Towards supervisory Model Predictive Control for circular life support systems in long-term space missions

Area of Science:

• Space exploration technology
• Control systems engineering
• Environmental engineering

Background:

• Regenerative Life Support Systems (LSS) are critical for long-duration space missions, providing essential survival functions.
• The complexity, efficiency, and reliability of LSS pose significant operational challenges.
• Coordinating diverse subsystems (mechanical, chemical, biological, energetic) is key to meeting mission requirements.

Purpose of the Study:

• To propose a supervisory control layer for regenerative LSS.
• To address the challenges of complexity, efficiency, and reliability in LSS operation.
• To validate the proposed control strategy using a comprehensive dynamical model.

Main Methods:

• Development of a nonlinear and time-varying Model Predictive Control (MPC) supervisory layer.
• Creation of a complete dynamical model for the European Space Agency's MELiSSA (Micro-Ecological Life Support System Alternative) project, integrating all compartments and phases (solid, liquid, gas).
• Simulation and control of the MELiSSA system using the MPC approach.

Main Results:

• First-time derivation, simulation, and control of a complete MELiSSA dynamical model via supervisory MPC.
• Successful operation of the controlled MELiSSA system over a 14-week mission.
• Demonstration of system resilience through a simulated failure scenario.

Conclusions:

• The proposed MPC supervisory control layer effectively manages complex regenerative LSS.
• The approach enhances the reliability and efficiency of life support systems for space exploration.
• This work provides a robust framework for controlling future advanced LSS.