Load Simulator Control Method Based On Neural Networks

Load simulator was an important simulation system in hardware-in-loop flight simulation laboratory, which could simulate aerodynamic loads acting on flight vehicle, such as unmanned aerial vehicle, planes. It was one of most important devices in the whole flight vehicle simulation system. It afforded datum, which were contributed to the use of new flight vehicle. With the development of national defence industry, load simulator was becoming more and more important. In this thesis, a method that could improve the system performances of the load simulator has been given.How to eliminate the surplus torque of a loading system was one of the key problems to design a load simulator. First, in the thesis some effective control methods were introduced to eliminate the surplus torque. Then the principle of surplus torque was analyzed in detail, and some factors that influenced the surplus torque were given. Based on the learning characteristic of neural network and the function approximation ability of the neural networks, a hybrid control based on neural networks was proposed. The application results of the unmanned aerial vehicle load simulator showed that the proposed controller could eliminate the surplus torque effectively and improve the dynamic loading performances of the load simulator fairly. In addition, the results showed that the proposed controller was of fine robustness to unknown external load disturbances.