Research on Adaptive Iterative Learning Control of Air Pressure in Railway Tunnel With IOTs Data

Zhang, Yinjun; Jian-hua, SU; Chen, Mengji

doi:10.1109/access.2019.2960638

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…From the iterative solution form of Equation (26), it can be seen that it belongs to a typical open-loop feedforward control form. In order to ensure the control accuracy, Equation (26) is often improved to the following closed-loop learning law form:…”

Section: Air Pressure Compensation Of Elevator Car Based On Pd-ilcmentioning

confidence: 99%

“…Mizuno et al [25] developed an air pressure control system to control air pressure inside elevator cars to a steady rate of change. Based on the Internet of Things (IoTs) big data, Zhang et al [26] proposed an adaptive iterative learning control algorithm to restrain the pressure fluctuation in the train according to the quasi-periodicity and repeatability of mass data in the process of train running in tunnels. Wen et al [27] proposed a kind of digital pressure control project to solve the problem of pressure fluctuation inside the high-speed train carriage.…”

Section: Introductionmentioning

confidence: 99%

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High-Speed Elevator Car Air Pressure Compensation Method Based on Coupling Analysis of Internal and External Flow Fields

et al. 2021

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As the demand for high-speed elevators grows, the requirements of elevator performance have also developed. The high speed will produce strong airflow disturbances and drastic pressure changes, which is prone to cause passenger discomfort. In this paper, an elevator car air pressure compensation method based on coupling analysis of internal and external flow fields (IE-FF) is proposed. It helps to adaptively track the ideal air pressure curve (IAPC) inside the car and controls the air pressure fluctuation to improve the ride comfort of the elevator. To obtain the air pressure transient value in the elevator car, an IE-FF modeling method is proposed. Based on the IE-FF model, the air pressure compensation system is developed. To realize the air pressure compensation inside the car, an adaptive iterative learning control (A-ILC) algorithm is proposed, to eliminate the passengers’ ear pressing due to the severe air pressure fluctuation. To verify the proposed method, the KLK2 (Canny Elevator Co., Ltd., 2015, Suzhou, China) high-speed elevator is applied. The numerical experiment results show that the proposed method has higher tracking accuracy and convergence speed compared to the classical Proportion Integral Differential (PID) algorithm and the Proportion Integral-iterative learning control (PD-ILC) algorithm.

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Section: Air Pressure Compensation Of Elevator Car Based On Pd-ilcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Speed Elevator Car Air Pressure Compensation Method Based on Coupling Analysis of Internal and External Flow Fields

et al. 2021

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Adaptive Chebyshev Neural Network Control for Ventilator Model under the Complex Mine Environment

Liu

Zhang

et al. 2020

Complexity

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Ventilator is important equipment for mines as it safeguards the lives under the shaft and ensures other equipment’s proper functioning by providing fresh air. Therefore, how to effectively control the ventilator system becomes more significant. In order to acquire the commonly used model and control strategy for ventilator systems, a new universal ventilator model is established based on the blast capacity differential pressure in the ventilating duct and the ventilator motor model. Then, an adaptive Chebyshev neural network (ACNN) controller is proposed to effectively control the ventilator system where the unknown load torque and the unknown disturbance caused by the complex environment under the shaft are approximated by the Chebyshev neural network (CNN). Afterwards, an appropriate Lyapunov function candidate is designed to guarantee the stability of the proposed controller and the closed-loop ventilator system. Finally, the ACNN controller has been demonstrated to be effective in terms of validity and precision for the new proposed ventilator model through the simulations.

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Research on Adaptive Iterative Learning Control of Air Pressure in Railway Tunnel With IOTs Data

Cited by 2 publications

References 22 publications

High-Speed Elevator Car Air Pressure Compensation Method Based on Coupling Analysis of Internal and External Flow Fields

High-Speed Elevator Car Air Pressure Compensation Method Based on Coupling Analysis of Internal and External Flow Fields

Adaptive Chebyshev Neural Network Control for Ventilator Model under the Complex Mine Environment

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