Dynamic Based Estimator for UAVs with Real-time Identification Using DNN and the Modified Relay Feedback Test

TL;DR

This paper introduces a real-time adaptive estimation method for UAVs using dynamic models, DNN-MRFT parameter identification, and decoupled EKFs, enabling accurate state estimation without prior UAV parameter knowledge.

Contribution

The paper presents a novel real-time UAV state estimation approach combining DNN-MRFT and decoupled EKFs that do not require pre-flight UAV parameters.

Findings

Achieved smooth, lag-free rotational speed and inertial acceleration estimates.

Enabled UAVs to track aggressive trajectories with low-rate position data.

Reduced closed-loop control actions by over 6%.

Abstract

Control performance of Unmanned Aerial Vehicles (UAVs) is directly affected by their ability to estimate their states accurately. With the increasing popularity of autonomous UAV solutions in real world applications, it is imperative to develop robust adaptive estimators that can ameliorate sensor noises in low-cost UAVs. Utilizing the knowledge of UAV dynamics in estimation can provide significant advantages, but remains challenging due to the complex and expensive pre-flight experiments required to obtain UAV dynamic parameters. In this paper, we propose two decoupled dynamic model based Extended Kalman Filters for UAVs, that provide high rate estimates for position, and velocity of rotational and translational states, as well as filtered inertial acceleration. The dynamic model parameters are estimated online using the Deep Neural Network and Modified Relay Feedback Test (DNN-MRFT) framework, without requiring any prior knowledge of the UAV physical parameters. The designed filters with real-time identified process model parameters are tested experimentally and showed two advantages. Firstly, smooth and lag-free estimates of the UAV rotational speed and inertial acceleration are obtained, and used to improve the closed loop system performance, reducing the controller action by over 6 %. Secondly, the proposed approach enabled the UAV to track aggressive trajectories with low rate position measurements, a task usually infeasible under those conditions. The experimental data shows that we achieved estimation performance matching other methods that requires full knowledge of the UAV parameters.