Quadrotor Unmanned Air Vehicle Research Articles

Phase image-guided adaptive rotation-invariant feature point detector

ABSTRACT Vision-based autonomous obstacle avoidance for unmanned air vehicle (UAV) is a vital research field to ensure the safety of UAV flight in the national airspace. In this paper, a proposed detector is introduced to extract the feature (corner) points of static obstacles from the surrounding cluttered environment for small quadrotor UAVs flying at low altitudes. The detected feature points are the bottleneck for most autonomous obstacle avoidance to facilitate tracking and identification of risk processes during UAV's take-off phase. The proposed feature point detector is based on the estimated phase image of the first image derivatives and the homogeneity test examination to give the proposed detector adaptive behavior. The performance of the proposed detector is compared with various feature point detectors in terms of several quantitative assessment indices and environmental conditions. From the experimental results, the proposed detector achieves promising results for significantly extracting the surrounding feature points.

Active Disturbance Rejection Control of Quadrotor UAVs Based on Joint Observation and Feedforward Compensation

Under the influence of uncertain parameters and external disturbance, the accuracy is low and the response is slow of attitude and trajectory tracking in the flight control of the quadrotor unmanned air vehicles (UAV). In order to solve the problems, Because of the excellent adaptability and noise suppression ability of the extended Kalman filter for nonlinear system problems , it is used to suppress the high-frequency signal disturbance to reduce the estimation burden of the extended state observer, and jointly with the expanded state observer to estimate the total disturbance formed by the system uncertainty parameters and external disturbances to reduce system reliance on accurate models, and the differential values of perturbation estimated values is used by feedforward compensation to improve the tracking accuracy under abrupt disturbances and to overcome the phase lag caused by abrupt disturbances. Extended Kalman filtering, extended state observer with the feedforward compensation and PD controller with error compensation are comprehensively integrated into an improved active disturbance rejection controller which can suppress high frequency noise and abrupt disturbances to the better extent. Simulation and experiment results show that the joint observer can effectively reduce the observation error amplitude, correct the observation phase lag in advance, and obtain more accurate state information, and that the improved active disturbance rejection controller can better meet requirements of quadrotor UAV for fast response, efficient and stable control. Therefore, it can accurately and efficiently complete complex trajectory tracking operations.

Vector Field based Control of Quadrotor UAVs for Wildfire Boundary Monitoring

Accurate real-time information about an ongoing wildfire event is important for realizing effective and safe wildfire fighting. This paper is intended to solve the problem of guiding Unmanned Air Vehicles (UAVs) equipped with onboard cameras to monitor dynamic wildfire boundaries. According to whether the prior knowledge of the wildfire boundary is available or not, we propose a model-based vector field and a model-free vector field for UAV guidance. By describing the wildfire boundary with a zero level set function, the propagation of the wildfire boundary is modeled with the Hamilton-Jacobi equation. If the prior knowledge of the boundary is available, the typical radial basis function thin-plate spline is adopted to approximate the wildfire boundary and predicts its propagation. Then a 3D analytical vector field is constructed for an implicit function representing the wildfire boundary. If only partial observation of the wildfire boundary within the UAV’s field of view is available, the horizontal error between the UAV and its sensed segment of wildfire boundary and the vertical error between the UAV and the desired altitude are utilized to construct a 3D distance error based vector field, directly. To guide the UAV to converge to and patrol along the advancing wildfire boundary, the complex nonlinear dynamics of the UAV is exploited with differential flatness and incorporated with the above mentioned vector fields to design a nonlinear geometric controller. Computer simulations have been conducted to evaluate the performance of the proposed 3D vector field based controllers with both synthetic and real data, and simulation results demonstrate that the proposed algorithms can be effective methods to monitor the advancing wildfire boundaries.

Learning-based collision-free coordination for a team of uncertain quadrotor UAVs

This paper investigates the safe coordination problem for a team of quadrotor unmanned air vehicles (UAVs) in the presence of model uncertainties and polygonal obstacles. Firstly, collision avoidance potential functions (PFs) related with both positions and velocities are utilized to construct the basic and bounded controller for achieving collision-free coordination. Then, in order to enhance the tracking performance under model uncertainties, an complementary control is developed via approximate dynamic programming (ADP), where the unknown dynamics of UAVs are accurately estimated in finite-time. Through using stored data in learning-based ADP, the approximated error of the weights in critic neural network converges with finitely excited signal. The sufficient conditions of the closed-loop system stability and collision avoidance are derived. The performance of the learning-based coordination methodology is demonstrated via simulation results.

Quadrotor Accelerometer and Gyroscope Sensor Fault Diagnosis Using Nonlinear Adaptive Estimation Methods

This paper presents the design, analysis, and real-time experimental evaluation results of a nonlinear sensor fault diagnosis scheme for quadrotor unmanned air vehicles (UAV). The objective is to detect, isolate, and estimate sensor bias faults in accelerometer and gyroscope measurements. Based on the quadrotor dynamics and sensor models under consideration, the effects of sensor faults are represented as virtual actuator faults in the quadrotor state equation. Two nonlinear diagnostic estimators are designed to provide structured residualsfor fault detection and isolation. Additionally, after the fault is detected and isolated, a nonlinear adaptive estimation scheme is employed for estimating the unknown fault magnitude.The proposed fault diagnosis scheme is capable of handling simultaneous faults in the accelerometer and gyroscope measurements. The effectiveness of the fault diagnosis method is demonstrated using an indoor real-time quadrotor UAV test environment.

Nonlinear Disturbance Observer Based Sliding Mode Control of Quadrotor Helicopter

This paper develops tracking control of quadrotor unmanned air vehicle in the presence of parametric uncertainties and mismatched exogenous disturbances. First, a traditional sliding mode control (SMC) law is synthesized for a nominal model without parametric uncertainties and disturbances to achieve nominal control performance. Then to compensate the system uncertainties existing in the actual system, gain-scheduling based SMC law is synthesized. However, the gained scheduled SMC does not provide better control performance in the presence of unknown exogenous disturbances existing in actual system. To end this, a novel nonlinear disturbance observer is integrated with gain scheduled SMC to counteract the exogenous disturbances and attain the performance of reference nominal model. The proposed nonlinear disturbance observer SMC method exhibits the following two salient characteristics: first, the design gains should be greater than the bound of disturbance estimation error rather than that of disturbances, second; in the absence of uncertainties the nominal control performance of sliding mode control strategy is retained. The stability analysis of the proposed control strategy is validated using Lyapunov theorem. Finally, numerical simulation results are exhibited to illustrate the efficacy of the proposed control strategy.

Simulation and Analysis of a Quadrotor UAV while Landing.

Unmanned aerial vehicles are widely used in military and civilian fields in recent years. Quadrotor Unmanned Aerial Vehicles (UAV) have high advantage among other UAV’s, in different categories, due to their ability to hover, and Vertical Take-Off and Landing (VTOL) capability. The mathematical simulation method can be adopted for analysis of UAV. The simulation method can reduce the flight period, cost and risk and improve its performance while Vertical take-off and landing (VTOL). While landing of UAV, the kinetic energy of vehicle is absorbed by UAV frame resulting in high stress concentration. The stresses are also produced in other parts of the UAV. The effect of the landing loads and stresses on the airframe of the quadrotor unmanned air vehicle must be completely understood and the UAV must be designed accordingly in order not to damage during landing.These stresses can be analyzed by simulation method to ensure the sustainability of UAV structure. This work includes structural and frequency analysis for Quadrotor UAV chassis.

Quadrotor Sensor Fault Diagnosis with Experimental Results

This paper presents a fault detection, isolation, and estimation scheme for sensor bias faults in accelerometer and gyroscope measurements of quadrotor unmanned air vehicles (UAVs). Based on sliding-mode observer techniques, a robust estimation of the quadrotor roll and pitch angles is obtained by using only accelerometer measurements. Then, a diagnostic scheme is developed for detecting, isolating, and estimating sensor bias faults in the gyroscope and accelerometer measurements. Structured residuals are generated, allowing the detection and isolation of multiple simultaneous sensor faults under consideration. After the faults are detected and isolated, two nonlinear estimators are employed to provide an estimate of the unknown fault magnitude. The stability and estimation performance properties of the nonlinear estimators are established. The sensor fault diagnosis algorithm is implemented and evaluated through experimental results using a real-time indoor quadrotor test environment.

Online optimisation‐based backstepping control design with application to quadrotor

In backstepping implementation, the derivatives of virtual control signals are required at each step. This study provides a novel way to solve this problem by combining online optimisation with backstepping design in an outer and inner loop manner. The properties of differential flatness and the B-spline polynomial function are exploited to transform the optimal control problem into a computationally efficient form. The optimisation process generates not only the optimised states, but also their finite order derivatives which can be used to analytically calculate the derivatives of virtual control signal required in backstepping design. In addition, the online optimisation repeatedly performed in a receding horizon fashion can also realise local motion planning for obstacle avoidance. The stability of the receding horizon control scheme is analysed via Lyapunov method which is guaranteed by adding a parameterised terminal condition in the online optimisation. Numerical simulations and flight experiments of a quadrotor unmanned air vehicle are given to demonstrate the effectiveness of the proposed composite control method.

Quadrotor Accelerometer and Gyroscope Sensor Fault Diagnosis with Experimental Results


 
 
 This paper presents the design and real-time experimental results of a fault diagnosis scheme for inertial measurement unit (IMU) measurements of quadrotor unmanned air vehicles (UAVs). The objective is to detect, isolate, and estimate sensor bias fault in accelerometer and gyroscope measurements. Based on the quadrotor dynamics and sensor models under consideration, the effects of sensor faults are represented as virtual actuator faults in the quadrotor state equations. Two nonlinear diagnostic estimators are designed to provide structured residuals enabling the simultaneous detection and isolation of the sensor faults. Additionally, based on the detection and isolation scheme, two nonlinear adaptive estimators are employed for the estimation of the fault magnitude. The performance of the diagnosis method is evaluated and demonstrated through real-time flight experiments.
 
 

IMU Sensor Fault Diagnosis and Estimation for Quadrotor UAVs

This paper presents a fault detection, isolation and estimation scheme for sensor bias fault in inertial measurement units (IMUs) of quadrotor unmanned air vehicles (UAVs). By using estimated roll and pitch angles obtained using a sliding-mode observer method, a diagnostic algorithm is developed for detecting, isolating and estimating sensor bias fault in the gyroscope and accelerometer measurements. The stability and estimation performance properties of the fault estimation algorithm are rigorously established. Flight data collected from a real-time quadrotor test environment is used to illustrate the effectiveness of the proposed method.

Research of Robust Flight Control Algorithm for Quadrotor UAV

For quadrotor unmanned air vehicles (UAVs) nonlinear control problems under disturbance conditions, a nested loop control approach is presented to realize robust control. The robust control approach is proposed to solve the stabilization and navigation problems in the quadrotor. The robust control strategy is composed of two controllers. A nonlinear backstepping controller is designed for the inner loop to stabilize the attitude angle. A PID controller based on BP neural network is designed for the outer loop in order to generate the reference path for the inner loop. Numerous simulations and flight test experiments have been made to study the performance of controller based on the independently developed quadrotor UAV. The results illustrate that the proposed controller has good stability, maneuverability and robustness.

Quadrotor UAV Indoor Localization Using Embedded Stereo Camera

Localization of Small-Size Unmanned Air Vehicles (UAVs) such as the Quadrotors inGlobal Positioning System (GPS)-denied environment such as indoors has been done using varioustechniques. Most of the experiment indoors that requires localization of UAVs, used cameras or ultrasonicsensors installed indoor or applied indoor environment modification such as patching (InfraRed) IR and visual markers. While these systems have high accuracy for the UAV localization, theyare expensive and have less practicality in real situations. We propose a system consisting of a stereocamera embedded on a quadrotor UAV for indoor localization. The optical flow data from the stereocamera then are fused with attitude and acceleration data from our sensors to get better estimationof the quadrotor location. Using stereo camera capabilities the quadrotor altitude are estimated usingSIFT Feature Stereo Matching are used in addition to the altitude estimation computed using opticalflow. To avoid latency due to computational time, image processing and the quadrotor control areprocessed threads and core allocation.

Dört rotorlu bir insansiz hava aracinin geri-adimlama yöntemi ile yol takibi kontrolü

ÖzetDört rotorlu(quadrotor), son yıllarda bir çok araştırmacı tarafından çalışılan çok popüler bir insansız hava aracıdır(İHA). Dört rotorlu, dikey iniş kalkış(VTOL) özelliği ve yüksek manevra kabiliyeti sayesinde klasik insansız hava araçlarına göre bir çok avantaja sahiptir. VTOL özelliği sayesinde, özellikle engebeli ve kısıtlı ortamlarda, uzun iniş ve kalkış pistine ihtiyaç duymadan kullanılabilmektedir. Ayrıca, dört rotorlu, VTOL özelliği taşıyan diğer İHA'lara göre, mekanik olarak daha basit bir yapıya sahiptir. Fakat, belirtilen avantajların yanı sıra, dört rotorlu, yüksek derecede doğrusal olmayan ve kararsız bir dinamiğe sahiptir. Bu nedenle, dört rotorlu'nun otonom kontrolü çeşitli zorluklar içermektedir ve bir çok araştırmacının ilgisini çekmektedir.Bu çalışmada, dört rotorlu bir insansız hava aracının, doğrusal olmayan bir kontrol metodu olan geri-adımlama yöntemi kullanılarak, yol(yörünge) takibi kontrolü sağlanmıştır. Kontrol sistemi oluşturulurken, dört rotorlu'nun yönelimi iç döngü olarak, pozisyonu ise dış döngü olarak kontrol edilerek, hava aracının istenilen yolu yüksek doğrulukla takip etmesi amaçlanmıştır. İlk olarak, dört rotorlu'nun doğrusal olmayan dinamik modeli Newton hareket denklemleri kullanılarak elde edilmiştir. Doğrusal olmayan bu model simülasyonlarda dinamik model olarak kullanılmıştır. Daha sonrasında, doğrusal olmayan dinamik model, kontrolcü tasarımında kullanılmak üzere, belirli varsayımlar aracılığıyla basitleştirilmiştir. Basitleştirilmiş dinamik model kullanılarak, geri adımlamalı kontrolcü, üç aşamada elde edilmiştir. Elde edilen kontrolcü, MATLAB/Simulink ortamında modellenmiş ve test edilmiştir. Ayrıca, kontrolcünün güçlü derecedeki bozuculara karşı dayanıklılığı da simülasyon ortamında denenmiştir. Elde edilen simülasyon sonuçları, geri-adımlamalı kontrolcü sayesinde, istenilen yolun yüksek doğrulukta takip edildiğini göstermiştir. Ayrıca, bozucular eklenerek yapılan simülasyonlarda, kontrolcünün güçlü derecedeki çeşitli bozucuları etkili şekilde bastırdığı görülmüştür.

Attitude Stabilization Control of a Quadrotor UAV by Using Backstepping Approach

The modeling and attitude stabilization control problems of a four-rotor vertical takeoff and landing unmanned air vehicle (UAV) known as the quadrotor are investigated. The quadrotor’s attitude is represented by the unit quaternion rather than Euler angles to avoid singularity problem. Taking dynamical behavior of motors into consideration and ignoring aerodynamic effect, a nonlinear controller is developed to stabilize the attitude. The control design is accomplished by using backstepping control technique. The proposed control law is based on the compensation for the Coriolis and gyroscope torques. Applying Lyapunov stability analysis proves that the closed-loop attitude system is asymptotic stable. Moreover, the controller can guarantee that all the states of the system are uniformly ultimately bounded in the presence of external disturbance torque. The effectiveness of the proposed control approach is analytically authenticated and also validated via simulation study.

Experimental quadrotor flight performance using computationally efficient and recursively feasible linear model predictive control

A new linear model predictive control (MPC) algorithm in a state-space framework is presented based on the fusion of two past MPC control laws: steady-state optimal MPC (SSOMPC) and Laguerre optimal MPC (LOMPC). The new controller, SSLOMPC, is demonstrated to have improved feasibility, tracking performance and computation time than its predecessors. This is verified in both simulation and practical experimentation on a quadrotor unmanned air vehicle in an indoor motion-capture testbed. The performance of the control law is experimentally compared with proportional-integral-derivative (PID) and linear quadratic regulator (LQR) controllers in an unconstrained square manoeuvre. The use of soft control output and hard control input constraints is also examined in single and dual constrained manoeuvres.

Health-Aware Coverage Control With Application to a Team of Small UAVs

This paper studies the problem of controlling a team of vehicles in order to cover a region with their onboard sensors. The goal is to perform such a coverage task when the capability of the vehicles to carry out the task varies through time. We consider variable sensing performance and the loss of vehicles. Algorithms are proposed to enable coverage despite such variable health conditions. These algorithms are validated through experiments with quad-rotor unmanned air vehicles. Since these algorithms require communication among the vehicles, simulations are carried out to show that the proposed algorithms degrade nicely when perfect communication is not possible.

Robust Flight Control of Quadrotor Unmanned Air Vehicles

For quadrotor unmanned air vehicles(UAVs) flight control problem,a robust controller design approach is presented.The controller consists of two parts: an inner-loop attitude controller and an outer-loop position controller.The attitude controller is designed with robust control based on signal compensation.The position controller is realized with classical PD method.It is applied to a quadrotor UAV developed in our laboratory to realize indoor hovering.Experimental results demonstrate the effectiveness of this control approach.

Toward a Science of Cyber–Physical System Integration

System integration is the elephant in the china store of large-scale cyber-physical system (CPS) design. It would be hard to find any other technology that is more undervalued scientifically and at the same time has bigger impact on the presence and future of engineered systems. The unique challenges in CPS integration emerge from the heterogeneity of components and interactions. This heterogeneity drives the need for modeling and analyzing cross-domain interactions among physical and computational/networking domains and demands deep understanding of the effects of heterogeneous abstraction layers in the design flow. To address the challenges of CPS integration, significant progress needs to be made toward a new science and technology foundation that is model based, precise, and predictable. This paper presents a theory of composition for heterogeneous systems focusing on stability. Specifically, the paper presents a passivity-based design approach that decouples stability from timing uncertainties caused by networking and computation. In addition, the paper describes cross-domain abstractions that provide effective solution for model-based fully automated software synthesis and high-fidelity performance analysis. The design objectives demonstrated using the techniques presented in the paper are group coordination for networked unmanned air vehicles (UAVs) and high-confidence embedded control software design for a quadrotor UAV. Open problems in the area are also discussed, including the extension of the theory of compositional design to guarantee properties beyond stability, such as safety and performance.

A Continuous Local Motion Planning Framework for Unmanned Vehicles in Complex Environments

As the complexity of an unmanned vehicle's operational environment increases so does the need to consider the obstacle space continually, and this is aided by splitting the motion planning functionality into distinct global and local layers. This paper presents a new continuous local motion planning framework, where the output and control space elements of the traditional receding horizon control problem are separated into distinct layers. This separation reduces the complexity of the local motion trajectory optimisation, enabling faster design and increased horizon length. The focus of this paper is on the output space component of this framework. Bezier polynomial functions are used to describe local motion trajectories which are constrained to vehicle performance limits and optimised to track a global trajectory. Development and testing is in simulation, targeted at a nonlinear model of a quadrotor unmanned air vehicle. The defined framework is used to provide situation-aware tracking of a global trajectory in the presence of static and dynamic obstacles, as well as realistic turbulence and gusts. Also demonstrated is the immediate-term decentralised deconfliction of multiple unmanned vehicles, and multiple formations of unmanned vehicles.