Löhr, Johannes and Wehrle, Martin and Fox, Maria and Nebel, Bernhard. (2014) Symbolic domain predictive control. In: Proceedings of twenty-eighth AAAI Conference on Artificial Intelligence and the twenty-sixth Innovative Applications of Artificial Intelligence Conference (AAAI 2014) : 27-31 July 2014, Québec City, Québec, Canada, 3. Palo Alto, Calif., pp. 2315-2321.
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Abstract
Planning-based methods to guide switched hybrid sys-
tems 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 gener-
ate 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 ex-
isting DPC approach is restricted in the sense that a dis-
crete 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.
tems 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 gener-
ate 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 ex-
isting DPC approach is restricted in the sense that a dis-
crete 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.
| Faculties and Departments: | 05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert) |
|---|---|
| UniBasel Contributors: | Wehrle, Martin |
| Item Type: | Conference or Workshop Item, refereed |
| Conference or workshop item Subtype: | Conference Paper |
| Publisher: | AAAI Press |
| Note: | Publication type according to Uni Basel Research Database: Conference paper |
| Language: | English |
| Related URLs: | |
| edoc DOI: | |
| Last Modified: | 20 Nov 2018 10:43 |
| Deposited On: | 05 Jun 2015 08:52 |
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