no code implementations • 14 Mar 2024 • Namhoon Cho, Seokwon Lee, Hyo-Sang Shin
In the context of adaptation, model reference adaptive control methods make the state response of the actual plant follow a reference model.
no code implementations • 17 Dec 2023 • Namhoon Cho, Hyo-Sang Shin
The first algorithm utilises automatic differentiation of the objective function along the update curve defined on the combined manifold of spheres.
no code implementations • 29 Jul 2023 • Saki Omi, Hyo-Sang Shin, Namhoon Cho, Antonios Tsourdos
Reinforcement learning has been greatly improved in recent studies and an increased interest in real-world implementation has emerged in recent years.
no code implementations • 20 Jun 2023 • Namhoon Cho, Hyo-Sang Shin
This study presents a constructive methodology for designing accelerated convex optimisation algorithms in continuous-time domain.
no code implementations • 12 Mar 2023 • Jianduo Chai, Shaoming He, Hyo-Sang Shin
This paper investigates the problem of traffic surveillance using an unmanned aerial vehicle (UAV) and proposes a domain-knowledge-aided airborne ground moving targets tracking algorithm.
no code implementations • 30 Dec 2022 • Ruifan Liu, Hyo-Sang Shin, Binbin Yan, Antonios Tsourdos
In many domains such as transportation and logistics, search and rescue, or cooperative surveillance, tasks are pending to be allocated with the consideration of possible execution uncertainties.
no code implementations • 8 Sep 2022 • Namhoon Cho, Hyo-Sang Shin, Antonios Tsourdos, Davide Amato
This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved solution accuracy in satisfying the interim point constraints placed on the performance output variables.
no code implementations • 5 Mar 2022 • Namhoon Cho, Seokwon Lee, Hyo-Sang Shin, Antonios Tsourdos
High-level frameworks have been developed separately in the earlier studies on Bayesian learning and sampling-based model predictive control.
1 code implementation • 17 Jan 2022 • Namhoon Cho, Hyo-Sang Shin
This study presents a policy optimisation framework for structured nonlinear control of continuous-time (deterministic) dynamic systems.
no code implementations • 9 Mar 2021 • Zichao Liu, Jiang Wang, Shaoming He, Hyo-Sang Shin, Antonios Tsourdos
This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem.
no code implementations • 27 Feb 2020 • Vinorth Varatharasan, Alice Shuang Shuang Rao, Eric Toutounji, Ju-Hyeon Hong, Hyo-Sang Shin
An onboard target detection, tracking and avoidance system has been developed in this paper, for low-cost UAV flight controllers using AI-Based approaches.
no code implementations • 27 Feb 2020 • Vinorth Varatharasan, Hyo-Sang Shin, Antonios Tsourdos, Nick Colosimo
The performance of the proposed framework is investigated through empirical tests and compared with that of the model trained with the COCO dataset.
no code implementations • 19 Aug 2019 • Hyo-Sang Shin, Shaoming He, Antonios Tsourdos
This paper aims to examine the potential of using the emerging deep reinforcement learning techniques in flight control.
1 code implementation • 18 Nov 2017 • Inmo Jang, Hyo-Sang Shin, Antonios Tsourdos
This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents.