Jianqiu Zhu

To support high-performance long-pulse plasma discharges, the Lingshu plasma control system (PCS) has been successfully developed and deployed, shifting the focus of current research toward the development and iterative refinement of advanced control algorithms. Simulink is a widely used and powerful tool for control algorithm development. However, integrating Simulink models into the FunctionBlock-based architecture of Lingshu PCS still requires substantial manual adaptation, resulting in low integration efficiency and prolonged deployment cycles. To address this issue, this paper proposes a Simulink-to-FunctionBlock transformation framework for the Lingshu PCS. The framework establishes an automated workflow from model development to system deployment, enabling the direct conversion of Simulink models into deployable FunctionBlocks. By bridging the semantic gaps between Simulink models and the FunctionBlock architecture, the proposed approach significantly reduces manual integration effort and accelerates algorithm deployment. The proposed framework is validated using a plasma control algorithm from the EAST tokamak. Experimental results demonstrate that Simulink models can be successfully transformed into FunctionBlocks and deployed on the Lingshu PCS while preserving the original algorithm behavior and satisfying real-time control requirements, thereby validating the effectiveness of the proposed framework.

Magnetic shape control is fundamental to stable tokamak operation, where the plasma boundary must be maintained with high accuracy and robustness against external disturbances. Experimental observations on EAST indicate that, under large transient-error conditions, a fixed-parameter PID-based controller may drive the actuators into saturation, degrading regulation performance and potentially leading to loss of shape control. To address this limitation, this work develops a deep reinforcement learning (RL) plasma shape controller for EAST within the existing Isoflux control scheme. The controller is trained in a disturbance-rich simulation environment designed to reproduce large transient-error conditions, and employs a tailored reward design that explicitly targets large-disturbance robustness while improving early-stage learning stability under actuator constraints. Simulation results show that the learned policy achieves improved disturbance rejection and higher control accuracy in regimes where PID control tends to saturate and recover slowly. The controller is further deployed and experimentally validated on EAST, demonstrating stable plasma shape regulation with acceptable accuracy and enhanced robustness during long-pulse operation.

To achieve the complex control of high-temperature, nonlinear fusion plasmas, the overall control task is decoupled into multiple parallel sub-tasks. To host these parallel tasks, the modular Lingshu plasma control architecture is designed. Architecturally, a unified data flow management system is implemented to coordinate all parallel components, thereby enabling integrated control over the plasma discharge. This data flow management framework centers on a Data Engine, which uniformly manages intra-component data through a key-value mapping structure. Furthermore, Data Engine integrates a shared memory manager (ShmMgr) to provide efficient, zero-overhead data exchange between components. Upon this, the system features two critical data services: a Just-In-Time Configuration Distribution Service that dynamically resolves and delivers control parameters within each microsecond-level control cycle, and a Real-time Data Archiving Service designed for long-pulse operations, which ensures persistent storage without interfering with deterministic real-time performance. The proposed framework has been validated through extensive experimental campaigns on the EAST. Configuration resolution and data archiving introduce minimal overhead ( μ s and μ s, respectively), meeting strict real-time requirements. A simulated 25 h discharge further confirmed its steady-state operational capability.

In order to obtain the accurate and fast equilibrium reconstruction for Experimental Advanced Superconducting Tokamak (EAST), parallel plasma equilibrium reconstruction (P-EFIT) based on parallel computation in the graphic processing unit (GPU) and the EFIT framework is developed. However, more data will be produced during shots with improved accuracy and rapidity. 76 KB of P-EFIT data are generated in each iteration of 500 μs with computation grid size of 129 x 129. EAST is aimed at 1000-s-long-pulse discharge, then 52 GB of P-EFIT data will be generated during a shot even at 1.5 ms saving interval. In the previous storage mode, these data will be saved in memory during shots and archived to MDSplus as a whole after the shot, which means a very large memory requirement and heavy network transmission load after shots. Therefore, the real-time storage is designed for P-EFIT data to replace the ``after shot archiving'' mode. In the new mode, the P-EFIT processes and real-time archiving process will coordinate and share P-EFIT data gathered from GPU via shared memory, then the P-EFIT data will be archived to MDSplus in real-time during shots based on ``segmented record'' of MDSplus, which provides the possibility to reuse the memory allocated for P-EFIT and save data in higher saving frequency. The real-time storage of P-EFIT data was tested in 10-s discharge and 1000-s-long-pulse simulation discharge, and the validity and the advantage of memory saving have been verified. The real-time storage has been integrated into a plasma control system (PCS) and will be applied to next experiments in EAST. In order to obtain the accurate and fast equilibrium reconstruction for Experimental Advanced Superconducting Tokamak (EAST), parallel plasma equilibrium reconstruction (P-EFIT) based on parallel computation in the graphic processing unit (GPU) and the EFIT framework is developed. However, more data will be produced during shots with improved accuracy and rapidity. 76 KB of P-EFIT data are generated in each iteration of 500 μs with computation grid size of 129 x 129. EAST is aimed at 1000-s-long-pulse discharge, then 52 GB of P-EFIT data will be generated during a shot even at 1.5 ms saving interval. In the previous storage mode, these data will be saved in memory during shots and archived to MDSplus as a whole after the shot, which means a very large memory requirement and heavy network transmission load after shots. Therefore, the real-time storage is designed for P-EFIT data to replace the ``after shot archiving'' mode. In the new mode, the P-EFIT processes and real-time archiving process will coordinate and share P-EFIT data gathered from GPU via shared memory, then the P-EFIT data will be archived to MDSplus in real-time during shots based on ``segmented record'' of MDSplus, which provides the possibility to reuse the memory allocated for P-EFIT and save data in higher saving frequency. The real-time storage of P-EFIT data was tested in 10-s discharge and 1000-s-long-pulse simulation discharge, and the validity and the advantage of memory saving have been verified. The real-time storage has been integrated into a plasma control system (PCS) and will be applied to next experiments in EAST.