  In the field of magnetic confinement plasma control, the accurate feedback of
plasma position and shape primarily relies on calculations derived from
magnetic measurements through equilibrium reconstruction or matrix mapping
method. However, under harsh conditions like high-energy neutron radiation and
elevated temperatures, the installation of magnetic probes within the device
becomes challenging. Relying solely on external magnetic probes can compromise
the precision of EFIT in determining the plasma shape. To tackle this issue, we
introduce a real-time, non-magnetic measurement method on the HL-3 tokamak,
which diagnoses the plasma position and shape via imaging. Particularly, we put
forward an adapted Swin Transformer model, the Poolformer Swin Transformer
(PST), to accurately and fastly interpret the plasma shape from the
Charge-Coupled Device Camera (CCD) images. By adopting multi-task learning and
knowledge distillation techniques, the model is capable of robustly detecting
six shape parameters under disruptive conditions such as a divertor shape and
gas injection, circumventing global brightness changes and cumbersome manual
labeling. Specifically, the well-trained PST model capably infers R and Z
within the mean average error below 1.1 cm and 1.8 cm, respectively, while
requiring less than 2 ms for end-to-end feedback, an 80 improvement over the
smallest Swin Transformer model, laying the foundation for real-time control.
Finally, we deploy the PST model in the Plasma Control System (PCS) using
TensorRT, and achieve 500 ms stable PID feedback control based on the
PST-computed horizontal displacement information. In conclusion, this research
opens up new avenues for the practical application of image-computing plasma
shape diagnostic methods in the realm of real-time feedback control.
