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Efficient Model Predictive Control for embedded applications
Student(en):

 Betreuer:

Giampaolo Torrisi
Beschreibung:

Solving nonlinear Model Predictive Control problems efficiently is a crucial challenge in control applications. General purpose commercial solvers can be too slow, requiring computational times that are by far larger than the available sampling time. Novel algorithms enable for low-complexity Model Predictive Control. In this project we are looking for a strongly motivated student to develop theoretical and/or new algorithmic features that increase efficiency, for instance:
  • Develop theoretical mathematical results to reduce the complexity of the algorithm. For this task, an excellent knowledge of convex (and possibly non-convex) optimization is required.
  • Automatic code generation for a user-defined problem. This involves detecting the problem and generating C code to solve efficiently the MPC problems. For this task, strong C programming skills are required.
  • Improve the computational part, by exploiting parallelization of operations. A specific background in Computer Science is strongly advised for this task.


Weitere Informationen 		Professor:

Roy Smith
Projektcharakteristik:

Typ:
Art der Arbeit:
Voraussetzungen:
Anzahl StudentInnen:
Status: taken
Projektstart:
Semester:


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