A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Adaptive and Optimal Motion Control of Multi-UAV Systems

This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At fi rst, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixed-wing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations and experiments on a multi-quadrotor UAV system testbed

The Phoenix Drone: An Open-Source Dual-Rotor Tail-Sitter Platform for Research and Education

In this paper, we introduce the Phoenix drone: the first completely open-source tail-sitter micro aerial vehicle (MAV) platform. The vehicle has a highly versatile, dual-rotor design and is engineered to be low-cost and easily extensible/modifiable. Our open-source release includes all of the design documents, software resources, and simulation tools needed to build and fly a high-performance tail-sitter for research and educational purposes. The drone has been developed for precision flight with a high degree of control authority. Our design methodology included extensive testing and characterization of the aerodynamic properties of the vehicle. The platform incorporates many off-the-shelf components and 3D-printed parts, in order to keep the cost down. Nonetheless, the paper includes results from flight trials which demonstrate that the vehicle is capable of very stable hovering and accurate trajectory tracking. Our hope is that the open-source Phoenix reference design will be useful to both researchers and educators. In particular, the details in this paper and the available open-source materials should enable learners to gain an understanding of aerodynamics, flight control, state estimation, software design, and simulation, while experimenting with a unique aerial robot.Comment: In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'19), Montreal, Canada, May 20-24, 201

Attitude Estimation and Control Using Linear-Like Complementary Filters: Theory and Experiment

This paper proposes new algorithms for attitude estimation and control based on fused inertial vector measurements using linear complementary filters principle. First, n-order direct and passive complementary filters combined with TRIAD algorithm are proposed to give attitude estimation solutions. These solutions which are efficient with respect to noise include the gyro bias estimation. Thereafter, the same principle of data fusion is used to address the problem of attitude tracking based on inertial vector measurements. Thus, instead of using noisy raw measurements in the control law a new solution of control that includes a linear-like complementary filter to deal with the noise is proposed. The stability analysis of the tracking error dynamics based on LaSalle's invariance theorem proved that almost all trajectories converge asymptotically to the desired equilibrium. Experimental results, obtained with DIY Quad equipped with the APM2.6 auto-pilot, show the effectiveness and the performance of the proposed solutions.Comment: Submitted for Journal publication on March 09, 2015. Partial results related to this work have been presented in IEEE-ROBIO-201

An Application of UAV Attitude Estimation Using a Low-Cost Inertial Navigation System

Unmanned Aerial Vehicles (UAV) are playing an increasing role in aviation. Various methods exist for the computation of UAV attitude based on low cost microelectromechanical systems (MEMS) and Global Positioning System (GPS) receivers. There has been a recent increase in UAV autonomy as sensors are becoming more compact and onboard processing power has increased significantly. Correct UAV attitude estimation will play a critical role in navigation and separation assurance as UAVs share airspace with civil air traffic. This paper describes attitude estimation derived by post-processing data from a small low cost Inertial Navigation System (INS) recorded during the flight of a subscale commercial off the shelf (COTS) UAV. Two discrete time attitude estimation schemes are presented here in detail. The first is an adaptation of the Kalman Filter to accommodate nonlinear systems, the Extended Kalman Filter (EKF). The EKF returns quaternion estimates of the UAV attitude based on MEMS gyro, magnetometer, accelerometer, and pitot tube inputs. The second scheme is the complementary filter which is a simpler algorithm that splits the sensor frequency spectrum based on noise characteristics. The necessity to correct both filters for gravity measurement errors during turning maneuvers is demonstrated. It is shown that the proposed algorithms may be used to estimate UAV attitude. The effects of vibration on sensor measurements are discussed. Heuristic tuning comments pertaining to sensor filtering and gain selection to achieve acceptable performance during flight are given. Comparisons of attitude estimation performance are made between the EKF and the complementary filter

Validation and Experimental Testing of Observers for Robust GNSS-Aided Inertial Navigation

This chapter is the study of state estimators for robust navigation. Navigation of vehicles is a vast field with multiple decades of research. The main aim is to estimate position, linear velocity, and attitude (PVA) under all dynamics, motions, and conditions via data fusion. The state estimation problem will be considered from two different perspectives using the same kinematic model. First, the extended Kalman filter (EKF) will be reviewed, as an example of a stochastic approach; second, a recent nonlinear observer will be considered as a deterministic case. A comparative study of strapdown inertial navigation methods for estimating PVA of aerial vehicles fusing inertial sensors with global navigation satellite system (GNSS)-based positioning will be presented. The focus will be on the loosely coupled integration methods and performance analysis to compare these methods in terms of their stability, robustness to vibrations, and disturbances in measurements

Cooperative monocular-based SLAM for multi-UAV systems in GPS-denied environments

This work presents a cooperative monocular-based SLAM approach for multi-UAV systems that can operate in GPS-denied environments. The main contribution of the work is to show that, using visual information obtained from monocular cameras mounted onboard aerial vehicles flying in formation, the observability properties of the whole system are improved. This fact is especially notorious when compared with other related visual SLAM configurations. In order to improve the observability properties, some measurements of the relative distance between the UAVs are included in the system. These relative distances are also obtained from visual information. The proposed approach is theoretically validated by means of a nonlinear observability analysis. Furthermore, an extensive set of computer simulations is presented in order to validate the proposed approach. The numerical simulation results show that the proposed system is able to provide a good position and orientation estimation of the aerial vehicles flying in formation.Peer ReviewedPostprint (published version

UAV Attitude Estimation Using Low-Frequency Radio Polarization Measurements

A method of attitude determination, which makes use of measurements of the polarization of the magnetic field of low-frequency (LF) radio signals, is presented and evaluated. This approach offers advantages relative to existing accelerometer-based systems in high-acceleration, cost-constrained environments such as small fixed-wing unmanned aerial vehicles. Flight test results are presented which demonstrate that LF polarization measurements can be used to obtain a significantly more accurate result than traditional approaches.Engineering and Physical Sciences Research Council (Doctoral Training Account award)This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TAES.2016.263750

Gain-Scheduled Complementary Filter Design for a MEMS Based Attitude and Heading Reference System

This paper describes a robust and simple algorithm for an attitude and heading reference system (AHRS) based on low-cost MEMS inertial and magnetic sensors. The proposed approach relies on a gain-scheduled complementary filter, augmented by an acceleration-based switching architecture to yield robust performance, even when the vehicle is subject to strong accelerations. Experimental results are provided for a road captive test during which the vehicle dynamics are in high-acceleration mode and the performance of the proposed filter is evaluated against the output from a conventional linear complementary filter

MEMS 센서를 활용한 멀티로터형 무인항공기의 저비용 비행제어시스템의 설계에 관한 연구

학위논문(석사)--서울대학교 대학원 :공과대학 기계항공공학부,2019. 8. 여재익.흔히 드론(Drone)이라고 불리는 멀티로터형 무인항공기는 저렴하고 조종하기 쉬우며 간단한 구조와 수직 이착륙이 가능하여 군사적인 용도를 비롯하여 상업적인 용도로 널리 쓰이고 있다. 멀티로터형 무인항공기는 가속도 센서, 자이로스코프 센서를 포함하는 관성 측정 유닛(IMU)을 이용하여 지표면에 대한 자세를 측정하여 각 모터의 회전속도를 제어하는 방식으로 비행하며, 비행 방향을 추정할 수 있는 지자기 센서와 고도를 측정할 수 있는 기압계를 내장한다. 비행체에 탑재되는 비행제어유닛(Flight Control Unit, FCU)은 이러한 센서 데이터와 조종 명령을 이용하여 각 모터를 제어한다. 이러한 계산을 수행하기 위해서 기존의 비행 제어 시스템은 하나 이상의 32-bit 마이크로프로세서를 사용하며 이에 따라 비행제어를 위한 펌웨어를 개발하는데 있어 회로 및 패턴 설계 및 소프트웨어 개발 환경(SDK)을 구성하는데 있어 많은 시간과 인력, 비용을 필요로 하여 전체 시스템의 가격이 저렴하지 않다. 또한 작은 크기의 멀티로터에 사용되는 저렴한 비행제어유닛은 프로그래밍이 불가능하거나 확장성에 제약이 있어 하나의 제어 시스템으로 하나의 비행체 모델에만 적용하는 한계가 있다. 따라서 빠르고 간편하게 프로그래밍이 가능하며 저렴하고 구하기 쉬운 8-bit AVR 프로세서와 MEMS 센서, C/C++언어를 이용하여 비행제어시스템을 구성하였으며 그 결과 확장성을 갖추면서 가격이 저렴하면서 효율적인 비행 제어 시스템을 구성할 수 있었다. 부족한 8-bit 프로세서의 성능은 프로세서의 수량을 늘리는 병렬 컴퓨팅 방법으로 해결할 수 있었으며 상보필터의 간결한 구조로 인해 8-bit 프로세서의 낮은 컴퓨팅 성능으로도 초당 약 250Hz의 제어 주기를 가질 수 있었다. 자세 제어 알고리즘으로 Cascade controller를 선택하여 외란에 강하며 빠른 제어 속도를 얻을 수 있었다. 결과적으로 진동이 상대적으로 큰 팜 사이즈의 쿼드로터 UAV에서도 안정적인 비행 성능을 구현할 수 있었다.1. Introduction ············································································· 1 1. 1. About Research ···································································· 4 1. 2. Basic Theory ········································································ 6 1. 2. 1. Attitude Estimation ······························································· 8 1. 2. 2. Cascade PID Controller ······················································ 14 1. 3. Research Goal ······································································ 17 2. Hardware Design ·································································· 18 2. 1. PCB Design ············································································ 20 2. 1. 1. Design of Flight Controller ··············································· 20 2. 1. 2. Design of PMU for BLDC System ····································· 29 2. 2. Body Frame Design ·························································· 33 2. 2. 1. DC Motor Powered Quadcopter ······································ 34 2. 2. 2. BLDC Motor Powered Hexacopter ································· 36 3. Software Design ···································································· 38 3. 1. Flight Software Design ···················································· 38 3. 1. 1. Attitude Reference System ················································ 39 3. 1. 2. Cascade PID Controller ······················································ 44 3. 1. 3. Bluetooth-based Control System ···································· 46 3. 2. IMU & Attitude Reference System ··························· 51 3. 2. Attitude Control Performance ······································· 51 4. Conclusion ·············································································· 53 참고문헌 ························································································ 55 Abstract ························································································ 57Maste