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 July 17, 2025
Assuring Safety of Vision-Based Swarm Formation Control
Vision-based formation control systems are attractive because they can use inexpensive sensors and can
work in GPS-denied environments. The safety assurance for such systems is challenging: the vision component’s accuracy depends on the environment in complicated ways, these errors propagate through the system and lead to incorrect control actions, and there exists no formal specification for end-to-end reasoning. We address this problem and propose a technique for safety assurance of vision-based formation control: First, we propose a scheme for constructing quantizers that are consistent with vision-based perception. Next, we show how the convergence analysis of a standard quantized consensus algorithm can be adapted for the constructed quantizers. We use the recently defined notion of perception contracts to create error
bounds on the actual vision-based perception pipeline using sampled data from different ground truth states, environments, and weather conditions. Specifically, we use a quantizer in logarithmic polar coordinates, and we show that this quantizer is suitable for the constructed perception contracts for the vision-based position estimation, where the error worsens with respect to the absolute distance between agents. We build our formation control algorithm with this nonuniform quantizer, and we prove its convergence employing an existing result for quantized consensus.
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- Award ID(s):
- 2008883
- PAR ID:
- 10539109
- Publisher / Repository:
- IEEE
- Date Published:
- Format(s):
- Medium: X
- Location:
- Toronto, Canada
- Sponsoring Org:
- National Science Foundation
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