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Joaquin Carrasco

Researcher at University of Manchester

Publications -  101
Citations -  1588

Joaquin Carrasco is an academic researcher from University of Manchester. The author has contributed to research in topics: Nonlinear system & Monotone polygon. The author has an hindex of 19, co-authored 93 publications receiving 1120 citations. Previous affiliations of Joaquin Carrasco include University of Murcia.

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Journal ArticleDOI

Comment on “Absolute stability analysis for negative-imaginary systems” [Automatica 67 (2016) 107–113] ☆

TL;DR: This note provides the connection between the paper “Absolute stability analysis for negative-imaginary systems” and classical results in absolute stability and Strictly negative- Imaginary systems satisfy the Aizerman conjecture.
Proceedings ArticleDOI

3D Vision-guided Pick-and-Place Using Kuka LBR iiwa Robot

TL;DR: In this paper, the authors present a control system for vision-guided pick-and-place tasks using a robot arm equipped with a 3D camera, which allows the robot to be able to pick and place an object with limited times of registering a new object and the developed software can be applied for new object scenario quickly.
Journal ArticleDOI

Adaptive Impedance-Conditioned Phase-Locked Loop for the VSC Converter Connected to Weak Grid

TL;DR: In this paper, an adaptive version of the impedance-conditioned phase-locked loop (IC-PLL) is proposed, which is used to address the issue of synchronisation with a weak AC grid.
Book ChapterDOI

A Hybrid Underwater Acoustic and RF Localisation System for Enclosed Environments Using Sensor Fusion

TL;DR: The proposed system is able to improve the position estimation of a group of Autonomous Underwater Vehicles (AUVs) or Remote Operated Vehicles (ROVs) for exploring enclosed environments.
Journal ArticleDOI

Distributed Neural Networks Training for Robotic Manipulation With Consensus Algorithm.

TL;DR: An algorithm that combines actor-critic-based off-policy method with consensus-based distributed training to deal with multiagent deep reinforcement learning problems is proposed and verified that all agents will converge to the same optimal model as the training time goes to infinity.