The objective of the proposed research is to study the integration of humans and automation for the operation of regenerative life support systems (RLSS). RLSS combine physico-chemical and biological processes with the purpose of increasing the autonomy of space habitats and the life quality of their living organisms by properly reusing byproducts and regenerating consumable resources. However, these processes require energy and time to transform chemical compounds and organic wastes into nutrients, consumables, and edible products. Consequently, the maintenance of RLSS imposes a considerable workload on human operators. In addition, the uncertainties introduced by unintended chemical reactions promoted by material loop closure may create unexpected situations that, if unattended, could translate into performance deterioration, human errors, and failures. The availability of novel chemical and biological sensors together with computational resources enable the development of monitoring and automation systems to alleviate human workload, help avoid human error, and increase the overall reliability of these systems. This research aggregates sensor data and human-expert situation assessments to create a representation of their situation knowledge base (\gloss{skb}). The representation is used in a switched control approach to the automation of RLSS, for decision support, and human-automation coordination. The aggregation method consists of an optimization process based on particle swarms. The purpose of this work is to contribute to the methodological development of situation-oriented and user-centered design approaches to human-automation systems. Experiments and simulations are supported on the process of respiration in an aquatic habitat acting as a RLSS.