CoDeveloper: A Secure, Web-Invocable Collaborative Software Development Tool

65210 Despite large prior investments in control hardware and software, particle accelerator operations still rely to a great extent on human intervention to adapt and respond to changing conditions, reducing both the efficiency and reliability of operations. This project will develop probabilistic methods that will extend the ability of model-based control systems to adapt and respond to changing conditions. The effort will leverage recent advances in (1) intelligent control and automated methods for error correction and (2) calibration of an accelerator model, closing the loop between adaptive modeling and intelligent control. Phase I will design and prototype a system that integrates intelligent control and adaptive modeling. This system will be designed towards meeting the requirements for control of a high energy heavy ion accelerator operations at Argonne National Laboratory. The prototype will demonstrate the ability to adapt and improve the effectiveness of a control model over time through the application of probabilistic learning methods. Commercial Applications And Other Benefits as described by awardee: The application of new technologies in adaptive modeling and intelligent control should yield significant benefits to DOE accelerator facilities in reducing machine startup and tuning time, automating trouble¿shooting and maintenance, improving performance efficiency, and reducing operational costs and staffing requirements. These automation techniques also should address needs in the industrial control market, significantly enhancing the competitiveness of U.S. industry