P
Peng Shi
Researcher at University of Adelaide
Publications - 1601
Citations - 80441
Peng Shi is an academic researcher from University of Adelaide. The author has contributed to research in topics: Control theory & Nonlinear system. The author has an hindex of 137, co-authored 1371 publications receiving 65195 citations. Previous affiliations of Peng Shi include Harbin Engineering University & Harbin University of Science and Technology.
Papers
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Journal ArticleDOI
Distributed finite-time containment control for double-integrator multiagent systems.
Xiangyu Wang,Shihua Li,Peng Shi +2 more
TL;DR: In this paper, the distributed finite-time containment control problem for double-integrator multiagent systems with multiple leaders and external disturbances is discussed and algorithms designed to guarantee that the states of the followers converge to the dynamic convex hull spanned by those of the leaders in finite time are proposed.
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Adaptive Output-Feedback Fuzzy Tracking Control for a Class of Nonlinear Systems
TL;DR: It is shown that the proposed fuzzy adaptive output controller can guarantee that all the signals remain bounded and that the tracking error converges to a small neighborhood of the origin.
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Adaptive Neural Fault-Tolerant Control of a 3-DOF Model Helicopter System
Mou Chen,Peng Shi,Cheng-Chew Lim +2 more
TL;DR: A disturbance observer-based adaptive neural fault-tolerant control scheme is developed to track the desired system output in the presence of system uncertainty, external disturbance, and actuator faults for the three degrees of freedom model helicopter.
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Network-Based Event-Triggered Control for Singular Systems With Quantizations
TL;DR: A discrete event-triggered H∞ control for a networked singular system with both state and input subject to quantizations is presented and two new sector bound conditions of quantizers are proposed to provide a more intuitive stability analysis and controller design.
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Control of Nonlinear Networked Systems With Packet Dropouts: Interval Type-2 Fuzzy Model-Based Approach
TL;DR: A novel fuzzy state-feedback controller is designed to guarantee the resulting closed-loop system to be stochastically stable with an optimal performance and to make the controller design more flexible, the designed controller does not need to share membership functions and amount of fuzzy rules with the model.