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Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. In this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.
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This content will become publicly available on January 1, 2026
Time-Constrained MPC Approach for Switched Nonlinear Systems With Applications to Biomedical Problems
Switched systems are important for modeling biomedical control problems, where the control action can be considered to be a switching signal to select the active mode (e.g., a drug therapy or intervention). Practical implementations impose constraints not only on the variable magnitudes, but also on each mode’s active and inactive times (AT and IT). To address this, a model predictive control strategy is proposed using an enlarged model with integer state variables to track past AT/IT for each mode. Two biomedical applications were selected to demonstrate the controller’s effectiveness through simulations. The results highlight that our approach is suitable for biomedical applications with intricate temporal requirements.
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- Award ID(s):
- 2315862
- PAR ID:
- 10624937
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Control Systems Letters
- Volume:
- 9
- ISSN:
- 2475-1456
- Page Range / eLocation ID:
- 949 to 954
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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