Explanipedia

Distributed Koopman Learning using Partial Trajectories for Control Open

Wenjian Hao, Zehui Lu, Devesh Upadhyay, Shaoshuai Mou · 2024

This paper proposes a distributed data-driven framework for dynamics learning, termed distributed deep Koopman learning using partial trajectories (DDKL-PT). In this framework, each agent in a multi-agent system is assigned a partial traje…

Distributed Koopman Learning with Incomplete Measurements Open

Wenjian Hao, Lili Wang, Ayush Rai, Shaoshuai Mou · 2024

Koopman operator theory has emerged as a powerful tool for system identification, particularly for approximating nonlinear time-invariant systems (NTIS). This paper considers a network of agents with limited observation capabilities that c…

Deep Koopman Learning using Noisy Data Open

Wenjian Hao, Devesh Upadhyay, Shaoshuai Mou · 2024

This paper proposes a data-driven framework to learn a finite-dimensional approximation of a Koopman operator for approximating the state evolution of a dynamical system under noisy observations. To this end, our proposed solution has two …

C3D: Cascade Control with Change Point Detection and Deep Koopman Learning for Autonomous Surface Vehicles Open

Jianwen Li, Hyunsang Park, Wenjian Hao, Xin Lei, Jalil Chavez‐Galaviz , et al. · 2024

In this paper, we discuss the development and deployment of a robust autonomous system capable of performing various tasks in the maritime domain under unknown dynamic conditions. We investigate a data-driven approach based on modular desi…

Deep Koopman learning of nonlinear time-varying systems Open

Wenjian Hao, Bowen Huang, Wei Pan, Di Wu, Shaoshuai Mou · 2023

Prognostic value of inflammatory markers for in-hospital mortality in intensive care patients with acute ischemic stroke: a retrospective observational study based on MIMIC-IV Open

Xuyang Hu, Jiaru Liang, Wenjian Hao, Jiaqi Zhou, Yuling Gao , et al. · 2023

Background Acute ischemic stroke (AIS) is a primary cause of death and disability worldwide. Four markers that can be readily determined from peripheral blood, namely, the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte …

Optimal Control of Nonlinear Systems with Unknown Dynamics Open

Wenjian Hao, Paulo Heredia, Bowen Huang, Zehui Lu, Zihao Liang , et al. · 2023

This paper presents a data-driven method to find a closed-loop optimal controller, which minimizes a specified infinite-horizon cost function for systems with unknown dynamics. Suppose the closed-loop optimal controller can be parameterize…

Adaptive Policy Learning to Additional Tasks Open

Wenjian Hao, Zehui Lu, Zihao Liang, Tian-Yu Zhou, Shaoshuai Mou · 2023

This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task. A method named Adaptive Policy Gradient (APG) is proposed in this paper, which combines Bellman's…

A Data-Driven Approach for Inverse Optimal Control Open

Zihao Liang, Wenjian Hao, Shaoshuai Mou · 2023

This paper proposes a data-driven, iterative approach for inverse optimal control (IOC), which aims to learn the objective function of a nonlinear optimal control system given its states and inputs. The approach solves the IOC problem in a…

Transcranial direct current stimulation for the treatment of post-stroke depression: A systematic review Open

Wenjian Hao, Yong Liu, Yuling Gao, Xiaoyang Gong, Yi Ning · 2023

Background Post-stroke depression (PSD) is not only a frequent neuropsychiatric manifestation secondary to stroke but is also associated with disability, poor rehabilitation outcomes, sleep disorders, cognitive impairment, and increased mo…

Deep Koopman Learning of Nonlinear Time-Varying Systems Open

Wenjian Hao, Bowen Huang, Wei Pan, Di Wu, Shaoshuai Mou · 2022

This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the Koopman operator and deep neural networks. Analysis of the …

Deep Learning of Koopman Representation for Control Open

Yiqiang Han, Wenjian Hao, Umesh Vaidya · 2020

We develop a data-driven, model-free approach for the optimal control of the dynamical system. The proposed approach relies on the Deep Neural Network (DNN) based learning of Koopman operator for the purpose of control. In particular, DNN …

Cell A* for Navigation of Unmanned Aerial Vehicles in Partially-known Environments Open

Wenjian Hao, Rongyao Wang, Alexander Krolicki, Yiqiang Han · 2020

Proper path planning is the first step of robust and efficient autonomous navigation for mobile robots. Meanwhile, it is still challenging for robots to work in a complex environment without complete prior information. This paper presents …

Data Driven Control with Learned Dynamics: Model-Based versus Model-Free Approach Open

Wenjian Hao, Yiqiang Han · 2020

This paper compares two different types of data-driven control methods, representing model-based and model-free approaches. One is a recently proposed method - Deep Koopman Representation for Control (DKRC), which utilizes a deep neural ne…

Data-Driven Control with Learned Dynamics Open

Wenjian Hao · 2020

This research focuses on studying data-driven control with dynamics that are actively learned from machine learning algorithms. With system dynamics being identified using neural networks either explicitly or implicitly, we can apply contr…

Wenjian Hao YOU? Author Swipe