Przemyslaw Gasior

Bio: Przemyslaw Gasior is an academic researcher from Poznań University of Technology. The author has contributed to research in topics: Multirotor & Propulsion. The author has an hindex of 4, co-authored 8 publications receiving 59 citations.

Fault diagnosis and condition monitoring of UAV rotor using signal processing

Abstract: In this paper, a method for fault detection of physical impairment of UAV rotor blades is presented. Actuators in multirotor UAV (Unmanned Aerial Vehicle) systems are common subjects fault diagnosis methods which are an essential part of the active fault-tolerant control scheme. Defects in a propulsion system of the aerial vehicle lead to the loss of thrust generated by rotors and as a result, to the disturbance of thrust balance, higher power consumption and further degradation resulting in the possible crash of the vehicle. Authors propose a three-stage algorithm based on the signal processing and machine learning to detect the occurrence of rotor fault, determine its scale and type. The method is based on measurements of acceleration from the onboard IMU (Inertial Measurement Unit) sensor as unbalanced rotating parts commonly cause vibrations in mechanical systems. The acceleration signal is stored in a cyclic buffer and then processed by simple feature extraction algorithms in order to obtain a characteristic signature of the faulty state. Three different methods of feature extraction are considered in this article, along with the analysis of variable buffer length. Next, the Support Vector Machine (SVM) classifier is used to determine the occurrence and character of the rotor fault. The presented solution was verified in series of experiments proving its effectiveness. In addition, such approach based on signal processing is very versatile and easy to implement in arbitrary flight controller.

Performance of Coaxial Propulsion in Design of Multi-rotor UAVs

Abstract: There are many different types of propulsion systems developed for multi rotor UAVs. One of the most interesting designs is so called X8 quadrocopter, which extends original quadrotor concept to 8 motors, arranged in 4 coaxial pairs. The advantage of this solution is increased lift of platform, with reasonable volume of platform kept. However, this design suffers from the loss of efficiency due to coaxial propellers’ configuration, because the lower propeller loses thrust working in prop wash of upper propeller. This paper presents the experimental verification of performance of such propulsion system in practical terms of designing multi rotor platforms, comparing to design with 8 isolated propulsion units. In addition, its advantages versus classic quadrotor concept is shown. The series of experiments with different motors and sizes of propellers were conducted to estimate efficiency of coaxial propulsion regarding useful thrust generated by each configuration.

Thrust estimation by fuzzy modeling of coaxial propulsion unit for multirotor UAVs

Abstract: In this paper, a simple and easily applicable model of the coaxial propulsion unit for multirotor UAVs is presented. Measurements performed on the experimental test bench provided information about the generated thrust in relation to PWM control signals and supply voltage. Modelling techniques based on Takagi-Sugeno fuzzy interface and surface fitting are proposed. Implementation of the first order inertial element with the varying time constant allows to consider the propulsion unit's dynamics. A fuzzy model was chosen for implementation taking into consideration a computational complexity benchmark. Fusion of four independent models provides information about a total thrust generated by the physical platform during real flight scenarios. Promising results of experimental studies open the way for possible applications of the presented method, such as expanding estimation algorithms of attitude and vertical velocity or improvement of the mathematical model.

Robust estimation algorithm of altitude and vertical velocity for multirotor UAVs

Abstract: This paper describes the process of the development and improvement of the altitude and vertical velocity estimation algorithm. The previous method was developed by authors two years back. After a diagnosis of pressure temperature drift caused by the main IMU, the additional barometric sensor was introduced. Based on readings from this reference component, three main modifications were developed to the algorithm's structure. In addition, three experimental sequences are presented to compare the previous approach with new ones. Results showed that new methods achieve better performance and are free from pressure drift caused by sensor's heating. Decreasing frequency of altitude measurements in Kalman filter resulted in more robust estimates for pressure changes not related to the vertical movement in indoor and outdoor flights. With properly working estimation algorithm, there is a possibility to develop a controller to maintain desired altitude or vertical velocity.

Cascade control algorithms of position and attitude for multirotor UAV

Abstract: Control algorithms are essential in multirotor aerial platforms and remain a trending research topic This paper presents the development process of different cascade controllers for multirotor UAVs Two main usage areas of this method are described — the control of position and attitude Based on the literature and experience from previous research, structures of mentioned algorithms are proposed Adequate data-driven simulation models are formulated and used in the tuning process Additionally, a customised test bench was constructed and utilised for experiments Comparison between previous solutions and new control structures is performed in both simulation and field experiments Better performance, robustness and flexibility of cascade control algorithms are proven on the basis of gathered results

Anomaly Detection and Condition Monitoring of UAV Motors and Propellers

Abstract: An early detection of fault components is crucial for unmanned aerial vehicles (UAVs), The goal of this paper is to develop a monitoring system to early detect possible faults of UAV motors and propellers. Motor current signature analysis (MCSA) approach is used to analyze the stator current signals under different conditions. Then, fuzzy adaptive resonance (Fuzzy ART) neural network (NN), which is an unsupervised learning scheme, is employed to judge whether motors are operating in normal or faulty condition. In addition, the vibration signature analysis (VSA) technique is employed to monitor the UAV propellers. A Q-Iearning-based Fuzzy ARTMAP NN is used to learn extracted statistical features, and the Genetic algorithm (GA) is used to select an optimal subset of features through an off-line manner in order to reduce computational time. The experimental results validated the effectiveness of the proposed model in detecting faults of UAV motors and propellers as compared with CART, KNN, NB and SVM.

Design Methodology for Heavy-Lift Unmanned Aerial Vehicles with Coaxial Rotors

Abstract: This work presents a novel design methodology for multirotor Unmanned Aerial Vehicles (UAVs). To specifically address the design of vehicles with heavy lift capabilities, we have extended existing design methodologies to include coaxial rotor systems which have exhibit the best thrust-to-volume ratio for operation of UAVs in urban environments. Such coaxial systems, however, come with decreased aerodynamic efficiency and the design approach developed in this work can account for this. The proposed design methodology and included market studies have been demonstrated for the development of a multi-parcel delivery drone that can deliver up to four packages using a novel morphing concept. Flight test results in this paper serve to validate the predictions of thrust and battery life of the coaxial propulsion system suggesting errors in predicted flight time of less than 5 percent.