A FRAMEWORK FOR A GENERAL PURPOSE INTELLIGENT CONTROL SYSTEM FOR PARTICLE ACCELERATORS

TO WHICH CONVENTIONAL CONTROL METHODS CANNOT BE SATISFACTORILY APPLIED. ADVANCED INFORMATION TECHNOLOGIES SUCH AS EXPERT SYSTEMS AND NEURAL NETWORKS HAVE BEEN APPLIED SEPARATELY TO THESE PROBLEMS WITH LIMITED SUCCESS. FEW, IF ANY, OF THESE ADVANCED INFORMATION TECHNOLOGIES HAVE BEEN APPLIED FOR GENERAL USE OR IN A MANNER USABLE BY MULTIPLE ACCELERATOR INSTRALLACTIONS. IN PHASE I EXISTING NEURAL NETWORK AND EXPERT SYSTEM TECHNOLOGIES WILL BE COUPLED TO PROVIDE A GENERAL PURPOSE, INTELLIGENT CONTROL SYSTEM. THIS APPROACH OFFERS A FEASIBLE SOLUTION TO THE COMPLEX CONTROL PROBLEMS ENCOUNTERED IN ACCELERATOR CONTROLS. THESE TECHNOLOGIES WILL BE INTEGRATED WITHIN THE FRAMEWORK OF AN EXISTING COMMERCIAL, GENERAL PURPOSE CONTROL SYSTEM THAT IS EXTENSIVELY USED BY THE ACCELERATOR CONTROL COMMUNITY. ALSO, TYPICAL AND REALISTIC SCENARIOS IN ACCELERATOR CONTROL WILL BE DEFINED USING A COMMERCIAL ACCELERATOR MODELING CODE THAT WILL BE ENHANCED TO INCORPORATE REALISTIC NOISE EFFECTS. THESE SCENARIOS WILL BE USED AS A TEST PLATFORM FOR THE GENERAL PURPOSE CONTROL FRAMEWORK BEING DEVELOPED AS PART OF THIS WORK.