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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Robust Constrained Model Predictive Control Based on Parameter-Dependent Lyapunov Functions
TL;DR: In this paper, robust constrained model predictive control (MPC) of systems with polytopic uncertainties is considered, and sufficient conditions for the existence of parameter-dependent Lyapunov functions are proposed in terms of linear matrix inequalities (LMIs).
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Control of Discrete-Time Stochastic Systems With Packet Loss by Event-Triggered Approach
TL;DR: Two different mathematical analysis methods are proposed to model the packet loss when an event-triggered scheme is subject to packet loss and the mean-square exponential stability of resulting augmented system is guaranteed.
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Distributed cooperative adaptive tracking control for heterogeneous systems with hybrid nonlinear dynamics
TL;DR: The cooperative leader-following tracking for a group of heterogeneous mechanical systems with nonlinear hybrid order dynamics is studied and the convergence and boundedness of the synchronization error is proved by the Lyapunov theory.
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Fault estimation and fault-tolerant control for networked systems based on an adaptive memory-based event-triggered mechanism
TL;DR: A novel adaptive memory-based mechanism is proposed by introducing the latest piece of historical output information that matches with a corresponding weight such that the closer information is, the more contribution to the releasing event.
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A mode-dependent stability criterion for delayed discrete-time stochastic neural networks with Markovian jumping parameters
Yan Ou,Peng Shi,Hongyang Liu +2 more
TL;DR: By a novel Lyapunov-Krasovskii functional combining with the delay partitioning technique and the free-weighting matrix method in terms of linear matrix inequalities (LMIs), the new stability criterion proves to be less conservative.