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Non-linear Dynamic Behavior Identification of a Quadcopter F450 Using an Artificial Neural Network-Based NARX Model
Identificación del comportamiento dinámico no lineal de un quadcopter F450 utilizando un modelo NARX basado en redes neuronales artificiales
DOI:
https://doi.org/10.15446/ing.investig.109708Keywords:
drone, system identification, underactuation, neural network, roll, pitch (en)dron, identificación de sistema, subactuado, red neuronal, alabeo, cabeceo (es)
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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.
El dron quadcopter es un sistema extremadamente complejo, multivariable, altamente no lineal y subactuado, caracterizado por sus seis grados de libertad controlados por solo cuatro actuadores como entradas. Esto resalta la importancia de emplear algoritmos avanzados para su identificación. Por lo tanto, esta investigación tuvo como objetivo utilizar un modelo de red neuronal no lineal para identificar el comportamiento dinámico de un quadcopter basado en el frame comercial F450. La adquisición de datos involucro cuatro experimentos en un entorno controlado para los angulos de alabeo y cabeceo, registrando los ciclos de trabajo de la señal y la actitud del quadcopter. Luego, se entrenó el modelo seleccionado de red neuronal autoregresiva no lineal con entradas exógenas (N-NARX), utilizando los datos adquiridos junto con el algoritmo de Levenberg-Marquardt. Posteriormente, se analizó la respuesta de los ángulos de actitud reales del quadcopter en el conjunto de datos de validación frente a los valores predichos generados por el modelo neuronal, obteniendo un ajuste del 89.44 % con un RMSE del 2.25 % para el ángulo de alabeo y un ajuste del 89.29 % con un RMSE del 2.20 % para el ángulo de cabeceo. Ambos ángulos de actitud fueron sometidos a una validación estadística de correlación cruzada para evaluar su relación en diferentes desfases temporales, observándose una sólida estabilización dentro de las bandas de confianza al nivel del 95 %. Se concluyo que el modelo de red neuronal propuesto puede capturar de manera efectiva las dinámicas no lineales del quadcopter.
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