SCL
Success Stories
SRA's responsive space expertise has helped organizations like NASA, the U.S. Naval Research Lab, and the U.S. Air Force Research Lab develop innovations solutions for some of their most challenging missions. Below are just a few examples of what SCL can do.
Autonomy for the ORS TacSat Missions
SCL was selected for the combined space- and ground-segment development for the TacSat-2 program by the Air Force Research Lab (AFRL). The SCL system was used for Flight autonomy, Assembly Integration & Test (AI&T), Mission Operations Center, and the remote tracking site.
Low-earth-orbiting (LEO) satellites, require significant onboard and related ground segment autonomy and autonomously controlled ground stations. These TacSats were ideal for SCL, which features unification of ground and space components within the same versatile, control system. Also ideal were SCL's artificial intelligence (AI) capabilities implemented using Expert Systems Technology which had been used successfully on NASA and DoD missions.
Flight control is provided through an onboard database combined with closed-loop control which allows scripts and rules to have visibility into onboard state data and to autonomously react to changes. Ground control is provided both in traditional operator-in-the-loop fashion, and in closed-loop mode using constructs identical to those used onboard. Ground control is accomplishes using a web-centric monitor and control system. Mission planning and tasking leverage the web-centric RIMS system and puts planning and tasking in the hands of the warfighters using a simplified web-browser interface.
The TacSat missions can be viewed as a test-bed for future low-cost, quick-turnaround space missions. Using demonstrably capable COTS products, such as SCL, to perform appropriate control-center functions will remain viable for most upcoming missions. All space missions involve mission planning, orbit propagation, and configuration management, regardless of the sponsoring organization or control-center architecture.
Automated Learning for Real-Time Expert System in
Monitoring and Control
SRA was awarded an STTR through NASA (KSC) to develop a system that would automate the detection of mechanical failures in the Marrotta fuel control valves used in the space shuttle main engines. This 2002 STTR combines two cross-cutting technologies: Florida Institute of Technology's (FIT) Adaptive Machine Learning algorithms (AML) and SCL, a Rule-Based Expert System.
The use of Adaptive Machine Learning (AML) techniques has proven that NASA can populate the SCL Rule-Based Expert System with monitor and control rules for a Space Shuttle Main Engine data stream. This approach keeps the human in the loop but removes mundane tasks, and allows analysis in real-time, post-test, and postflight.
Download NASA's Partnership Summary