Decentralized observer-based control for interconnected fractional-order stochastic systems under input saturation using partial state variables

Z Yu, Y Zhang, Y Sun, X Dai - Chaos, Solitons & Fractals, 2023 - Elsevier
This paper deals with issues of stochastic stability and decentralized control for
interconnected fractional-order stochastic systems (IFSSs) with input saturation. At first …

Probabilistic coordination of heterogeneous teams from capability temporal logic specifications

M Cai, K Leahy, Z Serlin… - IEEE Robotics and …, 2021 - ieeexplore.ieee.org
This letter explores coordination of heterogeneous teams of agents from high-level
specifications. We employ Capability Temporal Logic (CaTL) to express rich, temporal …

Decentralized graph-based multi-agent reinforcement learning using reward machines

J Hu, Z Xu, W Wang, G Qu, Y Pang, Y Liu - Neurocomputing, 2024 - Elsevier
In multi-agent reinforcement learning (MARL), it is challenging for a collection of agents to
learn complex temporally extended tasks. The difficulties lie in computational complexity and …

Formal Verification of Digital Twins with TLA and Information Leakage Control

L Huang, LR Varshney, KE Willcox - arxiv preprint arxiv:2411.18798, 2024 - arxiv.org
Verifying the correctness of a digital twin provides a formal guarantee that the digital twin
operates as intended. Digital twin verification is challenging due to the presence of …

Reinforcement Learning for Planning and Scheduling in Aviation

J Hu - 2023 - search.proquest.com
Aviation is a complicated field that involves a wide range of operations, from commercial
airline flights to Unmanned Aerial Systems (UAS). Planning and scheduling are essential …

[PDF][PDF] Optimal Control and Reinforcement Learning for Stochastic Systems under Temporal Logic Specifications

L Li - 2022 - digital.wpi.edu
This thesis aims to explore methods of near-optimal stochastic planning given high-level
formal specifications. High-level formal specifications specify the properties that system …

[BOOK][B] Convex optimization meets formal methods: verification, synthesis, and learning in Markov decision processes

LM Cubuktepe - 2021 - search.proquest.com
This dissertation studies the applicability of convex optimization to the formal verification and
synthesis of systems that exhibit randomness or stochastic uncertainties. These systems can …