Multi-channel design for iterative learning control

2004 ◽  
Author(s):  
Yongqiang Ye ◽  
Danwei Wang
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Channel Design

Iterative Learning Control for a Class of Linear Discrete-time Switched Systems

2014 ◽  
Vol 39 (9) ◽  
pp. 1564-1569 ◽  
Author(s):  
Xu-Hui BU ◽  
Fa-Shan YU ◽  
Zhong-Sheng HOU ◽  
Fu-Zhong WANG
Keyword(s):  
Discrete Time ◽  
Switched Systems ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning

scholarly journals Application of the Dynamic Iterative Learning Control to the Heteroplanar Active Magnetic Bearing

2020 ◽  
Vol 53 (2) ◽  
pp. 1511-1516
Author(s):  
Lukasz Hladowski ◽  
Arkadiusz Mystkowski ◽  
Krzysztof Galkowski ◽  
Eric Rogers ◽  
Bing Chu
Keyword(s):  
Iterative Learning Control ◽  
Learning Control ◽  
Magnetic Bearing ◽  
Iterative Learning ◽  
Active Magnetic Bearing

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.

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