Fixed-wing Unmanned Aerial Vehicle Research Articles

Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments.

Attitude control of fixed-wing unmanned aerial vehicles (UAVs) is a difficult control problem in part due to uncertain nonlinear dynamics, actuator constraints, and coupled longitudinal and lateral motions. Current state-of-the-art autopilots are based on linear control and are thus limited in their effectiveness and performance. Gls drl is a machine learning method to automatically discover optimal control laws through interaction with the controlled system that can handle complex nonlinear dynamics. We show in this article that deep reinforcement learning (DRL) can successfully learn to perform attitude control of a fixed-wing UAV operating directly on the original nonlinear dynamics, requiring as little as 3 min of flight data. We initially train our model in a simulation environment and then deploy the learned controller on the UAV in flight tests, demonstrating comparable performance to the state-of-the-art ArduPlane proportional-integral-derivative (PID) attitude controller with no further online learning required. Learning with significant actuation delay and diversified simulated dynamics were found to be crucial for successful transfer to control of the real UAV. In addition to a qualitative comparison with the ArduPlane autopilot, we present a quantitative assessment based on linear analysis to better understand the learning controller's behavior.

Reinforcement Learning-Based Fractional-Order Adaptive Fault-Tolerant Formation Control of Networked Fixed-Wing UAVs With Prescribed Performance.

This article investigates the fault-tolerant formation control (FTFC) problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults. To constrain the distributed tracking errors of follower UAVs with respect to neighboring UAVs in the presence of faults, finite-time prescribed performance functions (PPFs) are developed to transform the distributed tracking errors into a new set of errors by incorporating user-specified transient and steady-state requirements. Then, the critic neural networks (NNs) are developed to learn the long-term performance indices, which are used to evaluate the distributed tracking performance. Based on the generated critic NNs, actor NNs are designed to learn the unknown nonlinear terms. Moreover, to compensate for the reinforcement learning errors of actor-critic NNs, nonlinear disturbance observers (DOs) with skillfully constructed auxiliary learning errors are developed to facilitate the FTFC design. Furthermore, by using the Lyapunov stability analysis, it is shown that all follower UAVs can track the leader UAV with predesigned offsets, and the distributed tracking errors are finite-time convergent. Finally, comparative simulation results are presented to show the effectiveness of the proposed control scheme.

ASSESSMENT OF SPATIAL DATA OBTAINED BY MEANS OF THE USE OF UNMANNED AERIAL VEHICLE (UAV)

At present, in industry or research, engineering projects demand continuous spatial data from the Earth's surface. Depending on the required level of detail, collecting such data through traditional methods like topographic surveying can be prohibitively expensive. Moreover, these methods often involve extended timeframes and challenges in accessing difficult terrains, such as steep slopes. The use of an unmanned aerial vehicle (UAV), commonly referred to as a drone, equipped with sensors like photographic cameras, offers a means of consistently acquiring very high spatial resolution data from the Earth's surface. The incorporation of Ground Control Points (GCPs) ensures the data 's spatial accuracy. Photogrammetric process like structure from motion (SfM), generate various spatial products, including orthophotos and point clouds. This research centers on the application of a fixed-wing UAV to survey a 120 ha area to generate spatial data. The study demonstrates the high precision of the derived spatial data, validated against ground true data, using a flight height of 100 m over the above ground with an 80% overlap. The results include the generation of an orthophoto, a Digital Terrain Model (DTM), and a Digital Surface Model (DSM), with both terrain models derived from the point cloud.

Statistical A2G Coverage Characteristics in Dynamic Fixed-Wing UAV Networks

Statistical A2G Coverage Characteristics in Dynamic Fixed-Wing UAV Networks

Event-Based Guidance and Incremental Control with Application to Fixed-wing Unmanned Aerial Vehicle Perched Landing Maneuvers

Considering the nonlinearity and unknown dynamics of fixed-wing unmanned aerial vehicles in perched landing maneuvers, an event-based online guidance and incremental control scheme is proposed. The guidance trajectory for perched landing must be dynamically feasible therefore an event-based trapezoidal collocation point optimization method is proposed. Introduction of the triggering mechanism for the rational use of computing resources to improve PL accuracy. Furthermore, a filter-based incremental nonlinear dynamic inverse (F-INDI) control with state transformation is proposed to achieve robust trajectory tracking under high angle of attack (AOA). The F-INDI uses low-pass filters to obtain incremental dynamics of the system, which simplifies the design process. The state transformation strategy is to convert the flight-path angle, AOA and velocity into two composite dynamics, which avoids the sign reversal problem of control gain under high AOA. The stability analysis shows that the original states can be controlled only by controlling the composite state. Simulation results show that the proposed scheme achieves high perched landing accuracy and a reliable trajectory tracking control.

Path Planning for Fixed-Wing Unmanned Aerial Vehicles: An Integrated Approach with Theta* and Clothoids

Unmanned Aerial Vehicles (UAVs) have emerged as a compelling alternative to manned operations, offering the capability to navigate hazardous environments without risks for human operators. Despite their potential, optimizing UAV missions in complex and unstructured environments remains a pivotal challenge. Path planning becomes a crucial aspect to increase mission efficiency, although it is inherently complex due to various factors such as obstacles, no-fly zones, non-cooperative aircraft, and flight mechanics limitations. This paper presents a path-planning technique for fixed-wing unmanned aerial vehicles (UAVs) based on the Theta* algorithm. The approach introduces innovative features, such as the use of Euler spiral, or clothoids, to serve as connection arcs between nodes, mitigating trajectory discontinuities. The design of clothoids can be linked to the aircraft performance model, establishing a connection between curvature constraints and the specific characteristics of the vehicle. Furthermore, to lower the computational burden, the implementation of an adaptive exploration distance and a vision cone was considered, reducing the number of explored solutions. This methodology ensures a seamless and optimized flight path for fixed-wing UAVs operating in static environments, showcasing a noteworthy improvement in trajectory smoothness. The proposed methodology has been numerically evaluated in several complex test cases as well as in a real urban scenario to prove its effectiveness.

Optimization of multi-target continuous dynamic trajectory for unmanned aerial vehicles

In the problem of trajectory optimization for unmanned aerial vehicles (UAVs) passing through multiple target points, continuous velocity and acceleration in the process of optimization are essential for maintaining a feasible trajectory. This paper proposes a multi-segment holistic Bezier shaping method (MHBSM) for dynamic trajectory optimization. The MHBSM not only achieves holistic optimization of UAVs passing through a multi-target trajectory but also ensures high-order continuity at the junctions of trajectory segments. Unlike methods that generate non-dynamic trajectories by connecting arcs and straight lines (such as Dubins curves) without considering speed changes, MHBSM optimizes the trajectory while taking into account the dynamics of UAVs. The method optimizes the differential flat output space of a fixed-wing UAV dynamic model and transforms the dynamic optimization problem into a three-dimensional position optimization problem. The problem is then further transformed into an optimization of control points using the Bezier curve. The resulting trajectory satisfies the dynamic constraints. In comparison to the Gaussian pseudospectral method (GPM), MHBSM achieves similar optimization results with only a 2.58% difference, while requiring approximately 1.36% of the computational time on average. The MHBSM demonstrates excellent convergence performance while satisfying the constraints of continuous acceleration and obstacle avoidance.

Coverage Path Planning of UAV Based on Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization

In contrast to rotorcraft, fixed-wing unmanned aerial vehicles (UAVs) encounter a unique challenge in path planning due to the necessity of accounting for the turning radius constraint. This research focuses on coverage path planning, aiming to determine optimal trajectories for fixed-wing UAVs to thoroughly explore designated areas of interest. To address this challenge, the Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization algorithm (LP-FCMPIO) is proposed. Initially considering the turning radius constraint, a linear-programming-based model for fixed-wing UAV coverage path planning is established. Subsequently, to partition multiple areas effectively, an improved fuzzy clustering algorithm is introduced. Employing the pigeon-inspired optimization algorithm as the final step, an approximately optimal solution is sought. Simulation experiments demonstrate that the LP-FCMPIO, when compared to traditional FCM, achieves a more balanced clustering effect. Additionally, in contrast to traditional PIO, the planned flight paths display improved coverage of task areas, with an approximately 27.5% reduction in the number of large maneuvers. The experimental results provide validation for the effectiveness of the proposed algorithm.

Refined Fractional-Order Fault-Tolerant Coordinated Tracking Control of Networked Fixed-Wing UAVs Against Faults and Communication Delays via Double Recurrent Perturbation FNNs.

This article investigates the fault-tolerant coordinated tracking control problem for networked fixed-wing unmanned aerial vehicles (UAVs) against faults and communication delays. By supplementing the commonly used Gaussian functions in the fuzzy neural networks (FNNs) with sine-cosine functions and constructing two kinds of recurrent loops within the FNN architecture, double recurrent perturbation FNNs are cleverly designed to learn the unknown terms containing faults and uncertainties. Then, adaptive laws are designed for double recurrent perturbation FNNs. Moreover, by assimilating fractional-order calculus into the sliding-mode surfaces and the control signals, refined transient-state and steady-state adjustment performances can be obtained. It is shown by Lyapunov stability analysis that all fixed-wing UAVs can coordinately track their desired trajectories and the tracking errors are uniformly ultimately bounded. Comparative simulation results are provided to show the effectiveness of the proposed control strategy.

Developing a model for unmanned aerial vehicle with fixed-wing using 3D-map exploring rapidly random tree technique

While the motion planning algorithms consider the obstacles that were known in the map, it is possible to use obstacle avoidance algorithms to take over and send commands to theunmanned aerial vehicle (UAV), when there is an unknown obstacle on the way. The rapidly random tree (RRT) algorithm is used to plan paths for a quad-copter or a fixed-wing UAV. This work develops a model for UAV with fixed-wing using a 3D map exploring the RRT technique. The first step is to obtain a 3D occupancy map from the map data stored in the UAV city to provide a map with some pre-generated obstacles. The contribution of this work is to use RRT planning for 3D state space, where the motion segment or motion primitive connecting the two consecutive states should be defined in a 3D space while satisfying the motion constraints of a UAV. The simulation includes setting up a 3D map, providing the starting and destination pose, planning a way using RRT and 3D Dubins moving primitives, smoothing the acquired trajectory, and simulating the UAV flight. The results obtained demonstrate that the smoothed-generated waypoints significantly improved tracking in general with shorter paths.

Investigation of Empennage Location Effect to the Unmanned Aerial Vehicle (UAV) Structure Characteristic

Over the past few decades, unmanned aerial vehicles, commonly known as UAVs, have been widely used in a number of consumer and military applications, such as surveillance, tracking, monitoring, and aerial photography. Fixed-wing and rotary UAVs are the two primary categories in UAV. Interestingly, the hybridization of fixed-wing and rotary UAV gives better performance in terms of energy consumption and the needs of runaway. Designing new hybrid fixed wing-rotary UAV or hybrid vertical take-off and landing (VTOL) is challenging especially to identify the critical location in the UAV and material selection. Therefore, the objective of this research is to study the effect of empennage location and material selection on the structural strength of a hybrid VTOL UAV. The SolidWorks software was employed to design a 3D model of the UAV with different empennage locations, as well as perform a simulation of the structural strength of fibre glass, carbon fibre and kenaf for the hybrid VTOL UAV. The simulation analysis presents stress (Von Mises). The results show that the fibre glass (4.342 N/m2) at top empennage gives the best performance as compared to other parameters. In conclusion, this study is necessary to give a better picture of structural strength of composite materials and best design location in hybrid VTOL UAV for future research.

A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing.

This paper introduces a fuzzy logic-based autonomous ship deck landing system for fixed-wing unmanned aerial vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship deck landing model is based on a fuzzy logic system (FLS), which comprises three interconnected subsystems (speed, lateral motion, and altitude components). Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship deck landings. Specifically, the FLS receives five inputs: the range from the deck, the relative wind direction and speed, the airspeed, and the UAV's flight altitude. The FLS outputs provide data on the speed of the UAV relative to the ship's velocity, the bank angle (BA), and the angle of descent (AOD) of the UAV. The performance of the designed intelligent ship deck landing system was evaluated using the standard configuration of MATLAB Fuzzy Toolbox.

An analytical analysis of vibrational energy harvesting using piezoelectric energy harvester in a fixed wing unmanned aerial vehicle

Fixed-wing unmanned aerial vehicles (UAVs) in the modern day have widespread applications in various reconnaissance, agriculture, military, and civilian operations. Their wide domain of application has placed the development of drone technology at the forefront. The energy harvesting in UAVs seems promising since it not only has the ability to lengthen flying time but also guarantees that no appreciable weight is added. The current article explores the concept of placing piezoelectric material patches on the UAV wing in order to extract energy from the vibration of the wing. This study uses a UAV's fixed NACA 2412 wing to harvest vibration energy by finding the location and placing a piezoelectric patch where it will produce the most voltage. The mode shapes generated are used to get the appropriate location to place the piezoelectric patch. The fixed-wing of a UAV with a piezo patch placed is subjected to a harmonically varying pressure load of 213.934 Pa and the behavior of the wing and its frequency response is analyzed in terms of displacement and voltage as output. The maximum voltage produced by the single piezoelectric patch is found about 0.11 V at the first mode shape of the wing. Embedding and increasing the patch at a location of high displacements help to harvest more energy and can be utilized for other purposes.

Minimizing Fuel Consumption for Surveillance Unmanned Aerial Vehicles Using Parallel Particle Swarm Optimization.

This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton's equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67× compared to a sequential implementation on the CPU.

Diagnosis of Power Switch Faults in Three-Phase Permanent Magnet Synchronous Motors via Current-Signature Technique

The paper deals with the development of a model-based current-signature algorithm for the detection and isolation of power switch faults in three-phase Permanent Magnet Synchronous Motors (PMSMs). The algorithm, by elaborating the motor currents feedbacks, reconstructs the current phasor trajectories in the Clarke plane through elliptical fittings, up to detecting and isolating the fault depending on the characteristics of the signature deviation from the nominal one. As a rough approximation, as typically proposed in the literature, the fault of one out of six power switches implies that, at constant speed operation, the phasor trajectory deviates from the nominal circular path up to a semi-circular “D-shape” signature, the inclination of which depends on the failed converter leg. However, this evolution can significantly deviate in practical cases, due to the dynamics related to the transition of motor phase connections from failed to active switches. The study demonstrates that an online ellipse fitting of the current signature can be effective for diagnosis, through correlating the ellipse centre to the location of the failed switch. The performances of the proposed monitoring technique are here assessed via the nonlinear simulation of a PMSM employed for the propulsion of a lightweight fixed-wing Unmanned Aerial Vehicle (UAV), by quantifying the fault latencies and the related transients.

Roto-translation invariant formation of fixed-wing UAVs in 3D: Feasibility and control

This paper investigates the formation of fixed-wing UAVs in 3D, which communicate via a directed acyclic graph. Different from common formation problems, we consider the roto-translation invariant (RTI) formation, where the “roto-translation” refers to a rigid-body motion obtained by composing rotation and translation. Besides, the fixed-wing UAV is modeled by a 3-D nonholonomic constrained rigid body instead of a particle agent. The main results of this paper include proposing the formation feasibility conditions and designing the formation controller. Firstly, we define the RTI formation and propose the conditions to guarantee that the formations are feasible for the fixed-wing UAVs under the nonholonomic and input saturation constraints. Secondly, given feasible formations, we design a formation controller by introducing a virtual leader and employing the compensation of rotation, followed by proving the stability of the closed-loop system. Finally, simulation examples are presented to verify the theoretical results.

Adaptive PID Control for Autopilot Design of Small Fixed Wing UAVs

The development of a flight control system for the class of small fixed wing Unmanned Aerial Vehicle (UAV) imposes a challenge to the designers to achieve acceptable and stable performance characteristics across the defined flight envelope. This paper focuses on developing a dynamic model of the Aerosonde UAV, incorporating it with an adaptive Proportional, Integral and Derivative (PID) control system for designing both lateral and longitudinal autopilots. The PID gains for each control loop are self-tuned based on an analytical approach, making sure that the desired command value is achieved at each time interval as closely as possible. To check the robust nature of the designed control system in the presence of external wind disturbance, a Dryden wind gust model was developed, and the entire simulation results were carried out in MATLAB®/Simulink environment. The proposed adaptive PID controller proves the effectiveness and robustness of the autopilot control system.

Adaptive Neural Network Control for Fixed-Wing UAV With Disturbance Observer Under Switching Disturbance.

The adaptive neural network (NN) control for the fixed-wing unmanned aerial vehicle (FUAV) under the unmodeled dynamics and the time-varying switching disturbance (TVSD) is investigated in this article. To better describe the TVSD induced by the change in the flight area of the FUAV, a switching augmented model (SAM) based on the known information about the TVSD is proposed first. The parameter adaptation technique is used to estimate the related TVSD. Thereafter, the time-varying disturbance that cannot be described by the SAM is estimated by the disturbance observer (DO). The radial basis function NN (RBFNN) is adopted to approximate the unknown unmodeled dynamics. The coupling terms derived from the co-design of DO and the parameter adaptation (PA) are separated by some inequality techniques. Then, the separated unknown terms are eliminated by designing the parameters of the controller and that of the adaptive law. The separated known terms are tackled by adding robust control terms to the controller. In addition, to improve the estimation performance for the TVSD and RBFNN, the auxiliary system in the DO form is designed. Sufficient stable conditions about the closed-loop switched system (CLSS) are obtained with and without the inequality about the switching times. Finally, an illustrative example is given to show the feasibility and advantage of the proposed control strategy by the attitude model of the FUAV.

A Distributed Framework for Persistent Wildfire Monitoring with Fixed Wing UAVs

A Distributed Framework for Persistent Wildfire Monitoring with Fixed Wing UAVs

Self-Triggered Distributed Formation Control of Fixed-Wing Unmanned Aerial Vehicles Subject to Velocity and Overload Constraints

Self-Triggered Distributed Formation Control of Fixed-Wing Unmanned Aerial Vehicles Subject to Velocity and Overload Constraints