decomposition techniques. The approach presented relies on the application of a robust Nonlinear Model Predictive Control (NMPC) scheme that is based on the description(More)
In the last years many research studies have presented simulation or experimental results using Nonlinear Model Predictive Control (NMPC). The computation times needed for the solution of the resulting nonlinear optimization problems are in many cases no longer an obstacle due to the advances in algorithms and computational power. However, NMPC is not yet(More)
This paper studies the reduction of the conservativeness of robust nonlinear model predictive control (NMPC) via the reduction of the uncertainty range using guaranteed parameter estimation. Optimal dynamic experiment design is formulated in the framework of robust NMPC in order to obtain probing inputs that maximize the information content of the feedback(More)
This paper presents a robust nonlinear model predictive control scheme and its application to a batch bioreactor. The approach is based on the description of the uncertainty evolution as a scenario tree. This makes it possible to take explicitly into account the future disturbances and control inputs leading to a non-conservative approach that is not based(More)
Nonlinear Model Predictive control is one of the most promising control strategies in the field of advanced control. It can be used to optimize economic cost functions online satisfying all constraints which makes it very appealing in the context of industrial applications. In the last years, several robust NMPC methods have been presented. Among them,(More)