Kaiyuan Chen;Runqi Chai;Runda Zhang;Zhida Xing;Yuanqing Xia;Guoping Liu
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引用次数: 0
Abstract
Dear Editor, This letter presents a novel data-driven trajectory planning and control scheme for the unmanned ground vehicles (UGVs). A recent work [1] has demonstrated the effectiveness of approximating the optimal state feedback for a nonlinear unmanned system via deep neural network (DNN). To further the previous research, we construct a long-short term memory recurrent deep neural network (LSTMRDNN) to improve the performance of the data-driven approximation instrument. The proposed strategy is evaluated and verified through theoretical analyses and experiments.
期刊介绍：
The IEEE/CAA Journal of Automatica Sinica is a reputable journal that publishes high-quality papers in English on original theoretical/experimental research and development in the field of automation. The journal covers a wide range of topics including automatic control, artificial intelligence and intelligent control, systems theory and engineering, pattern recognition and intelligent systems, automation engineering and applications, information processing and information systems, network-based automation, robotics, sensing and measurement, and navigation, guidance, and control.
Additionally, the journal is abstracted/indexed in several prominent databases including SCIE (Science Citation Index Expanded), EI (Engineering Index), Inspec, Scopus, SCImago, DBLP, CNKI (China National Knowledge Infrastructure), CSCD (Chinese Science Citation Database), and IEEE Xplore.
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