A Constant Factor Approximation Algorithm for Event-Based Sampling
- 1 July 2007
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in American Control Conference (ACC)
- No. 07431619,p. 305-311
- https://doi.org/10.1109/acc.2007.4282991
Abstract
We consider a control system in which sensor data is transmitted from the plant to a receiver over a communication channel, and the receiver uses the data to estimate the state of the plant. Using a feedback policy to choose when to transmit data, the goal is to schedule transmissions to balance a trade-off between communication rate and estimation error. Computing an optimal policy for this problem is generally computationally intensive. Here we provide a simple algorithm for computing a suboptimal policy for scheduling state transmissions which incurs a cost within a factor of six of the optimal achievable cost.Keywords
This publication has 8 references indexed in Scilit:
- Optimal Estimation with Limited MeasurementsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Suboptimality Bounds in Stochastic Control: A Queueing ExamplePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Sampling of diffusion processes for real-time estimationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Interrupt-based feedback control over a shared communication mediumPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Comparison of Riemann and Lebesgue sampling for first order stochastic systemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Trading computation for bandwidth: reducing communication in distributed control systems using state estimatorsIEEE Transactions on Control Systems Technology, 2002
- Complexity and ApproximationPublished by Springer Science and Business Media LLC ,1999
- Discrete-Time Controlled Markov Processes with Average Cost Criterion: A SurveySIAM Journal on Control and Optimization, 1993