Neural Network Based Model Predictive Control for a Quadrotor UAV

Neural Network Based Model Predictive Control for a Quadrotor UAV

Blog Article

A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development.However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challenging due to their irregular shape and low Reynolds number behavior.This work Philips HF-P Ballasts proposes an approach to model the full translational dynamics of a quadrotor UAV by a feedforward neural network, which is adopted as the prediction model in a model predictive controller (MPC) for precise position control.The raw flight data are collected by tracking various pre-designed trajectories with PX4 autopilot.The neural network model is trained to predict the linear accelerations from the flight log.

The neural network-based model predictive controller is then implemented with the automatic control and dynamic bottle optimization toolkit (ACADO) to achieve real-time online optimization.Software in the loop (SITL) simulation and indoor flight experiments are conducted to verify the controller performance.The results indicate that the proposed controller leads to a 40% reduction in the average trajectory tracking error compared to the traditional PID controller.