Performance Analysis of Learning-Based Disturbance Observer for Pulsed Superconducting Cavity Field Control

The use of Disturbance Observer-based (DOB) control is widespread in stabilizing the electromagnetic field superconducting radio frequency cavities to facilitate beam acceleration particle accelerators. Repetitive disturbances such as loading and Lorentz force cavity detuning are compensated by DOB control, their suppression enhanced through incorporation a learning scheme into conventional disturbance observer. This paper evaluated performance learning-based observer for compensating pulsed proposes modifications better stability. A baseband model π-mode was simulated Matlab/Simulink with trapezoidal pulse input different values analyze controllers’ performance. simulations were conducted multiple filter bandwidths evaluate under plant uncertainties values. results demonstrate that yields faster convergence reference lower tracking errors during flat top voltage comparison control.