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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Observer-Based Event-Triggered Approach for Stochastic Networked Control Systems Under Denial of Service Attacks
TL;DR: This article investigates the stability analysis and controller synthesis problems for a class of stochastic networked control systems under aperiodic denial-of-service (DoS) jamming attacks and proposes a new adaptive event-triggered mechanism on the basis of the observer to eliminate the adverse effects of DoS attacks.
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Reachable Set Estimation for Markovian Jump Neural Networks With Time-Varying Delays
TL;DR: The reachable set estimation problem is investigated for Markovian jump neural networks (NNs) with time-varying delays and bounded peak disturbances to find a set as small as possible which bounds all the state trajectories of the NNs under zero initial conditions.
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Fuzzy control of nonlinear electromagnetic suspension systems
TL;DR: In this paper, the authors considered the electromagnetic suspension system as repulsive system or attractive system which is based on the source of electromagnetic levitation forces, and proposed various controller design schemes to manipulate electromagnetic suspension systems, see for example, [38, 84, 103, 182, 183, 191, 204].
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Observer-based integrated robust fault estimation and accommodation design for discrete-time systems
Ke Zhang,Bin Jiang,Peng Shi +2 more
TL;DR: A multiobjective augmented fault estimation observer containing an α-exponential stability performance index and an H ∞ performance index is proposed not only to guarantee the convergence speed of fault estimation but also to restrict the influence of uncertainties with respect to the fault estimation error as much as possible.
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Fusion Kalman/UFIR Filter for State Estimation With Uncertain Parameters and Noise Statistics
TL;DR: This paper fuse the Kalman filter that is optimal but not robust with the unbiased finite-impulse response (UFIR) filter which is more robust than KF but not optimal, to provide the best fusion effect.