Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (4): 805-811.doi: 10.23919/JSEE.2022.000080

• CLOUD CONTROL SYSTEMS • Previous Articles     Next Articles

Research on UAV cloud control system based on ant colony algorithm

Lanyong ZHANG(), Ruixuan ZHANG*()   

  1. 1 School of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China
  • Received:2022-02-28 Online:2022-08-30 Published:2022-08-30
  • Contact: Ruixuan ZHANG;
  • About author:|ZHANG Lanyong was born in 1983. He received his Ph.D. degree in engineering from Harbin Engineering University in 2011. Currently he is the director of the Institute of Intelligent Information Processing and Control Engineering at Harbin Engineering University, Harbin, China. His research interests include cluster intelligent control and robot control. E-mail:||ZHANG Ruixuan born in 1996. He received his B.S. degree in measurement and control technology and instruments from Hebei University in 2019. He received his M.S. degree in control engineering from Harbin Engineering University in 2022. His research interests include intelligent control and cloud control. E-mail:
  • Supported by:
    This work was supported by the Natural Science Foundation of Heilongjiang Province (LH2021E045).


In the cloud era, the control objects are becoming larger and the information processing is more complex, and it is difficult for traditional control systems to process massive data in a timely manner. In view of the difficulty of data processing in the cloud era, it is extremely important to perform massive data operations through cloud servers. Unmanned aeriel vehicle (UAV) control is the representative of the intelligent field. Based on the ant colony algorithm and incorporating the potential field method, an improved potential field ant colony algorithm is designed. To deal with the path planning problem of UAVs, the potential field ant colony algorithm shortens the optimal path distance by 6.7%, increases the algorithm running time by 39.3%, and increases the maximum distance by 24.1% compared with the previous improvement. The cloud server is used to process the path problem of the UAV and feedback the calculation results in real time. Simulation experiments verify the effectiveness of the new algorithm in the cloud environment.

Key words: ant colony algorithm, potential field method, cloud server, path planning