Established in 2005 under support of MŠMT ČR (project 1M0572)

Publications

Merging of Multistep Predictors for Decentralized Adaptive Control

Typ:
Conference paper
Proceedings name:
Proceedings of the American Control Conference
Publisher:
IEEE
Serie:
Seattle
Year:
2008
ISBN:
978-1-4244-2078-0
Keywords:
adaptive control, decentralised control, probability
Anotation:
Decentralized adaptive control is based on the use of many local controllers in parallel, each of them estimating its own local model and pursuing local aims. When each controller designs its strategy using only its model, the resulting control will be suboptimal since local models do not allow prediction of consequences of actions of the neighbors. We use probabilistic formulation of adaptive control to build predictive densities of future outputs. Mutual exchange of these densities on commonly observed variables is proposed to compensate for incompleteness of the local models. The task is to find a procedure how to use such information withing the control strategy design under the constraint that the resulting design procedure is of the same complexity as the one without the exchange. We present an approximate algorithm and illustrate its performance on a simple example.
 
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