Iterative learning control for high‐speed trains with velocity and displacement constraints

2022 ◽  
Author(s):  
Deqing Huang ◽  
Tengfei Huang ◽  
Chunrong Chen ◽  
Na Qin ◽  
Xu Jin ◽  
...  
Keyword(s):  
Iterative Learning Control ◽  
High Speed ◽  
Learning Control ◽  
Iterative Learning ◽  
High Speed Trains ◽  
Displacement Constraints

Modelling and iterative learning control of internal pressure for high-speed trains under excitation of varying-amplitude tunnel pressure waves

2021 ◽  
pp. 095440972110460
Author(s):  
Zhiying He ◽  
Chunjun Chen ◽  
Dongwei Wang ◽  
Jia Hu ◽  
Lu Yang
Keyword(s):  
Iterative Learning Control ◽  
High Speed ◽  
Control Algorithm ◽  
Learning Control ◽  
Iterative Learning ◽  
Air Pressure ◽  
Pressure Waves ◽  
High Speed Trains ◽  
The Mathematical Model ◽  
Fixed Period

Traditional control algorithm of shutting down the air ducts for a fixed period is not applicable to take both the riding comfort and the air quality inside high-speed train carriages into account in long tunnels. Inspired by the morphological similarity of the tunnel pressure waves generated by the same train passes through the same tunnel, an upgraded iterative learning control algorithm for suppressing the air pressure variation excited by the quasi-periodic varying-amplitude tunnel pressure wave is developed. Firstly, the mathematical model of the control system is established, in which the air ducts, gaps and random interferences are considered. Then, the methodology of determining the goal in each iteration is formed, and the implementation of the iterative learning control algorithm is discussed. Finally, simulations of the algorithm are carried out. The simulation results show that in the upgraded iterative learning control algorithm, both the goal and the output of the air pressure inside the carriage will converge into a range determined by the amplitude and random interferences. By comparing with the traditional control algorithm, the upgraded iterative learning control algorithm is more adaptable to meet the needs of riding comfort.

Iterative learning control of air pressure variation inside a high-speed train under the excitation of the tunnel pressure wave with a fixed-morphologic form

2021 ◽  
pp. 095440972110327
Author(s):  
Zhiying He ◽  
Chunjun Chen ◽  
Dongwei Wang ◽  
Chao Deng ◽  
Jia Hu ◽  
...  
Keyword(s):  
Iterative Learning Control ◽  
Pressure Wave ◽  
High Speed ◽  
Learning Control ◽  
Pressure Variation ◽  
Iterative Learning ◽  
Air Pressure ◽  
High Speed Train ◽  
Comfort Index ◽  
Simulation Signal

Based on the characteristics that the tunnel pressure wave has a fixed-morphologic form when the same train passes through the same tunnel, an applicational approach based on the iterative learning control (ILC) is developed, aiming at overcoming the drawbacks of the traditional strategy for controlling the air pressure variation inside a high-speed train carriage. To achieve the goal, the control system is mathematically modelled. Then, the problem is formulated. The task of suppressing the influence of the tunnel pressure wave on the air pressure inside the carriages is shifted as an ILC problem of tracking the comfort index with varying trial length. The algorithm of refreshing the control signal from trial to trial is determined and the process of ILC control is designed. Next, the convergence of the newly-developed applicational ILC algorithm is discussed and the algorithm is simulated by the simulation signal and field-test signal. Results show that the applicational ILC algorithm be more adaptable in handling the control of the air pressure inside carriage under the excitation of varying-amplitude, varying-scale and varying-initial-states tunnel pressure wave. Meanwhile, the matching with tunnel pressure wave makes the applicational ILC algorithm will take both the riding comfort and fresh air into consideration, which upgrades the performances when the high-speed train passing through long tunnels.

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

2021 ◽  
Vol 11 (4) ◽  
pp. 1700
Author(s):  
Lemiao Qiu ◽  
Huifang Zhou ◽  
Zili Wang ◽  
Shuyou Zhang ◽  
Lichun Zhang ◽  
...  
Keyword(s):  
Pressure Fluctuation ◽  
Iterative Learning Control ◽  
High Speed ◽  
Learning Control ◽  
Flow Fields ◽  
External Flow ◽  
Iterative Learning ◽  
Air Pressure ◽  
Coupling Analysis ◽  
Pressure Compensation

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.

High-performance tracking of high-speed supercavitating vehicles with uncertain parameters using novel parameter-optimal iterative learning control

Robotica ◽  
2014 ◽  
Vol 33 (08) ◽  
pp. 1653-1670 ◽  
Author(s):  
Meisam Yahyazadeh ◽  
Abolfazl Ranjbar Noei
Keyword(s):  
Phase Behavior ◽  
Iterative Learning Control ◽  
High Speed ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Minimum Phase ◽  
Feed Forward ◽  
Feed Forward Control ◽  
Supercavitating Vehicle

SUMMARYThis paper proposes a new technique based on a Parameter-Optimal Iterative Learning Control (POILC) to track a command pitch rate of a high-speed supercavitating vehicle (HSSV). The pitch rate of a supercavitating vehicle has non-minimum phase behavior. Thus, tracking is fundamentally limited to poor performance. To solve this problem, a feed-forward control can be used while using the cavitator as a control input in the feed-forward path to modify the slow response caused by non-minimum phase behavior. The main idea of this paper is to apply the cavitator input with high precision as a feed-forward control to improve tracking performance. The exact value of the feed-forward control is achieved using POILC. However, in the presence of uncertainty, zero convergence of POILC algorithms is threatened. It will be shown that applying adaptive weight in the performance index, the convergence is guaranteed in the presence of uncertainty and also when the system is sign-indefinite. The proposed technique includes an optimal planning to make the error norm monotonically convergent to zero. The convergence and perfect tracking will be guaranteed through a Lyapunov candidate. Performance and significance of the proposed supercavitating vehicle control will be verified by simulation.

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