Gregorio E. Drayer

Modeling, Design and Simulation of a Reconfigurable Aquatic Habitat for Life Support Control Research

This paper presents the design, modeling, and simulation of a recongurable aquatic habitat for experiments in regenerative life support automation; it supports the use of aquatic habitats as a small-scale approach to automation experiments relevant to largerscale regenerative life support systems. The habitat consists of a ten-gallon tank with four compartments, containing animal and botanical elements. The water volume serves as the medium through which life-support compounds, like oxygen, are transferred between organisms. A motorized hatch allows reconguration of the system to allow or prevent the exchange of gases with the atmosphere, and enables the study of fail-safe automation mechanisms. Sensors and actuators measure and intervene to regulate life support variables in the water. The model serves as an analytical reference for future tests in hardware settings, and to test advanced control architectures and policies that enable the system to operate safely and with increasing levels of autonomy, allowing for human intervention if necessary. The goal of the aquatic habitat is to enable life support control concepts that may be challenging to test in larger-scale life support systems. The mathematical description of the dynamic model of the system is presented in this paper with results from simulations of a distributed control approach applied to the habitat.

A FAM-based Switched Control Approach for the Automation of Bioregenerative Life Support Systems

Presented at the 41st AIAA International Conference on Environmental Systems (ICES), 17-21 July 2011, Portland, Oregon.

A Granular Multi-Sensor Data Fusion Method for Situation Observability in Life Support Systems

the projection of such state into the near future. This paper presents a multi-sensor data fusion method that collects discrete human-inputs and measurements to generate a granular perception function that supports situation observability. These human-inputs are situation-rich, meaning they combine measurements dening the operational condition of the system with a subjective assessment of its situation. As a result, the perception function produces situation-rich signals that may be employed in user-interfaces or in adaptive automation. The perception function is a fuzzy associative memory (FAM) composed of a number of granules equal to the number of situations that may be detected by human-experts; its development is based on their interaction with the system. The human-input data sets are transformed into a granular structure by an adaptive method based on particle swarms. The paper proposed describes the multi-sensor data fusion method and its application to a ground-based aquatic habitat working as a small-scale environmental system.

Development and Evaluation of User Interfaces for Situation Observability in Life Support Systems

a solution to this problem. Such interfaces are user-centered and allow the human operators to gain situation awareness and intervene if necessary. This paper makes use of a granular multi-sensor data fusion method to develop ecological user interfaces for a small-scale life support system. The methodology is applied to the model of a small-scale aquatic habitat working as a groundbased bioregenerative life support system. Three ecological user interfaces were designed and tested on eight non-expert users. Results show the advantage of using situation-rich signals generated by the granular multi-sensor data fusion method that simplies displays of information to allow for the future design of decision support tools.

Ecophysiological Models in Simulations of an Aquatic Habitat for Closed-Loop Life Support Research

A limitation in closed-loop life support system research is the no-availability of smallscale experimental capacities that may help to better understand the challenges in system closure, integration, and control. Ground-based aquatic habitats are an option for smallscale research relevant to bioregenerative life support systems (BLSS), given that they can operate as self-contained systems enclosing a habitat composed of various species in a single volume of water. This paper elaborates on the modeling, design, and simulation of a recongurable aquatic habitat for experiments in BLSS and automation. It focuses in the process of respiration: higher plants of the species Bacopa Monnieri produce O2 for snails of the genus Pomacea. The snails consume the O2 and generate CO2, which is used by the plants in combination with radiant energy to generate O2 through the process of photosynthesis. The paper expands the description of biological processes by introducing models of ecophysiological phenomena of the organisms involved. The model of the plants include a description of the rate of CO2 assimilation as a function of irradiance. The snails instead are modeled through their rate of consumption, treated as a combination of a constant and a random variable to account for changes in metabolic rates and aestivation. The latter consists in brief periods of torpor of the metabolism of the snails in which oxygen consumption is considerably reduced. Simulations and validation runs with hardware show how these phenomena may act as disturbances for the control mechanisms that aim to maintain safe concentration levels of dissolved oxygen in the habitat.

Human-Expert Data Aggregation for Situation-Based Automation of Regenerative Life Support Systems

a solution to this problem and, for such purpose, this paper elaborates on the aggregation of human-expert data to obtain the granular structure of the FAM. The aggregation process consists of an optimization process based on particle swarms. The result is a three dimensional array with parameters that dene n-dimensional non-interactive granules. Two alternatives are presented in this paper: (1) a four-dimensional optimization algorithm to obtain normal fuzzy sets, and (2) a ve-dimensional alternative that results in subnormal fuzzy sets. The results were obtained with simulations of an aquatic habitat that serves as a small-scale model of a RLSS. The discussion elaborates on which of the two alternatives may be better suited for applications in situation assessment and automation.