Adaptive Iterative Learning Control Based High Speed Train Operation Tracking Under Iteration-Varying Parameter and Measurement Noise

2015 ◽  
Vol 17 (5) ◽  
pp. 1779-1788 ◽  
Author(s):  
Zhenxuan Li ◽  
Zhongsheng Hou
Keyword(s):  
Iterative Learning Control ◽  
High Speed ◽  
Learning Control ◽  
Iterative Learning ◽  
Measurement Noise ◽  
High Speed Train ◽  
Adaptive Iterative Learning Control ◽  
Train Operation

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.

Adaptive iterative learning control for switched discrete-time systems with stochastic measurement noise

2019 ◽  
Vol 42 (2) ◽  
pp. 259-271
Author(s):  
Yan Geng ◽  
Xiaoe Ruan
Keyword(s):  
Linear Systems ◽  
Discrete Time ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Measurement Noise ◽  
System Matrix ◽  
Adaptive Iterative Learning Control ◽  
Matrix Estimation

This paper investigates an adaptive iterative learning control (AILC) scheme for a class of switched discrete-time linear systems with stochastic measurement noise. For the case when the subsystems dynamics are unknown and the switching rule is arbitrarily fixed, the iteration-wise input-output data-based system lower triangular matrix estimation is derived by means of minimizing an objective function with a gradient-type technique. Then, the AILC is constructed in an interactive form with system matrix estimation for the switched linear systems to track the desired trajectory. Based on the derivation of the boundedness of the estimation error of system matrix, by virtue of norm theory and statistics technique, the tracking error and the covariance matrix of the tracking error are derived to be bounded, respectively. Finally, the AILC concept is extended to nonlinear systems by utilizing linearization techniques. Simulation results illustrate the validity and effectiveness of the proposed AILC schemes.

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.

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