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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.

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Understanding the Role of Solvent Polarity in the Molecular Self-Assembly Process of Etoricoxib Solvates

TL;DR: In this paper, a comprehensive study of the solid and solution chemistry of etoricoxib (ETR) was conducted through the combined experimental identification and theoretical calculation, and six new solv...
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Relaxed Sum-of-Squares Based Stabilization Conditions for Polynomial Fuzzy-Model-Based Control Systems

TL;DR: This paper presents a relaxed stabilization condition for polynomial-fuzzy-model-based control system by using sum-of-squares (SOS) approach, and demonstrates the advantages of the proposed method over the existing convex SOS approach.
Proceedings ArticleDOI

State feedback control against sensor faults for Lipschitz nonlinear systems via new sliding mode observer techniques

TL;DR: This paper investigates the problem of simultaneous state and fault estimation and observer-based fault tolerant controller design for Lipschitz nonlinear systems with sensor failure and a new estimation technique is presented to deal with this design problem.
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Active fault-tolerant tracking control for near-space vehicle attitude dynamics with actuator faults

TL;DR: Based on both adaptive and fuzzy logic techniques, the fault-tolerant tracking problem for near-space vehicle (NSV) with actuator faults is investigated in this paper.
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Observer-based H∞ control on stochastic nonlinear systems with time-delay and actuator nonlinearity

TL;DR: An observer-based H ∞ controller is designed for a class of extended Markov jump systems subject to time-delay and actuator saturation nonlinearity and a continuous gain-scheduled approach is applied.