Position Of UAV Research Articles

Fuzzy intelligent control method for improving flight attitude stability of plant protection quadrotor UAV

At present, the attitude control method of plant protection UAV is the classical PID control, but there are some imperfections in the PID control, such as the contradiction between speediness and overshoot, the weak anti-jamming ability and adaptability. The physical parameters of plant protection UAV are time-varying, and the airflow also interferes with it. The control ability of classical PID is limited, and its control parameters are fixed, and its anti-jamming ability and adaptability are not strong. Therefore, a fuzzy adaptive PID controller is proposed in this paper. Fuzzy logic control is used to optimize the control parameters of PID in order to improve the dynamic and static performance and adaptability of attitude control of plant protection UAV. In the process of research, the mathematical model of UAV is established firstly, then the fuzzy adaptive PID is designed, and then the simulation is carried out in Simulink. The simulation results show that the fuzzy adaptive PID controller has better dynamic and static control performance and adaptability than the traditional PID controller. Therefore, the proposed control method has excellent application value in the attitude of plant protection UAV. Keywords: quadrotor UAV, attitude control, plant protect, fuzzy adaptive PID, Simulink DOI: 10.25165/j.ijabe.20191206.5108 Citation: He Z H, Gao W L, He X K, Wang M J, Liu Y L, Song Y, et al. Fuzzy intelligent control method for improving flight attitude stability of plant protection quadrotor UAV. Int J Agric & Biol Eng, 2019; 12(6): 110–115.

UAV Positioning and Power Control for Two-Way Wireless Relaying

This paper considers an unmanned-aerial-vehicle-enabled (UAV-enabled) wireless network where a relay UAV is used for two-way communications between a ground base station (BS) and a set of distant user equipment (UE). The UAV adopts the amplify-and-forward strategy for two-way relaying over orthogonal frequency bands. The UAV positioning and the transmission powers of all nodes are jointly designed to maximize the sum rate of both uplink and downlink subject to transmission power constraints and the signal-to-noise ratio constraint on the UAV control channel. The formulated joint positioning and power control (JPPC) problem has an intricate expression of the sum rate due to two-way transmissions and is difficult to solve in general. We propose a novel concave surrogate function for the sum rate and employ the successive convex approximation (SCA) technique for obtaining a high-quality approximate solution. We show that the proposed surrogate function has a small curvature and enables a fast convergence of SCA. Furthermore, we develop a computationally efficient JPPC algorithm by applying the FISTA-type accelerated gradient projection (AGP) algorithm to solve the SCA problem as well as one of the projection subproblem, resulting in a double-loop AGP method. Simulation results show that the proposed JPPC algorithms are not only computationally efficient but also greatly outperform the heuristic approaches.

Cooperative Relative UAV Attitude Estimation Using DoA and RF Polarization

Relative unmanned aerial vehicle attitude is estimated using only on-board radio-frequency signaling. The method uses a direction-of-arrival (DoA) vector estimate to determine two degrees-of-freedom (DoFs), a polarimetric narrowband multiple-input multiple-output (MIMO) channel estimate to specify the third DoF to within a 180° ambiguity, and one of the several potential methods for ambiguity resolution. Simulation results demonstrate that the method accurately determines aircraft attitude, with errors proportional to DoA and MIMO channel estimate errors. This approach is useful for cooperative navigation when external navigation aids are not available, such as in GPS-denied environments.

Fixed-time integral-type sliding mode control for the quadrotor UAV attitude stabilization under actuator failures

In this work, two fixed-time fault-tolerant control schemes are developed to address the problem of attitude stabilization of a quadrotor unmanned aerial vehicle with external disturbances and actuator faults. By utilizing homogeneous approach, a nominal controller is first presented to ensure the fixed-time stability of the fault-free system. Then, a novel integral-type sliding mode and adaptive technique based fixed-time control law is proposed to restrain the disturbances and actuator faults. To further compensate the uncertainties and failures actively, a fixed-time observer based fixed-time fault-tolerant controller is proposed using the integral-type sliding mode control strategy as well. The proposed schemes are proved to be stable theoretically and strictly in the sense of Lyapunov. Finally, the fine performances and capabilities of the designed schemes are verified by numerical simulations.

A method of farmland 3D terrain reconstruction based on UAV sequence images

When the plant protection UAV on farmland spraying, the UAV need to always maintain a relative height of 1-2 m away from the crops to ensure uniform and efficient spraying of pesticides. At present, the commonly used high-fixing schemes, such as GPS, barometer, ultrasound and so on, there are some deficiencies. Aiming at this problem, a method of reconstructing 3D terrain of farmland by UAV electro-optical payload taking sequence images is proposed, which can achieve the goal of UAV height-fixing. Firstly, the feature points selection module is established. The SILC image segmentation algorithm is adopted to segment the image and the centroid points of all the superpixel blocks are calculated. Then, the corner points are extracted by using Harris corner detector and these two parts of points are mixed together as the feature points. Secondly, the feature point matching module based on SAD algorithm is proposed. Finally, a three-dimensional reconstruction algorithm according to monocular sequence images, UAV position, attitude and camera pointing angle is derived. The results of the experiments demonstrated that this method can quickly and effectively reconstruct farmland terrain, which can provide a data base for UAV high-fixing and has important application value.

Research on Obstacle Avoidance of Six-Rotor Aircraft Transmission Line Based on Image

Aiming at the problem that the real-time obstacle avoidance of power line by power line patrol UAV is difficult to achieve, this paper takes the image of power line captured by dynamic camera as the object, proposes an algorithm based on EDLines to identify the power line and realize the distance estimation with the power line according to the change of feature points. Firstly, the ED algorithm detects the edge of the image, and then a model is built according to the characteristics of the transmission line. The identified lines are screened and detected to reduce the interference of the complex background. At the same time, the linear growth method connects the discontinuous transmission lines. Finally, the feature points on the transmission line are extracted, the matching relationship between frames in the image sequence is used to track the feature points, and combined the position of UAV obtains the distance relationship, and the corresponding control strategy is proposed. Ordinary background and complex background images verifies the accuracy of the proposed algorithm, and the actual distance obtained is compared with the estimated distance. Experiments show that the matching accuracy and recognition speed of this algorithm are improved compared with Hough transform algorithm in the process of detecting transmission lines.

Self-organization based clustering scheme for FANETs using Glowworm Swarm Optimization

As UAVs in a Flying ad-hoc Network (FANET) are mobile in nature, thus results in frequently changing topology and creates the communication issues. For better networking to have efficient communication in FANETs, we propose self-organization based clustering scheme inspired by the behavioral study of glowworm swarm optimization (GSO) for cluster formation and management. The cluster head election and cluster formation take place based on connectivity with the ground control station along with luciferin value and residual energy of the UAVs. The cluster management mechanism uses behavioral study of GSO by updating the luciferin value based on the UAVs’ position. Furthermore we propose a mechanism for route selection based on the neighbor range, residual energy and position of UAV for efficient communication. The performance of the proposed SOCS is evaluated in terms of energy consumption, cluster building time, cluster lifetime and probability of delivery success with other existing bio-inspired clustering schemes.

Neural network-based adaptive sliding mode control design for position and attitude control of a quadrotor UAV

In this paper, a novel method is suggested for the position and attitude tracking control of a quadrotor UAV in the presence of parametric uncertainties and external disturbance. The proposed method combines neural network adaptive scheme with sliding mode control, which preserves the advantages of the two methods. Firstly, dynamic model of quadrotor is divided into two fully actuated and under actuated subsystems. Secondly, sliding mode controllers are corresponding designed for each subsystem, and their coefficients in sliding manifolds are adaptively tuned by the neural network method. In each section, using Lyapunov theory, stability of closed loop system is proven.Finally, the method is examined for a square path tracking and a maximum overshoot of 7.5133% and a settling time 5.6648 s are obtained. By comparing the results obtained through different methods, it is concluded that the proposed controller provides the following main advantages: (1) good transient and steady state behaviors, (2) insensitivity to parameter variations, (3) disturbance rejection capability, and (4) remarkable stability and performance robustness. Hence, for operational purposes in which the fast and accurate response are of crucial importance, using the neural network-based adaptive sliding mode control approach is recommended.

Multi station TDOA location method based on improved artificial bee colony algorithm

In modern battlefield, passive location technology based on multi-station TDOA of UAV platforms has important application value, but the traditional Newton iteration algorithm requires higher initial value of iteration, and the positioning accuracy needs to be improved. We have found a new algorithm in our research, which is based on ABC algorithm, and is also a method used in UAV positioning system. Comparing with the traditional algorithm, simulation results of this algorithm indicate that it can remarkably improve the precision of TDOA passive location.

Reinforcement Learning for UAV Attitude Control

Autopilot systems are typically composed of an “inner loop” providing stability and control, whereas an “outer loop” is responsible for mission-level objectives, such as way-point navigation. Autopilot systems for unmanned aerial vehicles are predominately implemented using Proportional-Integral-Derivative (PID) control systems, which have demonstrated exceptional performance in stable environments. However, more sophisticated control is required to operate in unpredictable and harsh environments. Intelligent flight control systems is an active area of research addressing limitations of PID control most recently through the use of reinforcement learning (RL), which has had success in other applications, such as robotics. Yet previous work has focused primarily on using RL at the mission-level controller. In this work, we investigate the performance and accuracy of the inner control loop providing attitude control when using intelligent flight control systems trained with state-of-the-art RL algorithms—Deep Deterministic Policy Gradient, Trust Region Policy Optimization, and Proximal Policy Optimization. To investigate these unknowns, we first developed an open source high-fidelity simulation environment to train a flight controller attitude control of a quadrotor through RL. We then used our environment to compare their performance to that of a PID controller to identify if using RL is appropriate in high-precision, time-critical flight control.

A visual/inertial integrated landing guidance method for UAV landing on the ship

This paper presents a visual/inertial integrated guidance method for UAV shipboard landing. The airborne vision system is utilized to track infrared cooperated targets on the ship and output their center coordinates in the image. Meanwhile, the attitude information of the UAV is obtained from airborne Inertial Measurement Unit (IMU). Extended Kalman Filter (EKF) is chosen to fuse the visual and inertial information. The UAV's position, attitude, and velocity relative to the runway and the ship motion information are estimated. The ship motion information is utilized to predict the position of the intended landing point at the touchdown moment through the Autoregressive algorithm. And the motion information of the UAV is used to calculate the deviation from the intended landing path. All the information is delivered to the flight control system to calculate control command. Simulation shows that the visual/inertial integrated landing guidance system achieves satisfied estimation and prediction results.

Fuzzy intelligent control method for improving flight attitude stability of plant protection quadrotor UAV

At present, the attitude control method of plant protection UAV is the classical PID control, but there are some imperfections in the PID control, such as the contradiction between speediness and overshoot, the weak anti-jamming ability and adaptability. The physical parameters of plant protection UAV are time-varying, and the airflow also interferes with it. The control ability of classical PID is limited, and its control parameters are fixed, and its anti-jamming ability and adaptability are not strong. Therefore, a fuzzy adaptive PID controller is proposed in this paper. Fuzzy logic control is used to optimize the control parameters of PID in order to improve the dynamic and static performance and adaptability of attitude control of plant protection UAV. In the process of research, the mathematical model of UAV is established firstly, then the fuzzy adaptive PID is designed, and then the simulation is carried out in Simulink. The simulation results show that the fuzzy adaptive PID controller has better dynamic and static control performance and adaptability than the traditional PID controller. Therefore, the proposed control method has excellent application value in the attitude of plant protection UAV. Keywords: quadrotor UAV, attitude control, plant protect, fuzzy adaptive PID, Simulink DOI: 10.25165/j.ijabe.20191206.5108 Citation: He Z H, Gao W L, He X K, Wang M J, Liu Y L, Song Y, et al. Fuzzy intelligent control method for improving flight attitude stability of plant protection quadrotor UAV. Int J Agric & Biol Eng, 2019; 12(6): 110–115.

Adaptive square-root unscented Kalman filter for position and pose prediction of UAV

Adaptive square-root unscented Kalman filter for position and pose prediction of UAV

Improvement of Shipboard Landing Performance of Shipborne UAV Using Multi-sensor Fusion

This paper propose a multi-sensor fusion positioning algorithm based on inertial navigation, gps, monocular vision and Ultra wide band (uwb) to ensure that the uav accurately and stably land on the target point in the near ship region. Establish the relative coordinate system relative position relationship between uav and ship. Converts the measurement data of various sensors into this coordinate system, and establishes the multi - sensor data fusion equation based on Joint Kalman Filter. It also performs the data validation test based on chi-square test of Mahalanobis Distance. Finally, we use AirSim 3d simulation system to simulate the algorithm. Experimental results show that the modified algorithm can calculate the uav's flight position in the near ship segment in real time, and reduce the impact of the increase of measurement error by a few sensors. It prevents the failure of fusion results, and has certain adaptability and robustness.

Safety Distance Visualization Tool for LTE-Based UAV Positioning in Urban Areas

Safety Distance Visualization Tool for LTE-Based UAV Positioning in Urban Areas

UAV quadrotor attitude control: An ADRC-EMC combined approach

This study presents an original approach to the design of a complete digital attitude control unit for a UAV quadrotor. The approach is developed within the framework of Active Disturbance Rejection Control (ADRC) and Embedded Model Control (EMC), two well-established control design methodologies, both based on the estimation of the disturbances/uncertainties affecting the plant to control, and on their online cancellation. The attitude control design carried out in this paper demonstrates the possibility of adopting a simple input–output model to control the UAV attitude, while relying on the disturbance rejector to bridge the gap between model and plant reality. The designed attitude control unit encompasses an attitude state predictor, a control law, and a model-based reference generator. A multi-step test strategy is proposed to assess the performance of this disturbance-rejection-based attitude controller. Consequently, the presented experimental results are obtained both using a high-fidelity numerical simulator and in several experimental tests, carried out either on a laboratory single-axial test-bench or in-flight. Finally, the control unit performance is benchmarked, in simulation, against a state-of-the-art high-performance UAV attitude controller.

Discontinuous High-Gain Observer in a Robust Control UAV Quadrotor: Real-Time Application for Watershed Monitoring

A control algorithm that is robust with respect to wind disturbances for a quadrotor UAV attitude dynamics is presented. The proposed approach consists of a high-gain observer based on a discontinuous technique. Such an algorithm is embedded on board the quadrotor. The high-gain observer estimates external disturbances such as wind and parameter uncertainties, and a control algorithm is designed to compensate these undesired effects. The observer design is based on Lyapunov stability theory; simulation results and experiments validate the nonlinear observer performance and robustness of the approach under windy conditions. Also, a photogrammetry survey was carried out to develop Digital Elevation Models in order to experimentally demonstrate the effectiveness of our approach. The accuracy of such models was compared and the performance improvement is demonstrated.

Stabilization of UAV using Delta Sigma Modulator based controller

The design development and control of Unmanned Aerial Vehicles (UAV’s) have stimulated great significance in the automatic control research for the past 2 decades. In specific, Quad rotor systems are a promising platform in the area of Uthe AV research, due to its simple in construction, maintenance, ability to hover, and their vertical takeoff and landing (VTOL) capability. The dynamics and control of quad rotor are highly nonlinear and under actuated so it is considered as a test-rig to verify any new proposed nonlinear control algorithm. Different control algorithms were proposed and implemented to stabilize the UAV attitude, and altitude. Adaptive control and navigation algorithms also implemented in UAV platform to ensure the maneuvering against the internal and external disturbances. The proposed research paper explains the implementation of the developed digital control algorithm Delta-sigma modulatoan r (DSM) based controller for UAV to enhance the robustness.

Measurement of Unmanned Aerial Vehicle Attitude Angles Based on a Single Captured Image.

The limited load capacity and power resources of small-scale fixed-wing drones mean that it is difficult to employ internal high-precision inertial navigation devices to assist with the landing procedure. As an alternative, this paper proposes an attitude measurement system based on a monocular camera. The attitude angles are obtained from a single captured image containing five coded landmark points using the radial constraint method and three-dimensional coordinate transformations. The landing procedure is simulated for pitch angles from −15 to −40, roll angles from −15 to +15 and yaw angles from −15 to +15. For roll and pitch angles of approximately 0 and −25, respectively, the accuracy of the method reaches 0.01 and 0.04. This UAV attitude measurement system obtains an attitude angle by a single captured image, which has great potential for assisting with the landing of small-scale fixed-wing UAVs.

Study on Active Fault Tolerant Flight Control Systems for Quadrotor UAV with Actuator Failures

The rotor blades' fatigue fracture of Quadrotor UAV easily causes the instability or even crash of the UAV due to high-load and long-endurance flight missions. Under this circumstances, an active fault-tolerant flight controller of Quadrotor UAV based on integral sliding mode is proposed to strengthen the fault-tolerant capability of UAV's attitude and position. First of all, nonlinear mathematical model of quadrotor UAV with actuator failures is derived by kinematics and dynamics analysis. Secondly, a fault observer is constructed to determine when the actuator failure will occur, subsequently the UAV's attitude and position flight controllers are compensated using integral sliding mode control. The digital simulation and flight test shows that the controller has powerful fault-tolerant capacity and preferable dynamic and static characteristics which can stabilize the attitude and position responses of UAV when partial failure of single blade occurs.