Online Solvers for Hybrid MPC Using Generalized Disjunctive Programming 

Student(en): 
Betreuer: Damian Frick, Alexander Domahidi 
Beschreibung: Model predictive control (MPC) based on hybrid models that contain both continuous dynamics and logical statements is a very powerful tool for obtaining highperformance controllers that can handle most systems in practice. However, the solution of the hybrid MPC problem is NPhard in general, and it remains unclear how to solve these problems in acceptable runtime on embedded systems. One way is to reformulate them as mixedinteger binary programs and apply standard methods such as branchandbound. The reformulation is intuitive but has one major drawback: its relaxations are not very tight in practice, which leads to worse runtime. The goal of this thesis is to examine the paradigm of Generalized Disjunctive Programming, which allows one to compute tighter relaxations for the case of hybrid MPC problems. The questions to be investigated are: (1) how can we efficiently compute the convex hulls of the relaxations, (2) can we achieve significant speedups by using these tighter relaxations and (3) can the class of systems be restricted such that certain properties can be universally exploited? This project has a strong research component, and the candidates are expected to be strong in math and programming. Courses from mathematical optimization are a prerequisite. 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: Masterthesis 50% Theory 30% Implementation 10% Testing 10% Documentation Voraussetzungen: Optimization  
Anzahl StudentInnen: 1 Status: done  
Projektstart: Spring 2016 Semester: 