Position Difference for System Identification and Control of UAV Alap-Alap Using Back Propagation Algorithm Neural Network with Kalman Filter

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Provided by: Scientifantastic Apps
Topic: Networking
Format: PDF
To derive system identification and control of dynamic MIMO UAV nonlinear system, based on the collection of input-output data during sampled from a test flights, using artificial neural network is more convenient compared to physics and mathematics methods. The data is used as both training and testing set for artificial neural networks. There were 36250 input-output sampled flight data and grouped into two flight data sets. The first flight data set, a chirp signal, are used for training the neural network to determine parameters (weights) for the network, using all sample flight which are not belong to the second data set.
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