15 citations · 73 across the 24 of their papers we have counts for
7 papers · 1 filter
Distributed Koopman Learning using Partial Trajectories for Control
Wenjian Hao, Zehui Lu, Devesh Upadhyay +1
This paper proposes a distributed data-driven framework for dynamics learning, termed distributed deep Koopman learning using partial trajectories (DDKL-PT). In this framework, eac…
Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems
Zhiyi Chen, Harshal Maske, Devesh Upadhyay +3
This paper presents a modeling-control synthesis to address the quality control challenges in multistage manufacturing systems (MMSs). A new feedforward control scheme is developed…
Deep Koopman Learning using Noisy Data
Wenjian Hao, Devesh Upadhyay, Shaoshuai Mou
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 nois…
Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller
Mingxin Yu, Chenning Yu, M-Mahdi Naddaf-Sh +3
Sampling-based motion planning methods for manipulators in crowded environments often suffer from expensive collision checking and high sampling complexity, which make them difficu…
C3D: Cascade Control with Change Point Detection and Deep Koopman Learning for Autonomous Surface Vehicles
Jianwen Li, Hyunsang Park, Wenjian Hao +10
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.…
Inherent Diverse Redundant Safety Mechanisms for AI-based Software Elements in Automotive Applications
Mandar Pitale, Alireza Abbaspour, Devesh Upadhyay
This paper explores the role and challenges of Artificial Intelligence (AI) algorithms, specifically AI-based software elements, in autonomous driving systems. These AI systems are…
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
Liangqi Yuan, Dong-Jun Han, Su Wang +2
Multimodal federated learning (MFL) aims to enrich model training in FL settings where clients are collecting measurements across multiple modalities. However, key challenges to MF…