Non-linear Dynamic Behavior Identification of a Quadcopter F450 Using an Artificial Neural Network-Based NARX Model

A quadcopter drone is an extremely complex, multi-variable, highly nonlinear, and underactuated system characterized by its six degrees of freedom controlled by only four actuators as inputs. This highlights the importance of employing advanced algorithms for its identification. Therefore, this research aimed to use a nonlinear neural network model to identify the dynamic behavior of a quadcopter based on the commercially available F450 frame. Data acquisition involved four experiments in a controlled environment for both roll and pitch angles, recording the signal duty cycles and the quadcopter’s attitude. Then, the selected non-linear autoregressive neural network model with exogenous inputs (N-NARX) model was trained using the acquired data along with the Levenberg-Marquardt algorithm. Afterwards, the response of the quadcopter’s actual attitude angles from the validation dataset was analyzed against the predicted values generated by the neural model, obtaining an 89.44% fit with an RMSE of 2.25% for the roll angle and an 89.29% fit with an RMSE of 2.20% for the pitch angle. Both attitude angles were subjected to a statistical cross-correlation validation to assess their relationship at different time lags, observing a solid settling within the confidence bands at a 95% level. It was concluded the proposed neural network model can effectively capture the quadcopter’s nonlinear dynamics.