Abstract
Many techniques for designing controllers for hybrid systems suffer inherently from the complexity of computation, such that the applicability is limited to relatively small problems. It is often not obvious, however, which particular part of a problem formulation has a dominant impact on the increase of complexity with the problem size. This paper describes a thorough empirical investigation of the sources of complexity for an approach to optimal control of hybrid systems. This approach transforms the control task into a mixed-integer programming problem that is solved by Branch&Bound techniques. The influence of various parameters on the computational costs are investigated for a scalable technical example
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