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 • 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.