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In networked control systems, the sensory signals are often quantized before being transmitted to the controller. Consequently, performance is affected by the coarseness of this quantization process. Modern communication technologies allow users to obtain resolution-varying quantized measurements based on the prices paid. In this paper, we consider the problem of joint optimal controller synthesis and quantizer scheduling for a partially observed quantized-feedback linear-quadratic-Gaussian system, where the measurements are quantized before being sent to the controller. The system is presented with several choices of quantizers, along with the cost of using each quantizer. The objective is to jointly select the quantizers and synthesize the controller to strike an optimal balance between control performance and quantization cost. When the innovation signal is quantized instead of the measurement, the problem is decoupled into two optimization problems: one for optimal controller synthesis, and the other for optimal quantizer selection. The optimal controller is found by solving a Riccati equation and the optimal quantizer-selection policy is found by solving a linear program---both of which can be solved offline.
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Communication- and Control-aware Optimal Quantizer Selection for Multi-agent Control
We consider a multi-agent linear quadratic optimal control problem. Due to communication constraints, the agents are required to quantize their local state measurements before communicating them to the rest of the team, thus resulting in a decentralized information structure. The optimal controllers are to be synthesized under this decentralized and quantized information structure. The agents are given a set of quantizers with varying quantization resolutions—higher resolution incurs higher communication cost and vice versa. The team must optimally select the quantizer to prioritize agents with ‘highquality’ information for optimizing the control performance under communication constraints. We show that there exist a sepatation between the optimal solution to the control problem and the choice of the optimal quantizer. We show that the optimal controllers are linear and the optimal selection of the quantizers can be determined by solving a linear program.
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- Award ID(s):
- 1849130
- PAR ID:
- 10515365
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE control systems letters
- ISSN:
- 2475-1456
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/LCSYS.2024.3416858
