Rotor Unmanned Aerial Vehicle Research Articles

Fault diagnosis of drone motors driven by current signal data with few samples

Multi rotor unmanned aerial vehicles (UAVs) are extensively utilized across various domains, and the motor constitutes a pivotal element in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the imperative need for comprehensive research on fault diagnosis in UAV motors to ensure the stable and reliable execution of flight tasks. This study focuses on quadrotor UAVs as the research subject and devises targeted fault simulation experiments based on the structural features and operational characteristics of the DC brushless motor used in quadrotor UAVs, specifically examining the stator, rotor, and bearings. To address challenges related to the UAV’s own loads, limited space for redundant parts, and the high cost and difficulty associated with installing sensors for traditional fault diagnostic signals such as vibration and temperature, this study opts to use current signals as a substitute. This approach resolves the issue of challenging data collection for UAVs and investigates a current signal based fault diagnosis method for UAV motors. Lastly, in response to the limited training samples available for fault data due to the UAV’s highly sensitive characteristics regarding the health status of its components and flight stability, traditional machine learning and deep learning methods encounter difficulties in identifying representative features with a small number of training samples, leading to the risk of overfitting and reduced model accuracy in fault diagnosis. To overcome this challenge, we propose a hybrid neural network fault diagnosis model that incorporates a width learning system and a convolutional neural network (CNN). The width learning system eliminates temporal characteristics from the original current signal, capturing more comprehensive and representative sample features in the width feature space. Subsequently, the CNN is employed for feature extraction and classification tasks. In empirical small sample fault diagnosis experiments using current signal data for UAV motors, our proposed model outperforms other models used for comparison.

Application of a centrifugal disc fertilizer spreading system for UAVs in rice fields

Unmanned aerial vehicle (UAV) granular fertilizer spreading technology has been gradually applied in agricultural production. However, in the process of spreading operation, the actual influence effect of each factor in field operation is still unclear. Based on the self-developed UAV fertilizer spreading system, this paper explores the effects of three factors, the baffle retraction (B), spreading disc speed (D), and UAV flight altitude (H), on the granular fertilizer spreading effect in the actual field scenarios through the orthogonal test and taking the coefficient of variation (Cv) and relative error of fertilizer application rate (λ) as the evaluation indexes. The results showed that the optimal factor level combination of Cv was 11.23 % for BbDbHa (the baffle retraction is 6 %, spreading disc speed is 600r/min, and UAV flight height is 1.5 m) at UAV flight speed of 2 m/s. The best factor level combination for λ was BbDbHb of 7.99 % (the baffle retraction is 6 %, spreading disc speed is 600r/min, and UAV flight height is 2 m). In addition, by analysing the influence of the weather and the vortex of the rice canopy on the actual spreading effect, it was found that the weather has less influence on the spreading effect of this system, while the vortex caused by the airflow of the UAV rotor has a certain influence on the spreading effect, which is also relatively easy to ignore in fertilizer spreading operations. The results of the study can be used to explore the operational effects of actual fertilizer application by UAVs in rice field, which will help promote the development of UAV spreading technology and provide a reference for precision fertilizer application through agricultural aviation.

Experimental and numerical study of different wing number and flapping-rotation decomposition of a flapping wing rotor unmanned aerial vehicle

The flapping wing rotor (FWR) is a novel aerial vehicle that combines the aerodynamic benefits of both a flapping wing and a rotary wing. By utilizing the passive rotation effect resulting from the flexible deformation of the center symmetric flapping wing, the FWR can enhance its lift force. However, previous research has neglected to explore the mechanism behind the flapping-rotation motion of a flyable FWR, which elucidates its lift advantage compared to conventional flapping motion. Additionally, the impact of varying wing number on the flapping-rotation motion and performance of the FWR has not been taken into consideration. Therefore, it is imperative to conduct an experimental analysis to ascertain the impact of flapping-rotation decomposition and varying wing quantities on FWR. In this study, our prior vehicle design, which exhibited consistent stable hovering and maneuvering capabilities, is employed to construct the flapping wing rotor experiment system. Through this unique experimental system, the effects of flapping-rotation decomposition and different wing quantities on FWR are individually investigated. Additionally, computational fluid dynamics simulation is utilized as an auxiliary and supplementary approach to analyze the aerodynamic characteristics of the flapping-rotation motion. The result proves that the stable flapping-rotation motion does produce a more significant lift increase than the normal flapping motions. Under the premise of stable flapping-rotation motion, more wings will not only produce more lift but also require more driving power. The interactions between the wings also affect the flapping-rotation motion.

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.

Extracting Micro-Doppler Features from Multi-Rotor Unmanned Aerial Vehicles Using Time-Frequency Rotation Domain Concentration

This study addresses the growing concern over the impact of small unmanned aerial vehicles (UAVs), particularly rotor UAVs, on air traffic order and public safety. We propose a novel method for micro-Doppler feature extraction in multi-rotor UAVs within the time-frequency transform domain. Utilizing competitive learning particle swarm optimization (CLPSO), our approach divides population dynamics into three subgroups, each employing unique optimization mechanisms to enhance local search capabilities. This method overcomes limitations in traditional Particle Swarm Optimization (PSO) algorithms, specifically in achieving global optimal solutions. Our simulation and experimental results demonstrate the method’s efficiency and accuracy in extracting micro-Doppler features of rotary-wing UAVs. This advancement not only facilitates UAV detection and identification but also significantly contributes to the fields of UAV monitoring and airspace security.

UAV Atmosphere Sounding for Rocket Launch Support.

One of the crucial branches of activity at the Łukasiewicz Research Network-Institute of Aviation is developing a suborbital rocket vehicle capable of launching small payloads beyond the Earth's atmosphere, reaching over 100 km in altitude. Ensuring safety is a primary concern, particularly given the finite flight zone and impact area. Crucial to safety analysis is the wind profile, especially in the very first seconds of a flight, when rocket velocity is of the same order as the wind speed. Traditional near-ground wind data sources, ranging from wind towers to numerical models of the atmosphere, have limitations. Wind towers are costly and unfeasible at many test ranges used for launches, while numerical modeling may not reflect the specific ground profile near the launcher due to their large cell size (2 to +10 km). Meteorological balloons are not favorable for such measurements as they aim to provide the launch operator with a wind profile at high altitudes, and are launched only 1-2 times per flight attempt. Our study sought to prototype a wind measurement system designed to acquire near-ground wind profile data. It focuses on measuring wind direction and speed at near-ground altitudes with higher flight frequency, offering data on demand shortly before launch to help ensure safety. This atmosphere sounding system consists of an Unmanned Aerial Vehicle (UAV) equipped with an onboard ultrasonic wind sensor. Some reports in the literature have discussed the possibility of using UAV-borne anemometers, but the topic of measurement errors introduced by placing the anemometer onboard an UAV remains under studied. Limited research in this area underlines the need for experimental validation of design choices-for specific types of UAVs, anemometers, and mounting. This paper presents a literature review, a detailed overview of the prototyped system, and flight test results in both natural (outdoor) and controlled (indoor, no wind) conditions. Data from the UAV system's anemometer was benchmarked against a stationary reference weather station, in order to examine the influence of the UAV's rotor on the anemometer readings. Our findings show a wind speed Root Mean Square Error (RMSE) of 5 m/s and a directional RMSE of below 5.3° (both averaged for 1 min). The results were also compared with similar UAV-based wind measurements. The prototyped system was successfully used in a suborbital rocket launch campaign, thus demonstrating the feasibility of integrating UAVs with dedicated sensors for performing regular meteorological measurements in automatic mode.

FANET management process simulation at the deployment and operation stage

The object of the study is the process of managing the air network of air communication platforms of the FANET class (Flying Ad-Hoc Network), which is a component of the ground-air communication network, and which is performed on rotary unmanned aerial vehicles (UAVs) of the mini class, at the stage of deployment and operational management. The scientific research is aimed at the managing process formalization of aerial communication platforms of the air communication network in the implementation of two classes of management tasks – the class of traffic management tasks and the class of communication tasks. The analysis of this subject area showed that the management tasks at the stage of deployment and operational management of the air subnet are a multi-parameter optimization task and require the formation of control solutions at the OSI physical, channel and network levels, open systems interaction model. Tasks related to the adaptive management of radio coverage in zones (geographic areas of the area), including the clustering of terrestrial subscribers (communication nodes), were not considered, and relate to processes at the transport and application levels. At the same time, the article shows the mathematical apparatus of the approach to the compensation of the deviations of the trajectory of an unmanned aerial vehicle (UAV) in the conditions of a directional obstacle, which will allow the formation of control solutions for adaptive control, directional patterns at the output of the transmission path. Such compensation is carried out using methods of algorithmic exchange of probes (messages) between the mobile base station and communication platforms with a certain periodicity – solutions at the channel and network levels, as well as the use of Multi User MIMO technologies. This technology allows for information exchange with several client devices at the same time, and not sequentially, sending probes to several spacecraft on one channel, using several transmitting and receiving antennas, and the calculation of channel coefficients allows to estimate the azimuthal angle of deviation and the angle of elevation.

Fast fixed-time three-dimensional terminal guidance with non-concave trajectory constraint

Focusing on the non-concave trajectory constraint, a sliding-mode-based nonsingular feedback fast fixed-time three-dimensional terminal guidance of rotor unmanned aerial vehicle landing, planetary landing and spacecraft rendezvous and docking terminal phase with external disturbance is investigated in this paper. Firstly, a fixed-time observer based on real-time differentiator is developed to compensate for the external disturbance, whose estimation error can converge to zero after a time independent of the initial state. Then, a sliding surface ensuring fixed-time convergence is presented. This sliding surface can guarantee that the vehicle achieves a non-concave trajectory, which is better for avoiding collision and maintaining the visibility of the landing site or docking port. Next, the nonsingular guidance ensuring the fixed-time convergence of the sliding surface is proposed, which is continuous and chatter free. At last, three numerical simulations of Mars landing are performed to validate the effectiveness and correctness of the designed scheme.

A Sub-Aperture Overlapping Imaging Method for Circular Synthetic Aperture Radar Carried by a Small Rotor Unmanned Aerial Vehicle.

Circular synthetic aperture radar (CSAR) can obtain higher image resolution and more target information using 360° observation of the target. Due to the anisotropy of target scattering characteristics in the actual scene, the sub-aperture imaging method is usually used for CSAR imaging. However, the uniformly divided overlapping sub-aperture CSAR imaging algorithm only considers phase compensation, ignoring the effect of target scattering characteristics on echo amplitude. In CSAR imaging scenarios carried by small rotor unmanned aerial vehicles (SRUAVs), the size of the observed scene cannot be ignored compared to the distance between the target and the antenna and the effect of the anisotropy of the target scattered energy on the echo amplitude should be considered. In this paper, a sub-aperture CSAR imaging method based on adaptive overlapping sub-aperture is proposed. First, the boundary points of the sub-aperture are determined by analyzing the correlation coefficient and the variation coefficient of the energy function. Next, the overlapping sub-aperture division schemes are automatically generated by screening and combining the boundary points. The sub-aperture images are then generated by a Back Projection (BP) algorithm. Finally, sub-aperture image registration and incoherent superposition are used to generate the final CSAR image. Verified by the CSAR field echo data, the proposed method can realize imaging of the original echo data without the Inertial Navigation System (INS) and Global Positioning System (GPS) observation data. Compared with the CSAR full-aperture BP imaging algorithm, the entropy of the image generated by the proposed method increased by 66.77%. Compared with the sub-aperture CSAR imaging algorithm, the entropy of the image generated by the proposed method was improved by 11.12%, retaining more details of the target, improving the target contour features, and enhancing the focusing effect.

End-Cloud Collaboration Navigation Planning Method for Unmanned Aerial Vehicles Used in Small Areas.

Unmanned aerial vehicle (UAV) collaboration has become the main means of indoor and outdoor regional search, railway patrol, and other tasks, and navigation planning is one of the key, albeit difficult, technologies. The purpose of UAV navigation planning is to plan reasonable trajectories for UAVs to avoid obstacles and reach the task area. Essentially, it is a complex optimization problem that requires the use of navigation planning algorithms to search for path-point solutions that meet the requirements under the guide of objective functions and constraints. At present, there are autonomous navigation modes of UAVs relying on airborne sensors and navigation control modes of UAVs relying on ground control stations (GCSs). However, due to the limitation of airborne processor computing power, and background command and control communication delay, a navigation planning method that takes into account accuracy and timeliness is needed. First, the navigation planning architecture of UAVs of end-cloud collaboration was designed. Then, the background cloud navigation planning algorithm of UAVs was designed based on the improved particle swarm optimization (PSO). Next, the navigation control algorithm of the UAV terminals was designed based on the multi-objective hybrid swarm intelligent optimization algorithm. Finally, the computer simulation and actual indoor-environment flight test based on small rotor UAVs were designed and conducted. The results showed that the proposed method is correct and feasible, and can improve the effectiveness and efficiency of navigation planning of UAVs.

Modelling, control and simulation of a single rotor UAV with swashplateless torque modulation

Single rotor-base unmanned aerial vehicles (UAVs) represent the most minimalistic design to implement an aerial vehicle. We describe here a mono-rotor UAV, also named mono-spinner, that uses torque modulation (also called swashplateless mechanism) to provide roll and pitch control without the need of a complicated swashplate mechanism. A full non-linear model of the mono-spinner based on the Euler-Poincaré theoretical framework is given including a description of the structure of the drone and its rotor control system. Mechanical properties, kinetic energy and all of the external forces of interest to derive the equations of motion are described. A novel, Lyapunov-stable, quaternion-based nonlinear control law based on the decomposition of the orientation quaternion into a spin component and a reduced orientation component is provided. Finally, simulation results based on parameters measured from a real implementation of a mono-spinner are produced.

A CSAR 3D Imaging Method Suitable for Edge Computation

Due to the large amount of CSAR echo data carried by UAVs, either the original echo data need to be transmitted to the ground for processing or post-processing must be implemented after the flight. Therefore, it is difficult to use edge computing power such as a UAV onboard computer to implement image processing. The commonly used back projection (BP) algorithm and corresponding improved imaging algorithms require a large amount of computation and have slow imaging speed, which further limits the realization of CSAR 3D imaging on edge nodes. To improve the speed of CSAR 3D imaging, this paper proposes a CSAR 3D imaging method suitable for edge computation. Firstly, the improved Crazy Climber algorithm extracts sine track ridges that represent the amplitude changes in the range-compressed echo. Secondly, two-dimensional (2D) profiles of CSAR with different heights are obtained via inverse Radon transform (IRT). Thirdly, the Hough transform is used to extract the intersection points of the defocused circle along the heights in the X and Y directions. Finally, 3D point cloud extraction is completed through voting screening. In this paper, image detection methods such as ridge extraction, IRT, and Hough transform replace the phase compensation processing of the traditional BP 3D imaging method, which significantly reduces the time of CSAR 3D imaging. The correctness and effectiveness of the proposed method are verified by the 3D imaging results for the simulated data of ideal targets and X-band CSAR outfield flight raw data carried by a small rotor unmanned aerial vehicle (SRUAV). The proposed method provides a new direction for the fast 3D imaging of edge nodes, such as aircraft and small ground terminals. The image can be directly transmitted, which can improve the information transmission efficiency of the Internet of Things (IoT).

Application of aeromagnetic survey to mineral exploration of Jinping, Yunnan, China by using multirotor UAV

Jingping, Yunnan, China, is an unfriendly area for ground survey due to the high altitude, rough terrain and dense vegetation. To detect the concealed magnetite ore bodies in this area, the aeromagnetic survey in a 7.8 km2 region was carried out. The field experiments were conducted to investigate the source and level of the noise. Then, the proton magnetometer was placed 3 m under the multirotor unmanned aerial vehicle (UAV) to decrease the static interference. The inverse distance weighting interpolation algorithm with a nonlinear filter was proposed to suppress the noise and dynamic interference caused by the strong wind. The results show that these methods can alleviate the interference of the UAV rotor and strong wind effectively. The patterns of the data from our aeromagnetic survey agree well with the horizontal distribution of the magnetic strata deduced from the geological background. Furthermore, the concealed mafic magmatic rocks and the titanium magnetite inferred from the aeromagnetic survey are confirmed by 4 drill logs in the study area, which supports the validity of the UAV aeromagnetic survey.

A review analysis of tilt rotor unmanned aerial vehicles

TRUAVs (Tilt Rotor Unmanned Aerial Vehicles) are a new technology that is becoming more and more popular in today's modern world for a range of uses, including agricultural, civil engineering, target tracking, search and salvage operations, and surveillance. Such aerial vehicles were created as a solution to a number of issues, such as the lack of runways in remote and heavily forested areas. The TRUAV is a hybrid aircraft that presents the advantages of both conventional aero plane and helicopter technology in a single aircraft. This review article is largely concerned with the design and analysis of Tilt Rotor Unmanned Aerial Vehicles (TRUAV). The flight control legislation we developed has a straightforward design and excellent technical dependability.

Adaptive Nonsingular Fast Terminal Sliding Mode Control of Aerial Manipulation Based on Nonlinear Disturbance Observer

Aimed at resolving the trajectory tracking control problem of aerial manipulation, this paper presents an aerial manipulation system that consists of a quadrotor unmanned aerial vehicle (UAV) and a two-degree-of-freedom manipulator. The system adopted an integrated control strategy; that is, the rotor UAV and the manipulator were considered as a whole, the coupling effect was treated as an internal factor, and the whole was taken as the research object to establish a dynamic model. Then, by combining the nonsingular fast terminal sliding mode controller with the adaptive controller, an adaptive nonsingular fast terminal sliding mode controller was designed. A composite control rate based on the nonlinear disturbance observer and the adaptive nonsingular fast terminal sliding mode was determined in order to compensate for the external disturbance and the internal modeling error of the system as well as to ensure its rapid convergence. Finally, the different control methods were simulated by setting the operational tasks. The simulation results show that the proposed control method is effective.

Rotor Noise-Aware Noise Covariance Matrix Estimation for Unmanned Aerial Vehicle Audition

A noise covariance matrix (NCM) estimation method for unmanned aerial vehicle (UAV) audition is proposed with rotor noise reduction as its primary focus. The proposed NCM estimation method could be incorporated into audio processing algorithms using UAV-mounted microphone array systems. The NCM is formed through accurate estimation of the microphone array input signal's amplitude and phase by using a multi-sensory rotor noise power spectral density (PSD) estimator and a filter formed by exploiting the acoustical relationship between the microphone array and the rotor noise sources, respectively. The estimated NCM aims to be readily incorporable into several source enhancement algorithms to reduce the effects of rotor noise and improve the resultant audio quality. Experiment evaluation using real in-flight UAV rotor noise recordings shows that the estimated NCM significantly improves rotor noise reduction (of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 28$</tex-math></inline-formula> dB) and the quality of the target sound.

Developing a procedure to produce large-scale topographic maps of open-pit mines using rotary wing unmanned aerial vehicles

Recently, the rapid development of unmanned aerial vehicle (UAV) has led to increasingly popular applications of this technology in the field of surveying and mapping. This article presents the result of developing a procedure to produce large-scale topographic maps for open-pit mines in Vietnam in which unmanned aerial vehicles (UAV) are used to collect data. With the goal of creating a procedure that ensures accuracy, safety, and efficiency for the production of topographic maps, the theories and official regulations for measuring and editing topographic maps in general and in mines in particular, as well as using drones in Vietnam have been applied. The procedure is specifically developed for the vertical takeoff and landing (rotary) UAVs. Experiments consisting of the flight altitude calculation, determining the appropriate take-off position, and image acquisition were conducted at two open-pit mines, namely Dong Da Mai coal mine and Long Son quarry to verify the procedure. The digital surface models (DSM) and orthophotos are generated from the acquired photos, georeferenced to the VN-2000 coordinate system using ground control points (GCP), and evaluated with checkpoints. In addition, the experiment included the investigation of the various combination of GCP networks to determine the optimal GCP number for each case. Experimental results show that the procedure is logical, correct, straightforward, safe, and effective. UAV resulted products, such as orthophotos, DSM, and topographic maps were accurate and met the requirements of all the national standards.

UAV Detection Using Thrust Engine Electromagnetic Spectra

Artificial intelligence used in unmanned aerial vehicle (UAV) flight control systems tends to leave UAV control systems without any radio communication emissions, whose signatures in an electromagnetic spectrum (ES) are widely used to detect UAVs. There will be problems in the near future in detecting any dangerous threats associated with UAV swarms, kamikaze unmanned aerial vehicles (UAVs), or any other UAVs with electrically powered thrust engines because of the UAV’s flight capabilities in full radio silence mode. This article presents a different approach to the detection of electrically powered multi-rotor UAVs. The main idea is to register the electromagnetic spectrum of the electric thrust engines of the UAV, which varies because of the changing flight conditions. An experiment on a UAV’s electric thrust engine-produced electromagnetic spectrum is carried out, presenting the results of the flight-dependent characteristics, which were observed in the electromagnetic spectrum. The electromagnetic signature of the UAV’s electric thrust engines is analyzed, discussed, and compared with the most similar behaving electric engine, which was used on the ground as a domestic electric appliance. A precision tunable magnetic antenna is designed, manufactured, and tested in this article. The physical experiments have shown that the ES of the electric thrust engines of multi-rotor UAVs can be detected and recorded for recognition. The unique signatures of the ES of the multi rotor UAV electric engine are recorded and presented as a result of the carried-out experiments. A precision tunable magnetic antenna is evaluated for the reception of the UAV’s signature. Moreover, results were obtained during the performed experiments and discussions about the development of the future techniques for the identification of the ES fingerprints of the UAV’s electric thrust engine are carried out.

UAV-Mounted GPR for Object Detection Based on Cross-Correlation Background Subtraction Method

Unmanned aerial vehicle (UAV) ground-penetrating radar (GPR) is an important research topic for target detection in many fields. In this paper, we develop a UAV-mounted GPR system with a frequency band at 150 MHz–309 MHz. However, the received signal in the complex background is covered by various clutter and interference, leading to the serious obscuring of the target. To meet this challenge, a cross-correlation-based background subtraction (CCBS) method and an interference suppression technique are adopted in combination for more accurate detection. The CCBS method processes the raw echo by establishing a background-removal model and using the similarity between each A−Scan and a reference wave. In addition, a Butterworth filter is adopted to get rid of the active electromagnetic interference beyond the working frequencies of the system; then, a lateral Doppler filtering (LDF) technique is introduced to suppress the passive interference generated by the rotation of the UAV rotor itself. Moreover, a practical method for estimating the dielectric constant is introduced by the calibration process of the measured radar echo. Numerical simulations and experimental results by our UAV-GPR system demonstrate that the proposed method has presented a better performance than the traditional methods, and the system has great potential in detecting deeply buried targets.

Modeling and PID control of quadrotor UAV based on machine learning

Abstract The aim of this article was to discuss the modeling and control method of quadrotor unmanned aerial vehicle (UAV). In the process of modeling, mechanism modeling and experimental testing are combined, especially the motor and propeller are modeled in detail. Through the understanding of the body structure and flight principle of the quadrotor UAV, the Newton–Euler method is used to analyze the dynamics of the quadrotor UAV, and the mathematical model of the UAV is established under the small angle rotation. Process identifier (PID) is used to control it. First, the attitude angle of the model is controlled by PID, and based on this, the speed in each direction is controlled by PID. Then, the PID control of the four rotor aircraft with the center of gravity offset is simulated by MATLAB. The results show that the pitch angle and roll angle can be controlled by 5 degrees together without center of gravity deviation, and the PID can effectively control the control quantity and achieve the desired effect in a short time. Classical BP algorithm, classical GA-BP algorithm, and improved GA-BP algorithm were trained, respectively, with a total of 150 sets of training data, training function uses Levenberg-Marquardt (trainlm), and performance function uses mean squared error (MSE). In the background of the same noise, the improved GA-BP algorithm has the highest detection rate, classical GA-BP algorithm is the second, and classical BP algorithm is the worst. The simulation results show that the PID control law can effectively control the attitude angle and speed of the rotor UAV in the case of center of gravity deviation.