Teawon Han
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View article: Direct Data Driven Control Using Noisy Measurements
Direct Data Driven Control Using Noisy Measurements Open
This paper presents a novel direct data-driven control framework for solving the linear quadratic regulator (LQR) under disturbances and noisy state measurements. The system dynamics are assumed unknown, and the LQR solution is learned usi…
View article: Dynamic and Interpretable State Representation for Deep Reinforcement Learning in Automated Driving
Dynamic and Interpretable State Representation for Deep Reinforcement Learning in Automated Driving Open
Understanding the causal relationship between an autonomous vehicle's input state and its output action is important for safety mitigation and explainable automated driving. However, reinforcement learning approaches have the drawback of b…
View article: Source-Network-Load-Storage Coordinated Control System Based on Distribution IOT Cloud Platform and Virtual Power Plant
Source-Network-Load-Storage Coordinated Control System Based on Distribution IOT Cloud Platform and Virtual Power Plant Open
In view of the distribution network operation problems caused by many distributed generations integration to distribution network, and the increasingly serious peak valley imbalance in grid, this paper proposes a coordinated control system…
View article: An online evolving framework for advancing reinforcement-learning based automated vehicle control
An online evolving framework for advancing reinforcement-learning based automated vehicle control Open
In this paper, an online evolving framework is proposed to detect and revise a controller's imperfect decision-making in advance. The framework consists of three modules: the evolving Finite State Machine (e-FSM), action-reviser, and contr…
View article: An Online Evolving Framework for Advancing Reinforcement-Learning based Automated Vehicle Control
An Online Evolving Framework for Advancing Reinforcement-Learning based Automated Vehicle Control Open
In this paper, an online evolving framework is proposed to detect and revise a controller's imperfect decision-making in advance. The framework consists of three modules: the evolving Finite State Machine (e-FSM), action-reviser, and contr…
View article: Driving Intention Recognition and Lane Change Prediction on the Highway
Driving Intention Recognition and Lane Change Prediction on the Highway Open
This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the host-vehicle. The framework consists of a driving character…
View article: An Online Evolving Framework for Modeling the Safe Autonomous Vehicle Control System via Online Recognition of Latent Risks
An Online Evolving Framework for Modeling the Safe Autonomous Vehicle Control System via Online Recognition of Latent Risks Open
An online evolving framework is proposed to support modeling the safe Automated Vehicle (AV) control system by making the controller able to recognize unexpected situations and react appropriately by choosing a better action. Within the fr…