As the number of missions to cislunar space increases, the population of space objects in this region is expected to grow, making efficient uncertainty propagation essential for space situational awareness (SSA). This is complicated by the cislunar domain's vastness, chaotic dynamical environment, and limited availability of measurements. This paper presents an adaptive multi-fidelity uncertainty propagation method that dynamically adjusts the included perturbing forces based on position in cislunar space, minimizing computation time while maintaining a prescribed modeling accuracy. The proposed adaptive method is then integrated into a multi-target tracking framework to reduce the computational cost of track prediction without sacrificing accuracy, which is important for managing the growing number of objects in cislunar space. The effectiveness of the approach is demonstrated in simulated test cases relevant to upcoming cislunar missions and SSA applications, resulting in a significant reduction in computational cost compared to a non-adaptive approach while achieving equivalent or superior accuracy.
In this paper, we aim to automate the adjustment of air handling unit (AHU) setpoints within heating, ventilation, and air conditioning (HVAC) systems to maintain indoor temperatures at user-specified levels. A key challenge lies in obtaining sufficient high-quality sensor data f…
Process industries have accumulated validated specialist models, yet sensor drift, feedstock variation, and regime switching cause these models to degrade systematically in new scenarios. Collecting new labeled data and retraining is costly, while continuing with the original mod…
With large-scale integration of emerging power electronic devices represented by grid-forming inverters, power system dynamics increasingly exhibit strong nonlinearity, multi-timescale coupling, and black-box control logic. These features hinder conventional parameter identificat…
We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decomposes the design task into constrained code-generation steps: plant-interface construction, normalization, manipulated-variable…
Feasibility-restoration layers turn learned, market-based, or optimizer-generated decisions into actions satisfying hard constraints in systems such as power grids. Yet a repair is only as trustworthy as its constraint model: line parameters, sensitivities, ratings, and topology…
Accurately modeling the dynamics of planetary parachute and entry vehicle systems is critical for Entry, Descent, and Landing events such as vehicle separation and sensor activation. These dynamics are difficult to capture with traditional system-identification methods as parachu…