41 research outputs found
Transmission Power Scheduling for Energy Harvesting Sensor in Remote State Estimation
We study remote estimation in a wireless sensor network. Instead of using a
conventional battery-powered sensor, a sensor equipped with an energy harvester
which can obtain energy from the external environment is utilized. We formulate
this problem into an infinite time-horizon Markov decision process and provide
the optimal sensor transmission power control strategy. In addition, a
sub-optimal strategy which is easier to implement and requires less computation
is presented. A numerical example is provided to illustrate the implementation
of the sub-optimal policy and evaluation of its estimation performance.Comment: Extended version of article to be published in the Proceedings of the
19th IFAC World Congress, 201
Optimal Local and Remote Controllers with Unreliable Communication
We consider a decentralized optimal control problem for a linear plant
controlled by two controllers, a local controller and a remote controller. The
local controller directly observes the state of the plant and can inform the
remote controller of the plant state through a packet-drop channel. We assume
that the remote controller is able to send acknowledgments to the local
controller to signal the successful receipt of transmitted packets. The
objective of the two controllers is to cooperatively minimize a quadratic
performance cost. We provide a dynamic program for this decentralized control
problem using the common information approach. Although our problem is not a
partially nested LQG problem, we obtain explicit optimal strategies for the two
controllers. In the optimal strategies, both controllers compute a common
estimate of the plant state based on the common information. The remote
controller's action is linear in the common estimated state, and the local
controller's action is linear in both the actual state and the common estimated
state
Structural Results for Decentralized Stochastic Control with a Word-of-Mouth Communication
In this paper, we analyze a network of agents that communicate through the
``word of mouth," in which, every agent communicates only with its neighbors.
We introduce the prescription approach, present some of its properties and show
that it leads to a new information state. We also state preliminary structural
results for optimal control strategies in systems that evolve using
word-of-mouth communication. The proposed approach can be generalized to
analyze several decentralized systems