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Your search keyword '"Cheng, Shuang"' showing total 146 results
Start Over Author "Cheng, Shuang" Publisher elsevier b.v.
146 results on '"Cheng, Shuang"'

45. Fixed-time time-varying output formation–containment control of heterogeneous general multi-agent systems.

Author

Duan, Jie, Duan, Guichao, Cheng, Shuang, Cao, Shengxian, and Wang, Gong

Subjects
MULTIAGENT systems, REINFORCEMENT learning, DESIGN
Abstract

This work investigates fixed-time time-varying output formation–containment control of heterogeneous general multi-agent systems, which comprises one virtual leader, multiple leaders, and followers. First, fixed-time nonlinear control law is constructed, such that leaders can achieve time-varying output formation in a finite time. Meanwhile, leaders can track the virtual leader's output trajectory. Next, two complete distributed adaptive fixed-time observers are designed aiming to recover a convex hull. This is key to control issue in this paper. Further, nonlinear control law is constructed for followers, such that followers can move into the convex hull formed by multiple leaders. Finally, two examples are provided to verify the feasibility of the theoretical results. • Time-varying output formation–containment control of heterogeneous MASs is solved. • Novel adaptive fixed-time observers is proposed. • Fixed-time formation control law is proposed. • Fixed-time formation–containment control law is proposed. [ABSTRACT FROM AUTHOR]

Published
2023
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48. Bringing employee learning to AI stress research: A moderated mediation model.

Author

Zhou, Qiwei, Chen, Keyu, and Cheng, Shuang

Subjects
TECHNOLOGICAL innovations, ARTIFICIAL intelligence, DIGITAL technology, SUSTAINABLE development, ECONOMIC activity
Abstract

While a substantial portion of the literature characterizes artificial intelligence (AI) stress as a hindrance, our focus diverges by probing employee learning as an active response to this challenge. We highlight the role of employee knowledge and skills development amidst an enterprise's digital transformation. Drawing on the active learning perspective of the Job Demand-Control model, we investigate why and when AI stress promotes employee learning and subsequent adaptive coping behaviors. We propose that AI stress can create opportunities and resources for employee learning, leading to improved job performance and supportive behavior for digital transformation. Additionally, we examine how employee trust in AI moderates these relationships, finding that higher levels of AI trust are associated with greater use of active learning strategies when faced with AI stress. Our findings, based on a two-wave survey of 224 employees from a motor-vehicle testing company in China, are further supported by post-hoc interview data collected from 32 employees of the same company. Overall, our study contributes to the understanding of AI adoption, digital transformation, and stress learning. • AI stress drives employees to learn. • Employees' learning changes their cognition of digital transformation. • Employees' learning changes their work capacity. • Changes in employees' cognition and capacity lead to outcomes. • Employees with AI trust engage in more learning when faced with AI stress. [ABSTRACT FROM AUTHOR]

Published
2024
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