Iterative Learning Control model for a Resistive Wall Mode Active Controller

Sammanfattning: The EXTRAP T2R is a Reversed Field Pinch (RFP) device purposed to conduct magnetic confinement fusion research. Magnetic confinement works on the principle of isolating the hot plasma from the cool walls by locking in the plasma onto the magnetic field lines. Due to the generally unstable nature of plasma, the plasma column will shift off its centered position and encroach upon the cooling walls. To counteract this, a series of magnetic sensor coil are placed around the torus that serve as the input for another set of coils that will generate a magnetic field to force the plasma column back in place. This is a feedback mechanism that uses a Proportional-Integral-Derivative (PID) as the loop gain control mechanisms. While this was effective in stabilizing the system it was discovered that during the start up phase of the experiment there was a repeatable pattern of disturbance. As such it offers the opportunity to make use of a feedforward Iterative Learning Control (ILC) that could provide a much more precise stabilization and occlude the possibility of saturating the feedback coils. In this thesis an ILC system will be built upon the existing PID system, it will be modeled in MATLAB and SIMULINK then run to simulate and gauge its performance.