Robust adaptive three-dimensional trajectory tracking control scheme design for small fixed-wing UAVs

This paper aims to tackle the three-dimensional trajectory tracking problems of small fixed-wing unmanned aerial vehicles subject to nonlinearities, uncertainties and wind disturbances via adaptive techniques. The control objective is to efficiently control the thrust and the deflections of control surfaces to ensure the unmanned aerial vehicle arrives at a specified location within a given time frame. However, achieving this goal for small fixed-wing unmanned aerial vehicles can be challenging because the precise dynamic model and several parameters are not accessible, making most existing control strategies unworkable. Motivated by these facts, based on feedback linearization techniques, we derive linear models with equivalent disturbances to describe the translational dynamics without requiring precise aerodynamic force model information. To deal with the dilemmas where the norm bounds of equivalent disturbances depend on control inputs, system states, and unknown disturbances, a novel robust adaptive control strategy is designed for position control. Based on the assumption of two-time separation, the control scheme incorporates two parts, namely, a position controller containing the horizontal-plane and altitude parts and a robust filter-based attitude regulator. Also, to prevent chattering issues, we design a practical and robust adaptive position controller under which the tracking error is ultimately bounded The overall closed-loop stability is theoretically investigated based on the Lyapunov arguments. Hardware-in-loop simulation experiments are performed to testify our developed control scheme.

Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions

Fixed-wing Unmanned Aerial Vehicles (FW-UAVs) are intelligent aircrafts. It is of significance to carry out fault diagnosis of FW-UAVs to improve reliability and safety. An entire mission of FW-UAVs contains couple of phases; correspondingly, this paper treats FW-UAVs as multiple operation condition processes. An innovative framework is then proposed for fault diagnosis of FW-UAVs, where the process dynamics, multiple operation conditions, variable data density, and process disturbance are considered. Firstly, augmented matrixes are constructed with the data samples to involve the dynamic characteristic of FW-UAVs. Secondly, a modified DBSCAN algorithm employing Shared Nearest Neighbor based Distance (SNND-DBSCAN) and a K Nearest Neighbor algorithm employing SNND (SNND-KNN) are proposed respectively. They cooperate with each other to realize offline operation condition classification and online recognition. Thirdly, Multiple condition oriented Dynamic KPCA (M-DKPCA) algorithms incorporated with Weighted sliding window denoising (WM-DKPCA) is proposed for fault diagnosis. Finally, the proposed algorithms are tested with real flight data sets in terms of linear and nonlinear faults; and the comparisons between KPCA, DKPCA, M-DKPCA and WM-DKPCA are presented. The results confirm that the multiple condition oriented M-DKPCA and WM-DKPA algorithms are more suitable for fault diagnosis of FW-UAVs; and WSW denoising can indeed improve the fault diagnosis performance.