International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / A strategy for addressing the complexity of problem identification and notification by autonomous telemetry monitoring software is discussed. The Extreme Ultraviolet Explorer (EUVE) satellite's science operations center (ESOC) is completing a transition to autonomous operations. Originally staffed by two people, twenty-four hours every day, the ESOC is nearing the end of a phased transition to unstaffed monitoring of the science payload health. To develop criteria for the implementation of autonomous operations we first identified and analyzed potential risk areas. These risk areas were then considered in light of a fully staffed operations model, and in several reduced staffing models. By understanding the accepted risk in the nominal, fully staffed model, we could define what criteria to use in comparing the effectiveness of reduced staff models. The state of the scientific instrument package for EUVE is evaluated by a rule-based telemetry processing software package. In the fully automated implementation, anomalous states are characterized in three tiers: critical to immediate instrument health and safety, non-critical to immediate instrument health and safety, and affecting science data only. Each state requires specific action on the part of the engineering staff, and the response time is determined by the tier. The strategy for implementing this prioritized, autonomous instrument monitoring and paging system is presented. We have experienced a variety of problems in our implementation of this strategy, many of which we have overcome. Problems addressed include: dealing with data dropouts, determining if instrument knowledge is current, reducing the number of times personnel are paged for a single problem, prohibiting redundant notification of known problems, delaying notification of problems for instrument states that do not jeopardize the immediate health of the instrument, assuring a response to problems in a timely manner by engineering staff, and communicating problems and response status among responsible personnel.

The vast majority of the research efforts in finite capacity scheduling over the past several years has focused on the generation of precise and almost exact measures for the working schedule presupposing complete information and a deterministic environment. During execution, however, production may be the subject of considerable variability, which may lead to frequent schedule interruptions. Production scheduling mechanisms are developed based on centralised control architecture in which all of the knowledge base and databases are modelled at the same location. This control architecture has difficulty in handling complex manufacturing systems that require knowledge and data at different locations. Adopting biological control principles refers to the process where a schedule is developed prior to the start of the processing after considering all the parameters involved at a resource involved and updated accordingly as the process executes. This research reviews the best practices in gene transcription and translation control methods and adopts these principles in the development of an autonomous finite capacity scheduling control logic aimed at reducing excessive use of manual input in planning tasks. With autonomous decision-making functionality, finite capacity scheduling will as much as practicably possible be able to respond autonomously to schedule disruptions by deployment of proactive scheduling procedures that may be used to revise or re-optimize the schedule when unexpected events occur. The novelty of this work is the ability of production resources to autonomously take decisions and the same way decisions are taken by autonomous entities in the process of gene transcription and translation. The idea has been implemented by the integration of simulation and modelling techniques with Taguchi analysis to investigate the contributions of finite capacity scheduling factors, and determination of the ‘what if’ scenarios encountered due to the existence of variability in production processes. The control logic adopts the induction rules as used in gene expression control mechanisms, studied in biological systems. Scheduling factors are identified to that effect and are investigated to find their effects on selected performance measurements for each resource in used. How they are used to deal with variability in the process is one major objective for this research as it is because of the variability that autonomous decision making becomes of interest. Although different scheduling techniques have been applied and are successful in production planning and control, the results obtained from the inclusion of the autonomous finite capacity scheduling control logic has proved that significant improvement can still be achieved.

International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / The science payload for NASA's Extreme Ultraviolet Explorer (EUVE) satellite is controlled from the EUVE Science Operations Center (ESOC) at the Center for EUV Astrophysics (CEA), University of California, Berkeley (UCB). The ESOC is in the process of a transition from a single staffed shift to an autonomous, zero-shift, "lights out" science payload operations scenario (a.k.a., 1:0). The purpose of the 1:0 transition is to automate all of the remaining routine, daily, controller telemetry monitoring and associated "shift" work. Building on the ESOC's recent success moving from three-shift to one-shift operations (completed in Feb 1995), the 1:0 transition will further reduce payload operations costs and will be a "proof of concept" for future missions; it is also in line with NASA's goals of "cheaper, faster, better" operations and with its desire to out-source missions like EUVE to academe and industry. This paper describes the 1:0 transition for the EUVE science payload: the purpose, goals, and benefits; the relevant science payload instrument health and safety considerations; the requirements for, and implementation of, the multi-phased approach; a cost/benefit analysis; and the various lessons learned along the way.

In the world of educational satellites, student teams manually conduct operations daily, sending commands and collecting downlinked data. Educational satellites typically travel in a Low Earth Orbit allowing line of sight communication for approximately thirty minutes each day. This is manageable for student teams as the required manpower is minimal. The international Global Educational Network for Satellite Operations (GENSO), however, promises satellite contact upwards of sixteen hours per day by connecting earth stations all over the world through the Internet. This dramatic increase in satellite communication time is unreasonable for student teams to conduct manual operations and alternatives must be explored. This thesis first introduces a framework for developing different Artificial Intelligences to conduct autonomous satellite operations for CubeSat satellites. Three different implementations are then compared using Cal Poly's CP6 CubeSat and the University of Tokyo's XI-IV CubeSat to determine which method is most effective.