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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State estimation for discrete-time Markov jump linear systems with time-correlated and mode-dependent measurement noise
Wei Liu,Peng Shi,Jeng-Shyang Pan +2 more
TL;DR: A novel “distributed measurement differencing method” is applied to the problem of state estimation under consideration so that two algorithms are obtained, one of which is optimal in the sense of minimum mean-square error and the second is suboptimal.
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On stochastic stabilization of discrete-time Markovian jump systems with delay in state
Peng Shi,El-Kébir Boukas,Yan Shi +2 more
TL;DR: In this paper, the stochastic stabilization problem for a class of linear discrete time-delay systems with Markovian jump parameters is investigated, and sufficient conditions are proposed to solve the above problems, which are in terms of a set of solutions of coupled matrix inequalities.
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Dissipativity-Based Consensus for Fuzzy Multiagent Systems Under Switching Directed Topologies
Peng Shi,Jiafeng Yu +1 more
TL;DR: A novel polynomial fuzzy modeling approach is proposed to describe the error dynamic system which is formulated by a virtual leader and follower agents and consensus control protocol is designed to guarantee that the MASs under switching topologies can reach an agreement.
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Self-recovering extended Kalman filtering algorithm based on model-based diagnosis and resetting using an assisting FIR filter
TL;DR: In the SREKF algorithm, the EKF's failure or abnormal operation is automatically diagnosed using an intelligence algorithm for model-based diagnosis, and an assisting filter, a nonlinear finite impulse response (FIR) filter, is operated.
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L∞ performance of single and interconnected neural networks with time-varying delay
TL;DR: A condition is proposed for the L∞ performance of single neural networks with time-varying delay and persistent bounded input based on the Wirtinger-type inequality together with the reciprocal convex approach.