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Iterative Learning Model Predictive Control Approaches for Trajectory Based Aircraft Operation with Controlled Time of Arrival

  • Control Theory and Applications
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International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

In this work, iterative learning model predictive control approaches are presented to resolve the control problem of trajectory based aircraft operation with time-of-arrival constraint. Firstly, we formulated this problem being an output tracking issue with along-track wind disturbance. Next, P-type point-to-point iterative learning control algorithms are designed to overcome the influence of repetitive along-track wind. In addition, a point-topoint iterative learning model predictive control algorithm with variable prediction step is also designed to eliminate the interference of non-repetitive gusts on the air route. Finally, numerical simulations are provided to verify the effectiveness of the proposed method.

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Author information

Authors and Affiliations

  1. Advanced Control Systems Lab, School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Gaoyang Jiang

  2. College of Air Traffic Management, Civil Aviation University of China, Tianjin, 300300, China

    Gaoyang Jiang

  3. School of Automation, Qingdao University, Qingdao, 266071, China

    Zhongsheng Hou

  4. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Zhongsheng Hou

Authors
  1. Gaoyang Jiang
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  2. Zhongsheng Hou
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Corresponding author

Correspondence to Gaoyang Jiang.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Niket Kaisare under the direction of Editor Jay H. Lee. This work is supported by the National Natural Science Foundation of China under Grant (61433002).

Gaoyang Jiang received his B.S. degree in Electronic Information Engineering from the PLA Information Engineering University, Zhengzhou, China, in 2009, and an M.S. degree in Traffic Information Engineering and Control from the Civil Aviation University of China, Tianjin, China, in 2012. He is currently working toward a Ph.D. degree in Traffic Information Engineering and Control at Beijing Jiaotong University, Beijing, China. His research interests include data-driven control, iterative learning control, and model predictive control.

Zhongsheng Hou received his B.S. and M.S. degrees in applied mathematics from Jilin University of Technology, Changchun, China, in 1983 and 1988, respectively, and a Ph.D. degree in control theory from Northeastern University, Shenyang, China, in 1994. He was a Visiting Scholar at Yale University, New Haven, CT, USA, from 2002 to 2003. In 1997, he joined the Beijing Jiaotong University, Beijing, China, where he is currently a Full Professor and Founding Director of the Advanced Control Systems Laboratory, and the Head of the Department of Automatic Control. His research interests are the data-driven control, model free adaptive control, learning control, and intelligent transportation systems. Up to now, he has over 150 peer-reviewed journal papers published and over 140 papers in prestigious conference proceedings. He authors two monographs “Nonparametric Model and its Adaptive Control Theory, Science Press, China, 1999” and “Model Free Adaptive Control: Theory and Applications, CRC Press, 2013”. He served as an associate editor and guest editor for a few international journals and Chinese journals. He is an IEEE senior member, a member of IFAC Technical Committee “Adaptive and Learning Systems”, and a member of IFAC Technical Committee “Transportation Systems”. He is the Founding Director of The Technical Committee on Data Driven Control, Learning and Optimization (DDCLO), Chinese Association of Automation.

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Jiang, G., Hou, Z. Iterative Learning Model Predictive Control Approaches for Trajectory Based Aircraft Operation with Controlled Time of Arrival. Int. J. Control Autom. Syst. 18, 2641–2649 (2020). https://doi.org/10.1007/s12555-019-0590-7

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  • DOI: https://doi.org/10.1007/s12555-019-0590-7

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