Packet based Inference and Control

Communication constraints in Networked Control systems are frequently limits on data packet rates. To efficiently use the available packet rate budgets, we have to resort to event-triggered packet transmission. We have to sample signal waveforms…


1 Estimation and Control with Data-rate Constraints
1.1 A collection ofmotivating examples
1.1.1 Controller Area Networks (CANs) in Automobiles:
1.1.2 The CEO problem:
1.1.3 A splurge of sensors:
1.1.4 Collaborative Sensing and Control
1.2 Packetization ofmeasurements
1.3 Sampling strategy: predetermined or adaptive ?
1.4 Contributions of this thesis
2 Finite Sampling for Real-Time Estimation
2.1 Sampling by a single sensor
2.1.1 The sampling problem
2.1.2 Scheduling a single packet from an ideal sensor – Decoupling the sampling strategy fromthe filter
2.1.3 Keeping track of a scalar Ornstein-Uhlenbeck process
2.2 The single sample case
2.2.1 Optimum deterministic sampling
2.2.2 Optimum threshold sampling
2.2.3 Optimal sampling
2.2.4 Comparisons
2.3 Multiple samples for aWiener process
2.3.1 Deterministic sampling
2.3.2 Level triggered sampling
2.3.3 Optimal multiple sampling
2.3.4 Comparisons
2.4 Sampling an Ornstein-Uhlenbeck process N-times
2.4.1 Optimal deterministic sampling
2.4.2 Optimal Level-triggered sampling
2.4.3 Optimal Sampling
3 Average Cost Repeated Sampling for Filtering
3.1 Introduction
3.2 Real-time Estimation
3.3 Optimal repeated sampling
3.3.1 Solving the single stopping problem
3.3.2 Performance gains
4 Average Cost Control with Level-triggered Sampling
4.1 Introduction
4.2 Average cost control problem
4.3 Optimal control under periodic sampling
4.3.1 Equivalent discrete time ergodic control problem
4.4 Level-triggered sampling
4.4.1 Equivalent Discrete-timeMarkov chain
4.4.2 Existence of Optimal Controls and their Computation
4.5 Comparisons
5 Sampling in Teams for Sequential Hypothesis Testing
5.1 Event-triggered sampling in a teamof sensors
5.1.1 RelatedWorks
5.2 The Optimal Sampling Problem
5.2.1 The Likelihood ratio processes
5.2.2 Sampling strategies allowed
5.2.3 Detection performance
5.3 Threshold sampling policies
5.3.1 Useful notation
5.3.2 Synchronous threshold sampling
5.3.3 Tandem Threshold sampling
5.3.4 Optimal Threshold Sampling
5.4 Relative Performances and Conclusion
6 Conclusions
6.1 Finite horizon estimation
6.2 Estimation on an infinite horizon
6.3 Average cost Control
6.4 Sequential detection in teams
A Expectations of some variables at hitting time

Author: Rabi, Maben

Source: University of Maryland

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