H
Hao Shen
Researcher at Anhui University of Technology
Publications - 296
Citations - 12245
Hao Shen is an academic researcher from Anhui University of Technology. The author has contributed to research in topics: Computer science & Control theory. The author has an hindex of 54, co-authored 225 publications receiving 8681 citations. Previous affiliations of Hao Shen include Nanjing University of Science and Technology & Yeungnam University.
Papers
More filters
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Finite-time l 2 - l ∞ tracking control for Markov jump repeated scalar nonlinear systems with partly usable model information
TL;DR: The objective of the study is to design a state-feedback controller such that the augmented closed-loop system is mean-square stochastically finite-time bounded, and a tracking performance level is achieved over a finite time interval.
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Finite-time H ∞ control for a class of Markovian jump delayed systems with input saturation
TL;DR: In this paper, an observer-based state feedback controller is designed to guarantee that the resulted closed-loop constrained system is mean-square locally asymptotically finite-time stabilizable.
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A unified method to energy-to-peak filter design for networked Markov switched singular systems over a finite-time interval
TL;DR: This work designs a unified filter, which covers mode-independent filter, asynchronous filter and mode-dependent filter, so that the estimation error system is finite-time bounded while meets a fixed energy-to-peak performance requirement in the presence of those stochastic phenomena.
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Extended dissipativity-based synchronization of uncertain chaotic neural networks with actuator failures
TL;DR: Using a parameter-dependent Lyapunov function and a novel extended dissipation inequality, a criterion is proposed to guarantee that the synchronization error system is extended ( X, Y, Z -dissipative for all admissible uncertainties and actuator failures.
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An Improved Result on Sampled-Data Synchronization of Markov Jump Delayed Neural Networks
TL;DR: A new two-sided looped-functional is introduced to improve the informativeness of the formed Lyapunov–Krasovskii functional, which takes into an account the information of the entire sampling period.