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During the flight, the airship load will be subject to various external disturbances, which will make the load camera unclear. To solve this problem, we have established a passive and vibration isolation platform, and used the characteristic model control method in the active vibration isolation system and the stable platform system. Firstly, the super magnetostrictive actuator is selected as the active control component to establish the active-passive integrated vibration isolation platform model. Then the internal relationship of the stable platform is analyzed, and the stepping motor control model of the stable platform is established. Finally, the characteristic modeling is carried out. The identified characteristic parameters are combined with the designed adaptive tracking controller, golden section controller and logic differential controller to control the active vibration isolator and the stable platform. The simulations show that the designed controller is effective.

This paper research on the working quality of the payload of a stratospheric airship at high altitudes. When the stratospheric airship working on the stratosphere, it is confronted with many complicated factors. Taking the stable platform equipped with the airship as the research object, combining Kalman filter and fuzzy PID control algorithm, we propose the algorithm which named fuzzy PID control algorithm based on Kalman filter. Simulink simulation results show that the proposed algorithm not only has the advantages of fast response, small overshoot, high control precision, but also can effectively restrain the interference in the environment, which make the stratospheric airship stability.

This paper investigates the wind speed forecast for the stratospheric airship fixed over a geo-location because the wind speed forecast is a key challenge for the airship station-keeping control. In view of the wind speed series which changes with the time and space and shows the non-linear and non-stationary characteristics, this paper put forward a kind of adaptive model based on Incremental extreme learning machine(I-ELM). Firstly, in order to avoid the random selection of input dimensionality of the I-ELM, the phase space of the wind speed is reconstructed. Secondly, search and optimize the model parameter of the I-ELM to improve the generalization performance of the I-ELM. Finally, the prediction model is established by using the optimized I-ELM, and the results of the online prediction of short-term wind speed are obtained. The simulation result verifies that the proposed forecasting model is able to excavate the wind speed series features effectively and has relatively high prediction accuracy.