Applying Autonomous Control method for Material Circulation to Advanced Life Support System

An Advanced Life Support System (ALS) recycles and circulates substances within a living environment, and makes it possible to sustain life in space. Among those systems, this study addressed a system for recycling the elements C, H, and O in the system, which includes waste processing and food production. We proposed a procedure for such a system that combines automatic generation of scheduling and multi-agent reinforcement learning, based on a hierarchical control method. In this report, a procedure was applied to a simple model and a computer simulation was performed. Specifically, an automatic scheduling procedure was used to create an overall plan for operation of a model that assumed closed circulation. The tanks, processors, and module subsystems were controlled by multi-agent reinforcement learning (MARL) in a decentralized autonomous system. Currently, the calculations are complete for the simplest system, that with two tanks. These results indicate that MARL requires preliminary learning, particularly for emergency events, but otherwise, all learning can be done by MARL on-line.