Dynamic Extended Output-Based Observer for an Adaptive Vertical Farm Quadcopter

This paper suggests a new discrete-time nonlinear observer design based on an output dynamic extension technique. This method aims to filter the output measurements by minimizing the impact of measurement noise, thereby, enhancing the accuracy and reliability of state estimates. In order to ensure the Input-to-State Stability (ISS) property of the estimation error, a novel LMI condition is proposed. An application on an agricultural quadcopter model operating in an Adaptive Vertical Farm showcases the effectiveness of the proposed estimation approach.