3 citations · 5 across the 8 of their papers we have counts for
8 papers
Combinatorial Safety-Critical Coordination of Multi-Agent Systems via Mixed-Integer Responsibility Allocation and Control Barrier Functions
Johannes Autenrieb, Mark Spiller, Hyo-Sang Shin +1
This paper presents a hybrid safety-critical coordination architecture for multi-agent systems operating in dense environments. While control barrier functions (CBFs) provide forma…
Synchronisation-Oriented Design Approach for Adaptive Control
Namhoon Cho, Seokwon Lee, Hyo-Sang Shin
This study presents a synchronisation-oriented perspective towards adaptive control which views model-referenced adaptation as synchronisation between actual and virtual dynamic sy…
Automatic Optimisation of Normalised Neural Networks
Namhoon Cho, Hyo-Sang Shin
We propose automatic optimisation methods considering the geometry of matrix manifold for the normalised parameters of neural networks. Layerwise weight normalisation with respect…
Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip
Saki Omi, Hyo-Sang Shin, Namhoon Cho +2
Reinforcement learning (RL) performs well in continuous-control tasks, but evolving hidden disturbances create partial observability: the agent must infer decision-relevant latent…
A Passivity-Based Method for Accelerated Convex Optimisation
Namhoon Cho, Hyo-Sang Shin
This study presents a constructive methodology for designing accelerated convex optimisation algorithms in continuous-time domain. The two key enablers are the classical concept of…
Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction
Namhoon Cho, Hyo-Sang Shin, Antonios Tsourdos +1
This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved so…
Bayesian Learning Approach to Model Predictive Control
Namhoon Cho, Seokwon Lee, Hyo-Sang Shin +1
This study presents a Bayesian learning perspective towards model predictive control algorithms. High-level frameworks have been developed separately in the earlier studies on Baye…
Optimisation of Structured Neural Controller Based on Continuous-Time Policy Gradient
Namhoon Cho, Hyo-Sang Shin
This study presents a policy optimisation framework for structured nonlinear control of continuous-time (deterministic) dynamic systems. The proposed approach prescribes a structur…