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4 results on '"Lingjie Xu"'

1. Feedback-Added Pseudoinverse-Type Balanced Minimization Scheme for Kinematic Control of Redundant Robot Manipulators

Author

Zhenhuan Wang, Boyang Wang, Lingjie Xu, and Qinyu Xie

Subjects
Balanced minimization, pseudoinverse, feedback, kinematic control, redundant robot manipulators, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

In this paper, a new feedback-added pseudoinverse-type balanced minimization (FPBM) scheme is proposed and studied for the kinematic control of redundant robot manipulators. Such a scheme is designed by combining the minimum acceleration norm (MAN) solution and the weighted minimum velocity norm (WMVN) solution and by introducing the feedback. With the MAN and WMVN combination, the proposed FPBM scheme can not only remedy the phenomena of high velocity and acceleration but also generate a near-zero velocity after task execution. With the feedback introduction, the proposed FPBM scheme can guarantee a nondivergent end-effector tracking error. Based on a four-link robot manipulator, the simulation results are presented to show the effectiveness of the proposed FPBM scheme.

Published
2019
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4. Research on Coordinated Charging Strategy of Electric Vehicles Based on PSO Algorithm

Author

Lingjie Xu, Yana Xue, and Ruifeng Shi

Subjects
business.industry, Computer science, 020209 energy, Fossil fuel, 0202 electrical engineering, electronic engineering, information engineering, Particle swarm optimization, 02 engineering and technology, Power grid, business, Energy (signal processing), Automotive engineering, Renewable energy
Abstract

Electric vehicles (EVs) have unparalleled advantages compared to traditional cars in energy saving and alleviating the dependence on fossil fuel. However, thousands of EVs charging at the same time will lead to bad impacts on the power grid, which may further increase the peak-valley difference and load fluctuation. This paper aims to find an optimal coordinated charging strategy of EVs with consideration of renewable energy involvement. Based on Particle Swarm Optimization (PSO) algorithm, a coordinated charging control method is developed. EV users' satisfaction and the proportion of abandoned renewable energy are chosen to be part of the objective function. Monte-Carlo method is employed to simulate the charging behaviors of EV users. Simulation results under uncoordinated and coordinated charging scenarios are compared and analyzed, which shows that the proposed method can effectively smoothen the system load curve.

Published
2018
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