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We present a method for dynamics-driven, user-interface design for a human-automation system via sensor selection. We define the user interface to be the output of a multiple-input-multiple-output (MIMO) linear time-invariant (LTI) system and formulate the design problem as one of selecting an output matrix from a given set of candidate output matrices. Necessary conditions for situation awareness are captured as additional constraints on the selection of the output matrix. These constraints depend on the level of trust the human has in the automation. We show that the resulting user-interface design problem is a combinatorial, set-cardinality minimization problem with set function constraints. We propose tractable algorithms to compute optimal or suboptimal solutions with suboptimality bounds. Our approaches exploit monotonicity and submodularity present in the design problem and rely on constraint programming and submodular maximization. We apply this method to the IEEE 118-bus, to construct correct-by-design interfaces under various operating scenarios. 

The performance of hierarchical Model Predictive Control (MPC) is highly dependent on the mechanisms used to coordinate the decisions made by controllers at different levels of the hierarchy. Conventionally, reference tracking serves as the primary coordination mechanism, where optimal state and input trajectories determined by upper-level controllers are communicated down the hierarchy to be tracked by lower-level controllers. As such, significant tuning is required for each controller in the hierarchy to achieve the desired closed-loop system performance. This paper presents a novel terminal cost coordination mechanism using constrained zonotopes, designed to improve system performance under hierarchical control. These terminal costs allow lower-level controllers to balance both short- and long-term control performance without the need for controller tuning. Unlike terminal costs widely used to guarantee MPC stability, the proposed terminal costs are time-varying and computed on-line based on the optimal state trajectory of the upper-level controllers. A numerical example demonstrates the provable performance benefits achieved using the proposed terminal cost coordination mechanism.