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This paper investigates a method to improve building control performance via online identification and excitation (active learning process) that does not disrupt normal operations. In previous studies we have demonstrated scalable methods to acquire multi-zone thermal models of passive buildings using a gray-box approach that leverages building topology and measurement data. Here we extend the method to multi-zone actively controlled buildings and examine how to improve the thermal model estimation by using the controller to excite unknown portions of the building dynamics. Comparing against a baseline thermostat controller, we demonstrate the utility of both the initially acquired and improved models with a Model Predictive Control (MPC) framework, which includes weather uncertainty and timevarying temperature set-points. By coupling building topology, estimation, and control routines into a single online framework, we have demonstrated the potential for low-cost scalable methods to actively learn and control buildings for optimal occupant comfort and minimum energy usage, all while using the existing building's HVAC sensors and hardware.

Collision avoidance systems, whether for manned or unmanned aircraft, must reliably prevent collision while minimizing alerts. Deciding what action to execute at a particular instant may be framed as a multiple-objective optimization problem that can be solved offline by computers. Prior work has explored methods of efficiently computing the optimal collision avoidance logic from a probabilistic model of aircraft behavior and a cost function. One potential concern with using a probabilistic model to construct the logic is that the model may not adequately represent the real world. Inaccuracies in the model could lead to vulnerabilities in the system when deployed. This paper evaluates the robustness of collision avoidance optimization to modeling errors.