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- W2302091666 abstract "Based on the characteristics of the attitude control system, this paper use the neural network predictive model controller which can identify accurately attitude system and obtain real-time control sequence applied to the controlled object through the model prediction, feedback correction and rolling optimization each link to complete motion attitude control of unmanned aerial vehicle. This control system has good control effect, meanwhile, the fast, accuracy and anti-disturbance capability tracking a given process can be effectively improved. There is a very significant piece of legislation that attitude control is extremely important for performance of the unmanned aerial vehicle (UAV ) flight system. However, Movement of UAV attitude in the actual process contains a lot of uncertain factors. To get a higher control precision, we only use more advanced control theory. In this paper, first of all, posture model was established through simplifying scientifically. Secondly, combining with the expected control goal and the actual conditions, we adopt the prediction algorithm based on BP neural network in the design and expect to achieve better control effect. Then in order to better show the advanced design performance of the controller, we will compare and analyze with the classic PID control performance. Finally, the system performances of robust and adaptability were studied and analyzed." @default.
- W2302091666 created "2016-06-24" @default.
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- W2302091666 date "2016-01-01" @default.
- W2302091666 modified "2023-09-24" @default.
- W2302091666 title "Attitude Control System Design of UAV" @default.
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- W2302091666 doi "https://doi.org/10.2991/nceece-15.2016.136" @default.
- W2302091666 hasPublicationYear "2016" @default.
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