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1. Research on Power Matching and Energy Optimal Control of Active Load-Sensitive Electro-Hydrostatic Actuator

Author

Ligang Huang, Tian Yu, Zongxia Jiao, and Yanpeng Li

Subjects
Active load-sensitive, electro-hydrostatic actuator, fuzzy control, optimal efficiency, power matching, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Electro-hydrostatic actuator (EHA) is an important type of power-by-wire (PBW) which is a highly integrated closed volume control system with the advantages of high reliability, high efficiency and easy maintenance. However, under heavy load conditions, a large amount of heat the EHA motor produces affects the system’s working time and even the system life, which restricts the widespread application of EHA systems. In order to solve the problem of motor heating and the contradiction between high dynamics and high efficiency of the EHA system, a novel active load-sensitive principle and structure of EHA system is proposed based on the load-sensitive principle. The active load-sensitive EHA (ALS-EHA) is a dual control variable system consisting of a motor pump main loop and a load-sensitive loop. As for the latter, the system load pressure is introduced into a special designed and optimized pressure fellow valve (PFV). The valve outlet is connected to the plunger pump swash plate variable mechanism. The EHA pump displacement is actively adjusted by controlling the current input to PFV. The two-degree-of-freedom cooperative control for the output flow of the pump can be implemented by adjusting the motor speed and the pump displacement. An energy optimal robust control law based on fuzzy and disturbance compensation is presented for the control architecture. The simulation results show that the ALS-EHA system based on the control law proposed in this paper can effectively reduce the motor heat while ensuring the dynamic performance of the system.

Published
2021
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2. Mastering the Working Sequence in Human-Robot Collaborative Assembly Based on Reinforcement Learning

Author

Tian Yu, Jing Huang, and Qing Chang

Subjects
Human-Robot collaboration, real-time task planning, reinforcement learning, convolution neural network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

A long-standing goal of the Human-Robot Collaboration (HRC) in manufacturing systems is to increase the collaborative working efficiency. In line with the trend of Industry 4.0 to build up the smart manufacturing system, the collaborative robot in the HRC system deserves better designing to be more self-organized and to find the superhuman proficiency by self-learning. Inspired by the impressive machine learning algorithms developed by Google Deep Mind like Alphago Zero, in this paper, the human-robot collaborative assembly working process is formatted into a chessboard and the selection of moves in the chessboard is used to analogize the decision-making by both human and robot in the HRC assembly working process. To obtain the optimal policy of the working sequence to maximize the working efficiency, agents in the system are trained with a self-play algorithm based on reinforcement learning, without guidance or domain knowledge beyond game rules. A convolution neural network (CNN) is also trained to predict the distribution of the priority of move selections and whether a working sequence is the one resulting in the maximum of the HRC efficiency. A height-adjustable standing desk assembly is used to demonstrate the proposed HRC assembly algorithm and its efficiency in real-time task planning.

Published
2020
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3. Research on the Relevancy of Scientific Literature Based on the Citation-Mention Frequency

Author

Guang Yu, Weibin Wang, Cholmyong Pak, and Tian Yu

Subjects
Citation-mention frequency, co-citation analysis, relevancy, similarity, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

All papers cite references, but not all citations are equal. This is because references have different citation-mention frequencies. Some references are mentioned only once, while some are mentioned several times. Papers are cited by others when they are relevant to the citing paper. The fact that a reference has a high citation-mention frequency may mean that its content is more closely related to the citing paper. From this point of view, we examined the relevancy of a cited paper to a citing paper on the basis of citation-mention frequency. Two aspects of relevancy are considered: citation linkage and content. We construct a highly mentioned class of references and a rarely mentioned class of references. We introduce the concepts of “reference-similarity” and “content-similarity.” First, we count the number of co-cited references and calculate the reference-similarity. Second, we extract the abstracts of papers and calculate the content-similarity using the bag-of-words model. The results show that references from the highly mentioned class are more relevant to the citing papers than those from the rarely mentioned class.

Published
2019
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