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Chris Gerada
Researcher at University of Nottingham
Publications - 634
Citations - 10494
Chris Gerada is an academic researcher from University of Nottingham. The author has contributed to research in topics: Rotor (electric) & Stator. The author has an hindex of 37, co-authored 555 publications receiving 7161 citations. Previous affiliations of Chris Gerada include The University of Nottingham Ningbo China & Beihang University.
Papers
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Proceedings ArticleDOI
Prediction of inductance characteristics of PMSMs in saliency-based sensorless control
TL;DR: In this article, the authors proposed an approach to determine the machine parameters in Permanent Magnet Synchronous (PMSM) machines intended for sensorless motion operation using finite element perturbation analysis.
Proceedings ArticleDOI
Influence of Rotor Design on Electromagnetic Performance in Interior Permanent Magnet Machines
TL;DR: A detailed analysis of the rotor structure is carried out looking at understanding the effects of the geometrical parameters on key performance indexes, and an optimization procedure is implemented to satisfy the electromechanical performance of a case study traction motor.
Journal ArticleDOI
Torque Limiters for Aerospace Actuator Application
TL;DR: In this paper , different types of existing torque limiters are investigated for their suitability in aerospace EMA application and further integration within the electric motor, which can lead to improved reliability as well as higher power density resulting in next generation actuator electrical drives for MEA.
Decoupled Discrete Current Control for AC Drives at Low Sampling-to-Fundamental Frequency Ratios
Meiqi Wang,Giampaolo Buticchi,Jing Li,Chunyang Gu,David Gerada,Michele Degano,Lie Xu,Yongdong Li,He Zhang,Chris Gerada +9 more
TL;DR: In this article , an accurate model of current dynamics, which captures the computational delay and PWM characteristics in the discrete-time domain, is developed to eliminate cross-coupling effects in permanent magnet synchronous motor (PMSM) drive systems.
Proceedings ArticleDOI
Toward Obstacle Avoidance for Mobile Robots Using Deep Reinforcement Learning Algorithm
TL;DR: In this paper, a DDPG framework with separating experience is developed for mobile robot collision-free navigation, to replay the transitions of valuable and the failed experience discretely, and the environment state vector is designed including mobile robot and obstacles.