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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Land-use types and slope topography affect the soil labile carbon fractions in the Loess hilly-gully area of Shaanxi, China
TL;DR: The authors evaluated the effects of vegetation restoration on soil organic carbon and its fractions, by exploring the SOC, particulate organic carbon (POC), mineralizable organic C (MOC), and ligh...
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Adaptive synchronization of delayed Markovian switching neural networks with Lévy noise
TL;DR: A sufficient condition in terms of LMI is established to ensure the synchronization of the two systems, and the adaptive update law is determined as well.
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Tracking control of uncertain Euler–Lagrange systems with finite‐time convergence
TL;DR: In this paper, a unified solution to the tracking control problem of Euler-Lagrange systems with finite-time convergence is presented, where a reconstruction module is designed to estimate the overall of unmodeled dynamics, disturbance, actuator misalignment, and multiple actuator faults.
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Adaptive variable structure fuzzy neural identification and control for a class of MIMO nonlinear system
TL;DR: Simulation results demonstrate that the proposed AVS-FNN has a self-organizing ability, which can determine the structure and parameters of the FNN automatically, and has been applied successfully in the 3 DOF helicopter systems, showing the effectiveness and potential of the proposed design techniques.
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Finite Distribution Estimation-Based Dynamic Window Approach to Reliable Obstacle Avoidance of Mobile Robot
TL;DR: A new version of the DWA is proposed, called the finite distribution estimation-based dynamic window approach (FDEDWA), which is an algorithm that avoids dynamic obstacles through estimating the overall distribution of obstacles.