Trajectory Tracking Control of Planetary Entry Phase Based on Neural Network and Fractional Sliding Mode

Authors

Cunli Fan, 1. University of Beijing Information Science & Technology Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing 100192, China;;2. Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing 100192, China;;3. School of Automation, Beijing Information Science &Technology University, Beijing 100192, China;;
Dai Juan, 1. University of Beijing Information Science & Technology Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing 100192, China;;2. Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing 100192, China;;3. School of Automation, Beijing Information Science &Technology University, Beijing 100192, China;;
Haitao Liu, 1. University of Beijing Information Science & Technology Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing 100192, China;;2. Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing 100192, China;;3. School of Automation, Beijing Information Science &Technology University, Beijing 100192, China;;
Su Zhong, 1. University of Beijing Information Science & Technology Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing 100192, China;;2. Key Laboratory of Modern Measurement & Control Technology, Ministry of Education, Beijing 100192, China;;3. School of Automation, Beijing Information Science &Technology University, Beijing 100192, China;;
Zhu Cui, 4. School of Information and Communication Engineering, Beijing Information Science & Technology University, Beijing 100101, China;;
Wenting Xu, 5. School of Mathematics and Statistics, Hefei Normal University, Hefei 230601, China;

Abstract

Abstract: A fractional order sliding mode control method based on Radial Basis Function (RBF) neural network is proposed to solve the landing accuracy being affected by the interference during the landing process of planetary probe. Based on sliding mode control, a trajectory tracking control method for the entry phase of the probe is designed. Fractional calculus is introduced to alleviate the chattering caused by sliding mode control. RBF neural network is used to estimate and compensate the atmospheric density uncertainty. The method is applied to Mars landing scene simulation. The simulation results show that the proposed control method can accurately track the landing trajectory of the probe under the interference of unknown atmospheric density and uncertainty, so that the planetary probe can reach the parachute opening point with high accuracy and achieve stable landing of the planetary probe.

Recommended Citation

Fan, Cunli; Juan, Dai; Liu, Haitao; Zhong, Su; Cui, Zhu; and Xu, Wenting (2021) "Trajectory Tracking Control of Planetary Entry Phase Based on Neural Network and Fractional Sliding Mode," Journal of System Simulation: Vol. 33: Iss. 11, Article 18.
DOI: 10.16182/j.issn1004731x.joss.21-FZ0750
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss11/18

First Page

2697

Revised Date

2021-07-25

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-FZ0750

Last Page

2703

CLC

TP273.3

Recommended Citation

Fan Cunli, Dai Juan, Liu Haitao, Su Zhong, Zhu Cui, Xu Wenting. Trajectory Tracking Control of Planetary Entry Phase Based on Neural Network and Fractional Sliding Mode[J]. Journal of System Simulation, 2021, 33(11): 2697-2703.

Corresponding Author

Dai Juan,

DOI

10.16182/j.issn1004731x.joss.21-FZ0750