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Important to NASA’s Advanced Life Support program is the development of an autonomous, dynamic, self-contained bioregenerative life support system for future, long duration spacecraft and space stations to provide fresh food, air, water and to recycle waste products. These systems will rely on plants to rejuvenate the air and produce food through the process of photosynthesis and purify water through the process of transpiration. An intelligent, autonomous, reliable, and robust control system must be developed and applied to dynamically manage, control and optimize plant-based life support functions to allow the efficient growth of plants, providing the maximum amount of life essentials while using minimal resources. System identification and modeling of plant growth behavior must first be developed to characterize plant growth functions in order to develop an efficient control system.

The goal of Advanced Life Support Systems (ALSS) is to provide self-sufficiency in life support for productive research and exploration in space. Important in reaching this goal is the production of crop plants in one or more controlled environments for the purpose of providing life essential food, air, and water to a human crew. To do this reliably and efficiently, it is necessary to achieve control of the rates of various plant physiology processes. To develop an efficient control system that will be able to manage, control, and optimize plant-based life support functions, system identification and modeling of plant growth behavior must first be accomplished. We have developed a plant growth (physiology) model using artificial neural networks. Neural networks are suitable for both steady-state and dynamic modeling and identification tasks, since they can be trained to approximate arbitrary nonlinear input-output mappings from a collection of input and output examples.