A granular approach to the automation of bioregenerative life support systems that enhances situation awareness

Abstract

Bioregenerative life support systems introduce novel challenges for the development of model-based approaches to their control given the varying characteristic of the biological processes that constitute them. Switching control paradigms provide an alternative to manage such uncertainty by allowing flexibility into the control path, enabling different control modes depending on the situation of the system. This paper presents a perceptionbased approach that combines sensor information to define those conditions and act upon them. Combined sensor information creates sensing spaces in which the operational conditions of the system are found. The decomposition of the sensing spaces into perceptual elements or granules allows for situation assessment, system integration strategies, and the implementation of fail-safe and fail-operational mechanisms -all these critical in a wider range of complex socio-technical systems. This paper proposes the use of intelligent agents based on fuzzy associative memories (FAM's) to decompose sensing spaces into granular structures composed of n-dimensional non-interactive fuzzy sets. Granular structures resulting from such decomposition allow for the incremental development and automation of the system by associating a control task to each operational condition. Furthermore, the real-time information obtained from the membership value of the granules may provide a resource for situational awareness and for the design of new ecological interfaces to enhance humansystem interaction and real-time decision making. The approach presented in this paper is applied to the dynamic model of a reconfigurable aquatic habitat that serves as a small-scale bioregenerative test bed for life support control research. Results show how information generated by the FAM enhances the situation observability of the system.

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

“…The situation-rich signals generated by the FAM-based agent take values between 0 and 1, and can be used as switching signals to activate or inhibit controllers driving each actuator in the system in each situation. 4 These signals are also useful to develop decision support tools and ecological user interfaces. 20 They are indeed an approach to data abstraction that may find applications in mission control consoles, astronaut decision aids, and anomaly detection.…”

“…The granular approach employed makes use of the FAM-based agent architecture. 4 The architecture is characterized by a (1) perception function, (2) a set of controllers, (3) and a correspondence function. The latter associates controllers to each situation that is detected by the perception function and combines them into a single integrated control signal.…”

“…This paper proposes a multi-sensor fusion method that elaborates on a granular approach to these challenges. 4 It makes use of sensor data and expert assessments to generate a granular perception function in support of situation awareness. The method employs an agent architecture based on FAM 4 in an effort to allow for situation observability, i.e.…”

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

“…3 Figure 1 shows a diagram of a single FAMbased agent with a user interface manipulating a single variable in the aquatic habitat. The architecture is composed of a perception function, a set of controllers, and a correspondence function.…”

“…This paper makes use of a granular approach to these challenges that enhances situation observability by transforming abundant sensor information in situation-rich signals. 3 This paper makes use of these signals in ecological user interfaces to allow non-expert users to gain situation awareness and intervene. The methodology is applied to the model of a small-scale aquatic habitat working as a closed-loop LSS.…”