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Backstepping Based Trajectory Tracking Control of a TBM Steel Arch Splicing Manipulator
Yuxi Chen,Guofang Gong,Xinghai Zhou,Yakun Zhang,Weiqiang Wu 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.2
At present, the splicing of steel arches for open-type TBM suffers from the problems of labor-intensive, time-consuming, low efficiency and greater potencial risk to workers. Rock-fall and collapse caused by untimely support is still one of the main construction accidents. In this paper, a novel steel arch splicing manipulator is developed for unmanned and automated steel arch splicing, and a backstepping method based cascade control strategy is proposed to improve the trajectory tracking control performance. Firstly, the inner-loop controller is designed to compensate the flow coupling between each joint-driven hydraulic cylinder based on dynamic analysis and feedback linearization. Secondly, the adaptive robust controller is adopted for outer-loop controller design to deal with parametric uncertainties and external disturbances. Finally, the system stability is proved by Lyapunov function, then comparative experiments are conducted to verify the effectiveness and superiority of the proposed control scheme. It can be concluded that the proposed controller has a better trajectory tracking control performance, while the control input is much smoother than that of traditional PID controller.
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Deng Jinxiang,Li Mengjie,Tian Yakun,Wu Lingling,Hu Lin,Zhang Zhijun,Zheng Huaimiao 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.12
The present microbial reinforcement of rock and soil exhibits limitations, such as uneven reinforcement effectiveness and low calcium carbonate generation rate, resulting in limited solidification strength. This study introduces electroosmosis as a standard microbial grouting reinforcement technique and investigates its solidification effects on microbial-reinforced uranium tailings. The most effective electroosmosis effect on uranium tailings occurs under a potential gradient of 1.25 V/cm. The findings indicate that a weak electric field can effectively promote microbial growth and biological activity and accelerate bacterial metabolism. The largest calcium carbonate production occurred under the gradient of 0.5 V/cm, featuring a good crystal combination and the best cementation effect. Staged electroosmosis and electrode conversion efficiently drive the migration of anions and cations. Under electroosmosis, the cohesion of uranium tailings reinforced by microorganisms increased by 37.3% and 64.8% compared to those reinforced by common microorganisms and undisturbed uranium tailings, respectively. The internal friction angle is also improved, significantly enhancing the uniformity of reinforcement and a denser and stronger microscopic structure. This research demonstrates that MICP technology enhances the solidification effects and uniformity of uranium tailings, providing a novel approach to maintaining the safety and stability of uranium tailings dams
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Online health estimation strategy with transfer learning for operating lithium‑ion batteries
Fang Yao,Defang Meng,Youxi Wu,Yakun Wan,Fei Ding 전력전자학회 2023 JOURNAL OF POWER ELECTRONICS Vol.23 No.6
Complex power supply operation conditions complicate the degradation process of lithium batteries, which makes the charge–discharge cycle incomplete and the maximum available capacity not easily accessible. Besides, data-driven methods suffer from limited adaptation and possible overfi tting. This paper proposes an online health estimation strategy with transfer learning for estimating the state of health (SOH) of batteries under varying charge–discharge depths and current rates. It aims to alleviate the diffi culty in estimating SOH for operating batteries, and broaden the application range of the training model. The core of this strategy is a two-domain transfer CNN-LSTM model that estimates targets by transferring the battery degradation trends of multiple constant conditions. First, health indicators (HIs) with relatively high correlations and wide application ranges are extracted from the voltage and current data of the daily charge process. Then HI-based source domain selection criteria are designed. Since the battery experiences full and incomplete-discharged cases leading to various aging rates, a two-domain transfer CNN-LSTM model is designed. Each subnet includes a CNN and an LSTM to accomplish feature adaptation and time series forecasting. The weights of the sub-nets are updated online to track the drift of the time series covariates. Finally, the proposed strategy is verifi ed on target batteries with varying cut-off voltages and currents, which demonstrates notable accuracy and reliability.
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