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A Lunar habitat will be highly sensored and generate large amounts of data or telemetry. For this data to be useful to humans monitoring these systems and to automated algorithms controlling these systems it will need to be converted into more abstract data. This abstracted data will reflect the trends, states and characteristics of the systems and their environments. Currently this data abstraction process is manual and ad hoc. We are developing a Data Abstraction Architecture (DAA) that allows engineers to design software processes that iteratively convert habitat data into higher and higher levels of abstraction. The DAA is a series of mathematical or logical transformations of telemetry data to provide appropriate inputs from a hardware system to a hardware system controller, system engineer, or crew. The DAA also formalizes the relationships between data and control and the relationships between the data themselves.

High costs and extreme risks prevent the life testing of NASA hardware. These unavoidable limitations prevent the determination of sound reliability bounds for NASA hardware; thus the true risk assumed in future missions is unclear. A simulation infrastructure for determining these risks is developed in a configurable format here. Positive preliminary results in preparation for validation testing are reported. A stochastic filter simulates non-deterministic output from the various unit processes. A maintenance and repair module has been implemented with several levels of complexity. Two life testing approaches have been proposed for use in future model validation.

International Space Station life support hardware is controlled mainly from the ground by executing standard operating procedures. While some on-board software exists for safety purposes, most commands are sent from ECLSS ground controllers to achieve mission objectives. This will prove unwieldy for extended operations with increasing time delays. This paper presents a new approach to encoding standard operating procedures that provides a path to greater autonomy in life support operations. Software tools will allow for adjustable automation of procedures from either the ground or on-board. The Cascade Distiller System (CDS) being tested at NASA Johnson Space Center is used as an example system.

This paper describes the re-engineering of the hardware and software organization of an autonomous control system for advanced water recovery technology development. The hardware changes from large standing racks of VERSAModuleEurocard (VME) computers and I/O cards to a set of small, modular controllers embedded in the hardware of the controlled systems. Additionally, the paper describes the redesign of an existing intelligent control architecture, known as 3T, that since 1995 has provided autonomous 24/7 controls to several long-duration life-support systems in ground tests at JSC. The software is recast from a vertical, message-passing architecture to a distributed system which can populate the embedded hardware controllers in many alternative arrangements. Additional controllers allow for automatically migrating control codes when the original controllers fail.

BioSim is a simulation tool which captures many basic life support functions in an integrated simulation. Conventional analyses can not efficiently consider all possible life support system configurations. Heuristic approaches are a possible alternative. In an effort to demonstrate efficacy, a validating experiment was designed to compare the configurational optima discovered by heuristic approaches and an analytical approach. Thus far, it is clear that a genetic algorithm finds reasonable optima, although an improved fitness function is required. Further, despite a tight analytical fit to data, optimization produces disparate results which will require further validation.

A Lunar habitat will require a level of automation that is much greater than previous human space missions. The complexity of the habitat, the distance (and time delay) between the habitat and ground controllers and the fact that the habitat may be uncrewed for periods of time all point towards increased automation of the habitat. NASA JSC is developing an integrated testbed for exploring operational concepts for a Lunar habitat that includes significant automation. The testbed allows for early investigation of the hardware and software design decisions and their impacts on operating a Lunar habitat. The testbed also allows for investigation into the robustness of different automation concepts with respect to failures and perturbations of the system. The testbed consists of both dynamic simulations of habitat systems and some physical hardware-in-the-loop.

A reconfigurable control system is an intelligent control system that detects faults within the system and adjusts its performance automatically to avoid mission failure, save lives, and reduce system maintenance costs. The concept was first successfully demonstrated by NASA between December 1989 and March 1990 on the F-15 flight control system (SRFCS), where software was integrated into the aircraft's digital flight control system to compensate for component loss by reconfiguring the remaining control loop. This was later adopted in the Boeing X-33. Other applications include modular robotics, reconfigurable computing structure, and reconfigurable helicopters. The motivation of this work is to test such control system designs for future long term space missions, more explicitly, the automation of life support systems.

Environmental control and life support systems are usually associated with high demands for performance robustness and cost efficiency. However, considering the complexity of such systems, determining the balance between those two design factors is nontrivial for even the simplest space missions. Redundant design is considered as a design optimization dilemma since it usually means higher system reliability as well as system cost. Two coupled fundamental questions need to be answered. First, to achieve certain level of system reliability, what is the corresponding system cost? Secondly, given a budget to improve system reliability, what is the most efficient design for component or subsystem redundancy? The proposed analysis will continue from previous work performed on series systems by expanding the scope of the analysis and testing parallel systems. Namely, the online and offline redundancy designs for a Lunar Outpost Mission are under consideration.

This paper describes a simulation of an integrated advanced life support system. It contains models of the major components of an Advanced Life Support (ALS) system including crew, biomass, water recovery, air revitalization, food processing and power supply. The simulation also models malfunctions and stochastic processes. Sensors and actuators are modeled to allow controllers to interact with the simulation. The simulation is designed for testing and evaluation of life support control approaches. We use an example of a simple genetic algorithm to demonstrate how a control application might use the simulation. The simulation is implemented in Java to make it portable and easy to use.

This paper describes the role of transient simulations, heuristic techniques, and closed loop integrated control in designing and sizing habitat life support systems. The integration of these three elements allows for more accurate requirements to be derived in advance of hardware choices. As a test case, we used a typical lunar surface habitat. Large numbers of habitat configurations were rapidly tested and evaluated using automated decision support tools. Through this process, preliminary sizing for habitat life support systems were derived. Our preliminary results show that by using transient simulations and closed loop control, we substantially reduced the system mass required to meet mission goals. This has greater implications for general systems analyses and for life support systems.

Regenerative life support systems for long duration human space exploration missions present unique design challenges that are also reflected in constructing behavior models of these systems for analysis purposes. These systems have multiple modes of operation and complex non-linear dynamics that occur at multiple time scales. Coarse grained analysis of the complete system over long durations and fine grained temporal analysis of smaller system elements while avoiding computational intractability can be achieved by using multiple modeling and simulation paradigms. We present a multi-level simulation model of an advanced life support system. The simulation model couples a discrete-event approach at the system level, with more detailed hybrid (continuous/discrete) physical system modeling at the sub-system level.

The paper describes an approach to the integration of qualitative and quantitative modeling techniques for advanced life support (ALS) systems. Developing reliable control strategies that scale up to fully integrated life support systems requires augmenting quantitative models and control algorithms with the abstractions provided by qualitative, symbolic models and their associated high-level control strategies. This will allow for effective management of the combinatorics due to the integration of a large number of ALS subsystems. By focusing control actions at different levels of detail and reactivity we can use faster, simpler responses at the lowest level and predictive but complex responses at the higher levels of abstraction. In particular, methods from model-based planning and scheduling can provide effective resource management over long time periods.

Advanced life support systems have many interacting processes and limited resources. Controlling and optimizing advanced life support systems presents unique challenges that are addressed in this paper. We have developed a controller using reinforcement learning [Barto&Sutton], which actively explores the space of possible control strategies, guided by rewards from a user specified long term objective function. We evaluated this controller using Biosim, our discrete event simulation of an advanced life support system. This simulation supports all life support modules including crew, food production, air revitalization, water recovery, solid waste incineration and power. Our algorithm for reinforcement learning discovered unobvious strategies for maximizing mission length. By exploiting nonlinearities in the simulation dynamics, the learned controller outperforms a controller designed by an expert.