An intelligent control architecture for accelerator beamline tuning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Model-based tracking for agent-based control systems in the case of sensor failures
International Journal of Automation and Computing
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Tuning and controlling particle accelerators is time consumingand expensive. Inherently nonlinear, this control problem is one towhich conventional methods have not satisfactorily been applied;the result is constant and expensive monitoring by human operators.In recent years, and with isolated successes, advanced informationtechnologies such as expert systems and neural networks have beenapplied to the individual pieces of this problem. Most advancedinformation technology attempts are also very special purpose andbuilt in a manner not at all generalizable to other acceleratorinstallations. In this paper, we discuss preliminary results of ourresearch combining various methodologies from the field ofartificial intelligence into a general control system foraccelerator tuning. We consider state space search andadaptive/learning algorithms including fuzzy logic, rule-basedreasoning, neural networks, and genetic algorithms. We then proposea framework for applying these methods to a general purpose systemfor control. Finally, we discuss future plans for extending thesystem to include parallel distributed reasoning, an enhancedobject structure, and additional heuristic control methods.