In the evolving landscape of systems engineering, a trio of researchers from the University of Michigan—İbrahim Oğuz Çetinkaya, Sajad Khodadadian, and Taylan G. Topçu—have introduced a novel approach to mission engineering that could have significant implications for the energy sector. Their work, published in the journal Systems Engineering, focuses on creating intelligent mission coordination methodologies that can adapt to dynamic and uncertain environments.
The researchers propose a method that integrates high-fidelity digital mission models with reinforcement learning (RL), aiming to improve task allocation and reconfiguration in complex systems. They leverage a digital engineering infrastructure composed of a detailed digital mission model and agent-based simulation. The mission tactics management problem is formulated as a Markov Decision Process (MDP), and an RL agent is trained using Proximal Policy Optimization. The simulation serves as a sandbox where system states are mapped to actions, and the policy is refined based on mission outcomes.
To demonstrate the utility of their RL-based intelligent mission coordinator, the researchers conducted a case study on aerial firefighting. The findings showed that the RL-based coordinator not only surpassed baseline performance but also significantly reduced variability in mission performance. This suggests that digital engineering-enabled mission simulations combined with advanced analytical tools offer a mission-agnostic framework for improving mission engineering practice.
For the energy sector, this research could be particularly relevant in managing complex operations such as disaster response, grid management, and renewable energy integration. For instance, in the event of a natural disaster, an RL-based coordinator could optimize the deployment of emergency response teams and resources. Similarly, in grid management, it could help in dynamically allocating resources to meet demand fluctuations and ensure grid stability. The framework could also be extended to fleet design and selection problems, aiding in the optimization of energy infrastructure and logistics.
The researchers’ work serves as a proof of concept, demonstrating the potential of combining digital engineering with advanced analytical tools to enhance mission engineering practices. As the energy sector continues to evolve, such methodologies could play a crucial role in improving efficiency, reliability, and adaptability in energy systems.
This article is based on research available at arXiv.