Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (3): 668-680.doi: 10.23919/JSEE.2021.000057

• CONTROL THEORY AND APPLICATION • Previous Articles     Next Articles

Trajectory optimization of a reentry vehicle based on artificial emotion memory optimization

Shengnan FU1(), Liang WANG2(), Qunli XIA3,*()   

  1. 1 School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
    2 Beijing Aerospace Automatic Control Institute, Beijing 100854, China
    3 School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China
  • Received:2020-02-18 Online:2021-06-18 Published:2021-07-26
  • Contact: Qunli XIA;;
  • About author:|FU Shengnan was born in 1993. She received her B.E. degree from Beijing Institute of Technology in 2014. She is currently a doctoral student in School of Mechatronical Engineering, Beijing Institute of Technology. Her main research interests include flight vehicle design, guidance and control. E-mail:||WANG Liang was born in 1986. He received his Ph.D. degree in 2014. Now he is a senior engineer in Beijing Aerospace Automatic Control Institute. His main research interests include reentry vehicle attitude control and flight control. E-mail:||XIA Qunli was born in 1971. He received his B.E. degree in launching engineering, M.E. degree in flight mechanics and Ph.D. degree in aircraft design from Beijing Institute of Technology in 1993, 1996, and 1999 respectively. He is currently an associate professor in Beijing Institute of Technology. His research interests are missile guidance and control technology. E-mail:
  • Supported by:
    This work was supported by the Defense Science and Technology Key Laboratory Fund of Luoyang Electro-optical Equipment Institute, Aviation Industry Corporation of China (6142504200108)


The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization (AEMO) is discussed. Firstly, reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed. If the constraint is not satisfied, a distance measure and an adaptive penalty function are used to address this scenario. Secondly, AEMO is introduced to solve the trajectory optimization problem. Based on the theories of biology and cognition, the trial solutions based on emotional memory are established. Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum. The states of the trial solutions are determined according to the rules of memory enhancement and forgetting. As the iterations proceed, the trial solutions with poor quality will gradually be forgotten. Therefore, the number of trial solutions is decreased, and the convergence of the algorithm is accelerated. Finally, a numerical simulation is conducted, and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.

Key words: trajectory optimization, adaptive penalty function, artificial emotion memory optimization (AEMO), multiple constraint