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Adaptive Dynamic Programming for Cooperative Control with Incomplete Information

Köpf, Florian; Ebbert, Sebastian; Flad, Michael,; Hohmann, Sören

Abstract (englisch):
There is a trend towards interconnected and complex
dynamical systems that are controlled by more than one
controller. Due to the coupling of the controllers by means of
the system, these interacting controllers need to consider not
only the system dynamics but also the influence of each other.
However, in realistic scenarios, they usually do not exchange all
the information concerning their parameters and control laws
and an exact model of the system dynamics is often hard to
obtain. This is why we consider the challenging setting where
the controllers have no access neither to the parameters of each
other nor to the system dynamics. The controller design is quite
difficult in this scenario, as the final system configuration is
not known during the design process. In this complex scenario,
we propose algorithms where each controller uses Adaptive
Dynamic Programming to adapt its control law. Here, each
controller strives for reaching its individual control objectives,
a setting which can be formulated as a coupled optimization
problem, respectively a dynamic game. As an example, we
consider a vehicle model with tw ... mehr


Zugehörige Institution(en) am KIT Institut für Regelungs- und Steuerungssysteme (IRS)
Publikationstyp Proceedingsbeitrag
Jahr 2018
Sprache Englisch
Identifikator KITopen ID: 1000085390
Erschienen in IEEE International Conference on Systems, Man and Cybernetics (IEEE SMC 2018), Miyazaki, J, October 7-10, 2018
Verlag IEEE, Piscataway, NJ
Schlagworte Cooperative Control, Adaptive Dynamic Programming, Adaptive Optimal Control, Game Theory, Reinforcement Learning
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