Intelligent control system for industrial robots based on multi-source data fusion

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yang Zhang
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Abstract

Abstract Industrialization has advanced quickly, bringing intelligent production and manufacturing into people’s daily lives, but it has also created a number of issues with the ability of intelligent control systems for industrial robots. As a result, a study has been conducted on the use of multi-source data fusion methods in the mechanical industry. First, the research analyzes and discusses the existing research at home and abroad. Then, a robot intelligent control system based on multi-source fusion method is proposed, which combines multi-source data fusion with principal component analysis to better fuse data of multiple control periods; In the process, the experimental results are dynamically evaluated, and the performance of the proposed method is compared with other fusion methods. The results of the study showed that the confidence values and recognition correctness of the intelligent control system under the proposed method were superior compared to the Yu, Murphy, and Deng methods. Applying the method to the comparison of real-time and historical data values, it is found that the predicted data under the proposed method fits better with the actual data values, and the fit can be as high as 0.9945. The dynamic evaluation analysis of single and multi-factor in the simulation stage demonstrates that the control ability in the training samples of 0–100 is often better than the actual results, and the best evaluation results may be obtained at the sample size of 50 per batch. The aforementioned findings demonstrated that the multi-data fusion method that was suggested had a high degree of viability and accuracy for the intelligent control system of industrial robots and could offer a fresh line of enquiry for the advancement and development of the mechanical industrialization field.
基于多源数据融合的工业机器人智能控制系统
摘要工业化发展迅速,将智能生产和制造带入人们的日常生活,但同时也给工业机器人智能控制系统的能力带来了一些问题。因此,对多源数据融合方法在机械工业中的应用进行了研究。首先,本研究对国内外已有的研究进行了分析和探讨。然后,提出了一种基于多源融合方法的机器人智能控制系统,将多源数据融合与主成分分析相结合,更好地融合了多个控制周期的数据;在此过程中,对实验结果进行了动态评价,并与其他融合方法进行了性能比较。研究结果表明,与Yu、Murphy和Deng方法相比,该方法下的智能控制系统的置信度值和识别正确性都有所提高。将该方法应用于实时数据值与历史数据值的比较,发现该方法下的预测数据与实际数据值拟合较好,拟合度可高达0.9945。仿真阶段单因素和多因素的动态评价分析表明,0-100个训练样本中的控制能力往往优于实际结果,每批50个样本量时可能获得最佳评价结果。上述研究结果表明,所提出的多数据融合方法对于工业机器人智能控制系统具有较高的可行性和准确性,可以为机械工业化领域的进步和发展提供新的思路。
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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