SMT solvers for embedded hybrid MPC 

Student(en): 
Betreuer: Damian Frick 
Beschreibung: Many practical problems arising from hybrid model predictive control (MPC) can be written as discretetime affine systems with some logic coupling. Such systems include systems controlled by inverters such as electric drives or power converters, systems with discrete states such as gear switching in cars or even systems with nonlinear dynamics, for example friction models for race cars. All of these problems have to be controlled in the millisecond to microsecond range with the code running on an embedded platform that is typically much less powerful than a desktop computer. The resulting MPC problems can be written as mixedinteger quadratic programs, which are known to be NPhard. Therefore solving these MPC problems within a short time poses a significant challenge. In this project we want to explore an alternative way of solving these problems by using tools from software checking, in particular we want so use satisfiability modulo theories (SMT) solvers. The MPC problem can be viewed as a collection of logical statements involving affine functions. SMT solvers check the correctness of a collection of such statements by providing an example that satisfied them, thus providing a feasible trajectory (including control inputs) which can be used for controlling the system. This project has a focus on implementation in MATLAB and/or C/C++, the candidates are expected to be strong in math and programming. Courses from model predictive control and mathematical optimization are a plus. The embedded optimization group at IfA develops methods and tools for numerical optimization on embedded hardware, mostly for use in the model predictive control context. Weitere Informationen 
Professor: Manfred Morari 
Projektcharakteristik: Typ: Art der Arbeit: Semester/Masterthesis 20% Theory 60% Implementation 10% Testing 10% Documentation Voraussetzungen: MPC or Optimization a plus  
Anzahl StudentInnen: 1 Status: done  
Projektstart: 22.02.2016 Semester: Spring 2016 