A hybrid disturbance observer for delivery drone's oscillation suppression. (December 2022) - Chen, Zhu; Liu, Chang; Su, Hao; Liang, Xiao; Zheng, Minghui
Abstract: This paper proposes a new hybrid disturbance observer (DOB) to help suppress disturbance to the control systems. The proposed hybrid DOB consists of three main parts: (1) an actual system, (2) a simulated system, and (3) a learning filter that connects the actual and simulated systems. The simulated system aims to replicate the actual system response, where it leverages a neural network model to predict the input disturbance and generate the predicted system response. Such system response is used to generate a learning signal through a learning filter; this learning signal is then added to the feedforward loop of the estimation framework to enhance the disturbance estimate and its suppression performance for the actual system. The proposed hybrid DOB is designed to advance the standard DOB structure with a learning-based feedforward compensation. While the proposed method does not modify the baseline controller, it is well suited to systems whose baseline controllers are difficult or impossible to be changed. Considering the delivery drones are subject to oscillations when dropping payloads, experimental tests with multiple payload dropping scenarios have been conducted using both the hybrid and standard DOB, where the compared results validate the effectiveness and advantages of the proposed hybrid DOB. Highlights: A new hybrid learning-based disturbance observer (DOB) is proposed to suppress drone's oscillation. The learning filter design problem has been systematically formulated into an optimization problem. Performance improvement is theoretically guaranteed and experimental verified.