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

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

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.

Key words: planetary landing, trajectory tracking, radial basis function neural network, fractional calculus, sliding mode control

CLC Number:

TP273.3

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.

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