11,263 research outputs found
Probably Approximately Correct Nash Equilibrium Learning
We consider a multi-agent noncooperative game with agents' objective
functions being affected by uncertainty. Following a data driven paradigm, we
represent uncertainty by means of scenarios and seek a robust Nash equilibrium
solution. We treat the Nash equilibrium computation problem within the realm of
probably approximately correct (PAC) learning. Building upon recent
developments in scenario-based optimization, we accompany the computed Nash
equilibrium with a priori and a posteriori probabilistic robustness
certificates, providing confidence that the computed equilibrium remains
unaffected (in probabilistic terms) when a new uncertainty realization is
encountered. For a wide class of games, we also show that the computation of
the so called compression set - a key concept in scenario-based optimization -
can be directly obtained as a byproduct of the proposed solution methodology.
Finally, we illustrate how to overcome differentiability issues, arising due to
the introduction of scenarios, and compute a Nash equilibrium solution in a
decentralized manner. We demonstrate the efficacy of the proposed approach on
an electric vehicle charging control problem.Comment: Preprint submitted to IEEE Transactions on Automatic Contro
Guaranteed Cost Tracking for Uncertain Coupled Multi-agent Systems Using Consensus over a Directed Graph
This paper considers the leader-follower control problem for a linear
multi-agent system with directed communication topology and linear nonidentical
uncertain coupling subject to integral quadratic constraints (IQCs). A
consensus-type control protocol is proposed based on each agent's states
relative to its neighbors and leader's state relative to agents which observe
the leader. A sufficient condition is obtained by overbounding the cost
function. Based on this sufficient condition, a computational algorithm is
introduced to minimize the proposed guaranteed bound on tracking performance,
which yields a suboptimal bound on the system consensus control and tracking
performance. The effectiveness of the proposed method is demonstrated using a
simulation example.Comment: Accepted for presentation at the 2013 Australian Control conferenc
- …