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Optimal Performance of Feedback Control Systems with Limited Communication over Noisy Channels
Optimization and Control · Submitted 10 Sep 2006
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim For each case we obtain a sequential decomposition of the optimization problem.
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The abstract, sentence by sentence
A discrete time stochastic feedback control system with a noisy communication channel between the sensor and the controller is considered. The sensor has limited memory. At each time, the sensor transmits encoded symbol over the channel and updates its memory. The controller receives a noisy version of the transmitted symbol, and generates a control action based on all its past observations and actions. This control action action is fed back into the system. At each stage the system incurs an instantaneous cost depending on the state of the plant and the control action. The objective is to choose encoding, memory updating and control strategies to minimize the expected total costs over a finite horizon, or the expected discounted cost over an infinite horizon, or the expected average cost per unit time over an infinite horizon. For each case we obtain a sequential decomposition of the optimization problem. The results are extended to the case when the sensor makes an imperfect observation of the state of the system.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | A discrete time stochastic feedback control system with a noisy communication channel between the sensor and the controller is considered. | background 0.87 | 0.15 | 0.03 | asserted 0.89 |
| s1 | The sensor has limited memory. | background 0.84 | 0.13 | 0.02 | asserted 1.00 |
| s2 | At each time, the sensor transmits encoded symbol over the channel and updates its memory. | method 0.62 | 0.10 | 0.02 | asserted 1.00 |
| s3 | The controller receives a noisy version of the transmitted symbol, and generates a control action based on all its past observations and actions. | method 0.56 | 0.12 | 0.03 | asserted 1.00 |
| s4 | This control action action is fed back into the system. | method 0.62 | 0.14 | 0.02 | asserted 1.00 |
| s5 | At each stage the system incurs an instantaneous cost depending on the state of the plant and the control action. | background 0.73 | 0.19 | 0.02 | asserted 1.00 |
| s6 | The objective is to choose encoding, memory updating and control strategies to minimize the expected total costs over a finite horizon, or the expected discounted cost over an infinite horizon, or the expected average cost per unit time over an infinite horizon. | contribution 0.46 | 0.39 | 0.15 | asserted 0.85 |
| s7 | For each case we obtain a sequential decomposition of the optimization problem. | result 0.72 | 0.94 | 0.38 | asserted 1.00 |
| s8 | The results are extended to the case when the sensor makes an imperfect observation of the state of the system. | contribution 0.73 | 0.89 | 0.07 | asserted 1.00 |
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The paper
Original abstract
A discrete time stochastic feedback control system with a noisy communication channel between the sensor and the controller is considered. The sensor has limited memory. At each time, the sensor transmits encoded symbol over the channel and updates its memory. The controller receives a noisy version of the transmitted symbol, and generates a control action based on all its past observations and actions. This control action action is fed back into the system. At each stage the system incurs an instantaneous cost depending on the state of the plant and the control action. The objective is to choose encoding, memory updating and control strategies to minimize the expected total costs over a finite horizon, or the expected discounted cost over an infinite horizon, or the expected average cost per unit time over an infinite horizon. For each case we obtain a sequential decomposition of the optimization problem. The results are extended to the case when the sensor makes an imperfect observation of the state of the system.
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