Symbolic Domain Predictive Control


  • Johannes Löhr University of Freiburg
  • Martin Wehrle University of Basel
  • Maria Fox King's College London
  • Bernhard Nebel University of Freiburg



domain predictive control, hybrid domains, dynamic systems


Planning-based methods to guide switched hybrid systems from an initial state into a desired goal region opens an interesting field for control. The idea of the Domain Predictive Control (DPC) approach is to generate input signals affecting both the numerical states and the modes of the system by stringing together atomic actions to a logically consistent plan. However, the existing DPC approach is restricted in the sense that a discrete and pre-defined input signal is required for each action. In this paper, we extend the approach to deal with symbolic states. This allows for the propagation of reachable regions of the state space emerging from actions with inputs that can be arbitrarily chosen within specified input bounds. This symbolic extension enables the applicability of DPC to systems with bounded inputs sets and increases its robustness due to the implicitly reduced search space. Moreover, precise numeric goal states instead of goal regions become reachable.




How to Cite

Löhr, J., Wehrle, M., Fox, M., & Nebel, B. (2014). Symbolic Domain Predictive Control. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).