M
Mien Van
Researcher at Queen's University Belfast
Publications - 58
Citations - 2091
Mien Van is an academic researcher from Queen's University Belfast. The author has contributed to research in topics: Sliding mode control & Computer science. The author has an hindex of 20, co-authored 42 publications receiving 1286 citations. Previous affiliations of Mien Van include National University of Singapore & University of Ulsan.
Papers
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Finite Time Fault Tolerant Control for Robot Manipulators Using Time Delay Estimation and Continuous Nonsingular Fast Terminal Sliding Mode Control
TL;DR: The proposed AFTC scheme possess several advantages such as high precision, strong robustness, no singularity, less chattering, and fast finite-time convergence due to the combined NFTSMC and HOSM control, and requires no prior knowledge of the fault due to TDE-based fault estimation.
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An Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control for Robust Fault Tolerant Control of Robot Manipulators
TL;DR: A novel control methodology for tracking control of robot manipulators based on a novel adaptive backstepping nonsingular fast terminal sliding mode control (ABNFTSMC) is developed and compared with other state-of-the-art controllers.
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An Enhanced Robust Fault Tolerant Control Based on an Adaptive Fuzzy PID-Nonsingular Fast Terminal Sliding Mode Control for Uncertain Nonlinear Systems
TL;DR: This paper develops an enhanced robust fault tolerant control using a novel adaptive fuzzy proportional-integral-derivative-based nonsingular fast terminal sliding mode (AF-PID-NFTSM) control for a class of second-order uncertain nonlinear systems.
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Robust Fault-Tolerant Control for a Class of Second-Order Nonlinear Systems Using an Adaptive Third-Order Sliding Mode Control
TL;DR: A novel adaptive third- order SMC, which combines a novel third-order sliding mode surface, a continuous strategy and an adaptation law, is proposed and has an excellent capability to tackle several types of actuator faults with an enhancing on robustness, precision, chattering reduction, and time of convergence.
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Bearing Defect Classification Based on Individual Wavelet Local Fisher Discriminant Analysis with Particle Swarm Optimization
Mien Van,Hee-Jun Kang +1 more
TL;DR: A novel method is proposed by transforming the multiclass task into all possible binary classification tasks using a one-against-one (OAO) strategy and it is shown that the proposed method is well suited and effective for bearing defect classification.