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.
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Observer-Based Fault Estimation and Accomodation for Dynamic Systems
Ke Zhang,Bin Jiang,Peng Shi +2 more
TL;DR: In this paper, the authors propose an FA for T-S Fuzzy Models Based Nonlinear Systems (FFMBSN) with time delay and loss of actuator effectiveness.
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Finite-time stability and stabilisation for a class of nonlinear systems with time-varying delay
TL;DR: A delay-dependent FTS criterion is proposed for open-loop fuzzy system by introducing some free fuzzy weighting matrices, which are less conservative than other existing ones and the parallel distributed compensation controller is designed to ensure FTS of the time-delay fuzzy system.
Fuzzy Output Feedback Control Design for Nonlinear Systems: An LMI Approach
Sing Kiong Nguang,Peng Shi +1 more
TL;DR: A technique is developed based on a well-known Lyapunov functional approach for designing an fuzzy output feedback control law which guarantees the gain from an exogenous input to a regulated output is less or equal to a prescribed value.
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Constrained Infinite-Horizon Model Predictive Control for Fuzzy-Discrete-Time Systems
TL;DR: The problem of constrained infinite-horizon model-predictive control for fuzzy-discrete systems is considered, and new sufficient conditions are proposed in terms of linear-matrix inequalities to design both parallel-distributed compensation and nonparallel-distribution compensation state-feedback controllers.
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Exponential Stability of Markovian Jumping Systems via Adaptive Sliding Mode Control
TL;DR: A new dynamic model, which involves parameters uncertainties, nonlinearities, and Lévy noises, is proposed, and an adaptive sliding mode controller is built to study the stability of such a complex model.