Position difference for system identification and control of UAV Alap-Alap using back propagation algorithm neural network with Kalman Filter

Using artificial neural network is more convenient compared to physics and mathematics methods 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. 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. Validation of the network is performed using the second data set, which were not used for training, which are representation of UAV circular flight movement. After an artificial neural network was trained using the training data set, the network is excited by the second set input data set. To make data, in particular the position, free from noise/glitch, the Kalman Filter is used before the position is further processed. The novelty lies on using difference instead of the absolute position only to predict/calculate next position. The outputs (position, roll, pitch and yaw), on the next period, produced by real UAV system were similar to the predicted outputs produced by Neural Network model. Furthermore adaptive direct inverse control is used to control the UAV follows a predetermined reference position.(EMS2022)