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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This content will become publicly available on June 17, 2025
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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