Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty

Abstract

In the past decade, many approaches that attempted to solve the problem of optimal control and parameter estimation of an unmanned aerial vehicle with a priori uncertain parameters simply implied two ways to solve such problem. First, by the formation of optimal control based on a refined mathematical model of the unmanned aerial vehicle, and second, by using the estimation and identification methods of the model parameter of the unmanned aerial vehicle based on measured data from flight tests. However, the identification of the dynamic parameters of the unmanned aerial vehicle is a complicated task because of a number of factors such as random vibration noise, disturbance, and uncertainty of the sensor measurements. Due to the influence of random vibration noise, the problem of correlated vibration noises and uncertainty has encountered inevitably, and the accuracy of the state estimation for unmanned aerial vehicle is degraded. This study concentrates on the optimal control and state estimation for the unmanned aerial vehicle under the combination of both random vibration noise and uncertainty collected by the sensors. The effects of random vibrations at various stages of a large-scale flight that are a priori uncertain require the inclusion of identification algorithms in the optimal control loop. The results showed that the method used in the analysis had been able to provide accurate estimations.