309 papers
This paper proposes a hybrid zonotope-based framework for computing exact and scalable forward and backward reachable sets of closed-loop ReLU RNNs without unrolling, featuring a tunable relaxation scheme to balance computational complexity and approximation accuracy while providing a sufficient condition for safety certification.
This paper proposes a multi-agent deep reinforcement learning framework utilizing virtual anchor nodes for unmanned aerial vehicles to achieve superior accuracy and efficiency in localizing chemical plume sources from undocumented orphaned wells compared to traditional fluxotaxis methods.
This paper proposes a novel rotatable antenna (RA)-enabled covert communication system that jointly optimizes transmit beamforming and RA rotational angles via an alternating optimization algorithm to maximize covert rate while satisfying covertness and power constraints, demonstrating superior performance over benchmark schemes.
This paper presents an optimized C++ implementation of the QPALM-OCP algorithm that exploits the inherent parallel structure of optimal control problems to significantly enhance computational performance through parallelization and vectorization techniques.
This paper proposes a probabilistic forecasting framework using clustered quantile regression on UK low-voltage transformer data to dynamically optimize thermal protection settings, achieving a 10–12% capacity increase while enabling risk-informed operational decisions through direct overheating risk quantification.
This paper proposes an emergency-aware and frequency-constrained HVDC planning method for multi-area asynchronously interconnected grids that integrates a coordinated emergency control scheme and an enhanced system frequency response model to optimize inter-area HVDC capacities while balancing economic efficiency with frequency security requirements.
This paper proposes a robust parametric model predictive control framework for microgrid dispatch that addresses the endogenous uncertainty of battery degradation by integrating an XGBoost-based probabilistic degradation model with tunable penalty weights to optimize the trade-off between operational costs and long-term battery health under varying temperature conditions.
This paper presents an algebraic framework for approximating Markovian open quantum dynamics that guarantees complete positivity and trace preservation by projecting Lindblad generators onto their center manifolds, offering both asymptotically exact unitary reductions and perturbative methods with explicit error bounds while clarifying their connection to adiabatic elimination.
This paper presents a control framework for a morphing-wing drone that enables it to traverse gaps narrower than its wingspan by dynamically sweeping its wings mid-flight, utilizing an aerodynamic model and nonlinear model predictive control to manage lift loss and ensure precise trajectory tracking.
This paper addresses the critical need for accessible validation data in Structural Health Monitoring by introducing an open-source, simulated dataset of a fixed-fixed steel beam that incorporates realistic environmental variations, damage scenarios, and sensor faults to facilitate the development and verification of novel data-driven and hybrid SHM strategies.
This paper proposes a decentralized cooperative localization framework for multi-robot systems in GPS-denied environments that utilizes asynchronous sensor fusion, automatic coordinate alignment, and a dual-landmark evaluation strategy to achieve significantly higher accuracy and robustness compared to centralized methods.
This paper introduces a maximum-entropy random walk framework for directed hypergraphs that models both broadcasting and merging interaction mechanisms through a Kullback-Leibler divergence projection, utilizing Sinkhorn-Schrödinger iterations to infer transition kernels while analyzing ergodicity and demonstrating effectiveness on synthetic and real-world datasets.
This paper proposes a novel event-triggered, data-based adaptation method for Model Predictive Control that utilizes statistical monitoring to detect performance degradation and selectively updates the controller via reinforcement learning and identification, thereby avoiding the pitfalls of continuous updates like catastrophic forgetting, and validates the approach on a district heating system benchmark.
This paper presents a decision-support method that combines modeling-to-generate-alternatives with power flow simulations to design diverse, cost-effective, and socially acceptable carbon-neutral district heating networks that minimize impacts on the electricity grid, as demonstrated through a Dutch case study.
This paper introduces Conformalized Data-Driven Reachability (CDDR), a framework that leverages the Learn Then Test procedure to provide Probably Approximately Correct (PAC) coverage guarantees for reachable set over-approximations in linear and nonlinear systems using only independent and identically distributed calibration data, thereby overcoming the limitations of existing deterministic methods that require known noise bounds or specific system parameters.
This paper establishes the existence of, develops an algorithm for computing, and demonstrates the application of quadratizations that preserve the dissipativity (stability) properties of polynomial ODE systems, thereby enabling more reliable model analysis and control in fields such as systems theory and synthetic biology.
This paper proposes a hybrid control framework that enhances quadrotor resilience in underground environments by using a normalizing flow-based prior as a runtime monitor to dynamically switch between a learning-based controller for efficiency and a safety controller for collision avoidance when encountering out-of-distribution scenarios.
This paper presents a method to construct time-varying ISS-Lyapunov functions from candidate ISS-Lyapunov functions for impulsive systems, thereby combining the ease of construction of the latter with the necessary and sufficient stability guarantees of the former, even in cases of simultaneous continuous and discrete instability.
This paper extends Control Lyapunov Value Functions to disturbed nonlinear systems by defining the Robust CLVF (R-CLVF) to identify the smallest robust control invariant set and guarantee exponential stabilization, while addressing computational challenges through warmstart and system decomposition techniques.
This paper proposes and analyzes stochastic greedy algorithms (MRG, DRG, and Random-WSSA) to efficiently solve budget- and performance-constrained weak submodular sensor selection problems with robustness guarantees, demonstrating their effectiveness in Earth-observing satellite constellation applications.