321 papers
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.
This paper proposes a graph signal processing framework for reversible Markov chains that defines optimal graph filters—specifically Bernstein, Chebyshev, and Legendre filters—to significantly accelerate the convergence of ergodic averages compared to traditional methods.
This paper proposes a risk-aware autonomous driving framework that extends linear temporal logic specifications to incorporate event timing and severity, formulating a linear programming problem to synthesize policies that balance collision risks and traffic rule violations, as validated in the Carla simulator.
This paper establishes necessary and sufficient conditions for the input-to-state stability (ISS) of impulsive switched systems with both stable and unstable modes by introducing time-varying ISS-Lyapunov functions under relaxed mode-dependent average dwell and leave time constraints, while also providing methods to construct decreasing Lyapunov functions and guarantee ISS even with unknown switching signals.
This paper introduces C*, a novel real-time coverage path planning algorithm for unknown environments that utilizes a Rapidly Covering Graph to ensure complete, near-optimal coverage with minimal computational complexity, significantly outperforming existing methods in efficiency and adaptability for both single and multi-robot systems.
This paper introduces LUCIFER, an inference-only middleware that decouples online language grounding from decision-making via a Signal Contract, enabling autonomous systems to robustly convert evolving human verbal reports into control-relevant signals that improve safety and information efficiency across diverse planning architectures.
This paper introduces a dual-stress framework combining synaptic filtering and adversarial attacks to classify and quantify the fragility, robustness, and antifragility of parameters in PPO-trained RL agents, revealing that targeted filtering can enhance policy adaptability in continuous control environments.
This paper presents a sensorless 4-channel bilateral teleoperation framework that enables stable, high-speed force feedback control on low-cost manipulators through disturbance-observer-based estimation and simplified tuning, ultimately demonstrating that such force-enhanced data significantly improves imitation learning performance.
This paper introduces CBF-RL, a framework that integrates Control Barrier Functions directly into the reinforcement learning training process to internalize safety constraints, thereby enabling safe, robust, and filter-free deployment of policies in real-world scenarios like humanoid robot navigation.