earticle

영어

Comparing to the big volume, large weight and high power consumption of the conventional samplers which are fixed on the lunar rover, the paper firstly described a novel flexible mini lunar sampling robot. Then the nonlinear dynamics resonance broken system is built to model the contact between the sampling robot and the lunar regolith. It is found to be suitable for drilling when the sampling robot is in the resonance condition. For the nonlinear time-varying system of the dynamic modeling of the sampler in drilling, we presented the method of the frequency neural-fuzzy adaptive control based on the dynamic resonant frequency prediction of the flexible sampling robot using neural networks. Firstly the algorithm predicts the dynamic resonant frequency of the sampling robot by GRNN. Then a neural-fuzzy adaptive control system is established, in which the frequency prediction error, the amplitude and its variable are adopted as the input and the sweep frequency bandwidth as the output, to adjust the frequency bandwidth dynamically. What’s more, the simulation results verify the effectiveness of the control strategy. Finally, the experimental results show that the control algorithm can improve the drilling depth, drilling efficiency and the discarding efficiency by 66.7%, 65.2% and 67.4%, respectively, in stimulant lunar regolith.