scholarly journals A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3066
Author(s):  
Egidio D’Amato ◽  
Vito Antonio Nardi ◽  
Immacolata Notaro ◽  
Valerio Scordamaglia
Keyword(s):  
Sensitivity Analysis ◽  
Fault Detection ◽  
Flight Control ◽  
Particle Filtering ◽  
Low Cost ◽  
Experimental Tests ◽  
Attitude Estimation ◽  
Fault Detection And Isolation ◽  
Raspberry Pi ◽  
Filter Approach

Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

scholarly journals Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Aerospace ◽  
2019 ◽  
Vol 6 (9) ◽  
pp. 94 ◽  
Author(s):  
Matteo D. L. Dalla Vedova ◽  
Alfio Germanà ◽  
Pier Carlo Berri ◽  
Paolo Maggiore
Keyword(s):  
Fault Detection ◽  
Flight Control ◽  
Failure Modes ◽  
Numerical Models ◽  
Short Circuit ◽  
Fault Detection And Isolation ◽  
Model Based ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Functional Components

Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.

System identification and robust attitude control of an unmanned helicopter using novel low-cost flight control system

2019 ◽  
Vol 234 (5) ◽  
pp. 634-645
Author(s):  
Mohammad Hossein Khalesi ◽  
Hassan Salarieh ◽  
Mahmoud Saadat Foumani
Keyword(s):  
Control System ◽  
Robust Control ◽  
System Identification ◽  
Flight Control ◽  
Low Cost ◽  
Superior Performance ◽  
Raspberry Pi ◽  
Unmanned Helicopter ◽  
Flight Control System ◽  
Robust Control System

In recent years, unmanned aerial systems have attracted great attention due to the electronic systems technology advancements. Among these vehicles, unmanned helicopters are more important because of their special abilities and superior performance. The complex nonlinear dynamic system (caused by main rotor flapping dynamics coupled with the rigid body rotational motion) and considerable effects of ambient disturbance make their utilization hard in actual missions. Attitude dynamics have the main role in helicopter stabilization, so implementing proper control system for attitude is an important issue for unmanned helicopter hovering and trajectory tracking performance. Besides this, experimental utilization of low-cost flight control system for unmanned helicopters is still a challenging task. In this article, dynamic modeling, system identification, and robust control system implementation of roll and pitch dynamics of an unmanned helicopter is performed. A TRex-600E radio-controlled helicopter is equipped with a novel low-cost flight control system designed and constructed based on Raspberry Pi Linux-based microcomputer. Using Raspberry Pi makes this platform simpler to utilize and more time and cost-effective than similar platforms used before. The experiments are performed on a 5-degree-of-freedom testbed. The robust control system is designed based on [Formula: see text] method and is evaluated in real flight tests. The experiment results show that the proposed platform has the ability to successfully control the roll and pitch dynamics of the unmanned helicopter.

A Particle Filter Approach for Identifying Tire Model Parameters From Full-Scale Experimental Tests

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Edoardo Sabbioni ◽  
Ruixin Bao ◽  
Federico Cheli ◽  
Davide Tarsitano
Keyword(s):  
Particle Filtering ◽  
Experimental Tests ◽  
Lateral Acceleration ◽  
Design Stage ◽  
Model Parameters ◽  
Tire Model ◽  
Passenger Cars ◽  
Sideslip Angle ◽  
Lane Changes ◽  
Filter Approach

Mathematical models simulating the handling behavior of passenger cars are extensively used at a design stage for evaluating the effects of new structural solutions or control systems. The main source of uncertainty in these type of models lies in tire–road interaction, due to high nonlinearity. Proper estimation of tire model parameters is thus of utter importance to obtain reliable results. This paper presents a methodology aimed at identifying the magic formula-tire (MF-Tire) model coefficients of the tires of an axle only based on measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed, and steer angle) during standard handling maneuvers (step-steers, double lane changes, etc.). The proposed methodology is based on particle filtering (PF) technique. PF may become a serious alternative to classic model-based techniques, such as Kalman filters. Results of the identification procedure were first checked through simulations. Then, PF was applied to experimental data collected using an instrumented passenger car.

A Particle Filter Approach for Identifying Tyre Model Parameters From Full-Scale Experimental Tests

2015 ◽  
Author(s):  
Ruixin Bao ◽  
Francesco Braghin ◽  
Federico Cheli ◽  
Edoardo Sabbioni
Keyword(s):  
Particle Filtering ◽  
Experimental Tests ◽  
Lateral Acceleration ◽  
Design Stage ◽  
Model Parameters ◽  
Passenger Cars ◽  
Sideslip Angle ◽  
Tyre Model ◽  
Lane Changes ◽  
Filter Approach

Mathematical models simulating the handling behavior of passenger cars are extensively used at a design stage for evaluating the effects of new structural solutions or control systems. The main source of uncertainty in this type of models lies in the tyre-road interaction, due high nonlinearity. Proper estimation of tyre model parameters is thus of utter importance to obtain reliable results. A methodology aimed at identifying the Magic Formula-Tyre (MF-Tyre) model coefficients of the tyres of an axle based only on the measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed and steer angle) during standard handling maneuvers (step-steers, double lane changes, etc.) is presented in this paper. The proposed methodology is based on Particle Filtering (PF) technique. PF may become a serious alternative to classic model-based techniques, such as Kalman filters. Results of the identification procedure were first checked through simulations. Then PF was applied to experimental data collected on an real instrumented passenger-car vehicles.

Non-Linear Complementary SO(3) Filters for Attitude Estimation and Navigation of Ground Vehicles

2014 ◽  
Vol 663 ◽  
pp. 254-258
Author(s):  
Fargham Sandhu ◽  
Hazlina Selamat ◽  
Yahaya Md Sam
Keyword(s):  
Low Cost ◽  
Attitude Estimation ◽  
Least Square ◽  
Low Grade ◽  
Ground Vehicles ◽  
Recursive Least Square ◽  
Stability And Control ◽  
Filter Approach ◽  
Navigational System ◽  
And Control

The use of Inertial Navigational System (INS) has been proven to be suitable for vehicular stability and control. The same system can be used for inertial based navigation in the absence of GPS. In this paper, the problem of obtaining good attitude estimates from low cost sensors used for car navigation in the absence of GPS data is discussed. The states to be estimated are using angular velocity and linear accleration signals obtained from the sets of gyros and accelerometers of the INS. The special orthogonal group, the SO(3)-based complementary filters, have been used as the estimators as they are most suited for embedded systems to generate highly efficient algorithms for navigation. The INS has also been integrated with a set of magnetometers to assist in achieving global navigation. This integration requires kinematics equations as well as the inclusion of the gyro and accelerometer calibration and filtering. By using the quatronion representation, not only highly compact algorithms for integration can be generated, but it can also estimate and remove the effects of other biases and misalignments caused by, for instance, inaccurate installations and inherent sensors problems. The results obtained through simulation indicate better performance then Kalman filter approach as well as iterative recursive least square approach even with low grade sensors. The results are comparable with attitude estimation using roll index but with much less computations and better performance.

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