ABSTRACT
We have developed 1 CTL, an on-line nonlinear adaptive controller, to optimize and control the operation of a repetitively-pulsed negative ion source. The controller processes multiple diagnostics, including the beam current waveform, to determine the ion source operating conditions. A figure of merit is constructed that weights beam current magnitude, noise, and pulse-to-pulse stability. The operating space of the ion source is mapped coarsely using automated scan procedures. Then, CTL, using information derived by fitting the sparse operating-space data using the Connectionist Normalized Local Spline artificial neural network (CNLS-net), interactively adjusts four ion-source control knobs (through regulating control loops) to optimize the figure of merit. Once coarse optimization is achieved using CNLS-net’s model of machine parameter space, fine tuning is performed by executing a simplified gradient search algorithm directly on the machine. Beam quality obtained using the neural-net-based adaptive controller is consistently quite good. The search technique has tuned the ion source for near-optimum operation on six cold startups in one to four hours from the time of initial arc.
