The broad research objective of this project is to develop online (i.e., real-time) adaptive optimal controllers for maritime systems that operate in complex environments where intermittent sensing and actuation render modern online optimal control methods unsuitable. Efforts leverage the success of our recently developed actor-critic-identifier, model-based approximate dynamic programming (ADP) methods for continuous systems that approximate the optimal control through reinforcement learning. This project generalizes these previous efforts by examining hybrid dynamic systems. Such a generalization is required to enable more practical MCM missions that involve a mix of continuous dynamics (i.e., the dynamics of the physical vehicle and mines) and discrete dynamics (e.g., resulting from intermittent communication events, intermittent sensing events, discrete changes in the mine intercept strategy, etc.). The specific aims that motivate the proposed tasks include: characterization of the stability and the robustness of optimal controllers under intermittent sensing and actuation, development of computational methods for the synthesis of feedback controllers under intermittent sensing and control, and development of efficient methods to enable real-time approximation of optimal solutions to multi-agent mine engagement problems under abrupt network topology changes and communication rate constraints.


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