H
Hamid Reza Karimi
Researcher at Polytechnic University of Milan
Publications - 981
Citations - 31217
Hamid Reza Karimi is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Control theory & Nonlinear system. The author has an hindex of 83, co-authored 874 publications receiving 24361 citations. Previous affiliations of Hamid Reza Karimi include University of Wollongong & University of Bremen.
Papers
More filters
Journal ArticleDOI
Output-Feedback-Based $H_{\infty}$ Control for Vehicle Suspension Systems With Control Delay
TL;DR: This paper deals with the problem of output-feedback H∞ control for a class of active quarter-car suspension systems with control delay with Lyapunov theory and linear matrix inequality approach, and the existence of admissible controllers is formulated in terms of LMIs.
Journal ArticleDOI
New Delay-Dependent Exponential $H_{\infty}$ Synchronization for Uncertain Neural Networks With Mixed Time Delays
Hamid Reza Karimi,Huijun Gao +1 more
TL;DR: An exponential H infin synchronization method for a class of uncertain master and slave neural networks with mixed time delays, where the mixed delays comprise different neutral, discrete, and distributed time delays is established.
Journal ArticleDOI
Robust Observer Design for Unknown Inputs Takagi–Sugeno Models
TL;DR: This paper deals with the observer design for Takagi-Sugeno (T-S) fuzzy models subject to unknown inputs and disturbance affecting both states and outputs of the system.
Journal ArticleDOI
Dissipativity-Based Fuzzy Integral Sliding Mode Control of Continuous-Time T-S Fuzzy Systems
TL;DR: This paper is concerned with dissipativity-based fuzzy integral sliding mode control (FISMC) of continuous-time Takagi-Sugeno (T-S) fuzzy systems with matched/unmatched uncertainties and external disturbance, and an appropriate integral-type fuzzy switching surface is put forward.
Journal ArticleDOI
Data-driven design of robust fault detection system for wind turbines
TL;DR: In this paper, a robust data-driven fault detection approach is proposed with application to a wind turbine benchmark, where robust residual generators directly constructed from available process data are used to achieve the robustness of the residual signals related to the disturbances.