Multi-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.

Vision-Guided Tracking and Emergency Landing for UAVs on Moving Targets

This paper presents a vision-based adaptive tracking and landing method for multirotor Unmanned Aerial Vehicles (UAVs), designed for safe recovery amid propulsion system failures that reduce maneuverability and responsiveness. The method addresses challenges posed by external disturbances such as wind and agile target movements, specifically, by considering maneuverability and control limitations caused by propulsion system failures. Building on our previous research in actuator fault detection and tolerance, our approach employs a modified adaptive pure pursuit guidance technique with an extra adaptation parameter to account for reduced maneuverability, thus ensuring safe tracking of moving objects. Additionally, we present an adaptive landing strategy that adapts to tracking deviations and minimizes off-target landings caused by lateral tracking errors and delayed responses, using a lateral offset-dependent vertical velocity control. Our system employs vision-based tag detection to ascertain the position of the Unmanned Ground Vehicle (UGV) in relation to the UAV. We implemented this system in a mid-mission emergency landing scenario, which includes actuator health monitoring of emergency landings. Extensive testing and simulations demonstrate the effectiveness of our approach, significantly advancing the development of safe tracking and emergency landing methods for UAVs with compromised control authority due to actuator failures.

Utilizing unmanned aerial vehicle technology for environmental monitoring: Future trends, methods, and applications

AbstractThis paper provides an overview of the application of Unmanned Aerial Vehicles (UAVs) in environmental monitoring, with a focus on air pollution surveillance. Initially, the paper highlights the significance of UAV technology for real‐time air quality monitoring in developing countries and explores the characteristics of fixed‐wing and multirotor UAVs in monitoring air pollutants. It then delves into methods of measuring air pollutants using UAV platforms, including the three‐dimensional distribution of pollutants around roadsides, green belts, and street‐facing communities. The advantages of UAV measurements, such as lower costs, greater flexibility, and the ability to monitor in three dimensions, are emphasized. Finally, the paper discusses future trends in the field of environmental monitoring using UAVs, including technological advancements, evolving regulatory policies, and integration with other technologies like Artificial Intelligence, big data analytics, and 5G communication. These developments suggest an increasingly significant role for UAVs in environmental monitoring, enhancing efficiency, reducing costs, and contributing to public participation and environmental awareness. However, due to the delays in publication and review processes, the most recent studies may not have been included in the literature review, leading to a scenario where the review might not reflect the latest research trends.

Model-Scale Evaluation of Autonomous Ship Landing Guidance and Control Modes

This paper presents the results of an extensive model-scale experimental evaluation of autonomous ship landing guidance and control modes, with flight tests performed in the Maneuvering and Seakeeping (MASK) Basin at the U.S. Naval Surface Warfare Center Carderock Division. The experiments were performed using a commodities-based multirotor unmanned aerial vehicle (UAV) operating from a 20-ft-long model scale ship subject to scaled wave conditions. During testing, two separate guidance algorithms were evaluated: a quadratic programming (QP) based landing algorithm that plans the trajectory to a forecasted deck state and a simpler “baseline??? method that tracks deck motions while closing the distance between the aircraft and deck at a constant rate. Both algorithms commanded a Froude-scaled explicit model following control law, and the control law parameters were modified to progressively degrade aircraft tracking bandwidths. The results showed that the predictive capabilities of the QP algorithm allowed more direct landing paths to be planned when compared to the baseline algorithm and also allowed the QP algorithm to land with lower tracking bandwidths. But while the QP algorithm performed well in the majority of cases, there were several landings where a combination of poor deck state predictions and how the QP algorithm utilizes predictions to choose a land time resulted in significant terminal velocity and attitude errors. The baseline guidance algorithm, on the other hand, proved to be both simple and reliable when the UAV was in high bandwidth configurations but required a high reference tracking bandwidth.

Coordinated transportation of tethered multi-rotor UAVs based on differential graphical games

This paper investigates the stability control of the tethered multi-rotor unmanned aerial vehicles (T-MRUAVs) system in cargo delivery, where precise payload trajectory control is deemed unnecessary, with a focus on enhancing controller simplicity and robustness. To achieve this objective, it is imperative to employ an optimal formation tracking control, ensuring not only the symmetrical positioning of UAVs concerning the payload but also the achievement of distributed optimal solutions. Therefore, a formation tracking control strategy based on differential graphical games is designed to ensure optimized performance metrics and minimized suspended load swing. The designed control framework includes an outer loop and an inner loop control, which respectively achieve formation tracking and attitude stability. In addition to maintaining equilibrium states, each UAV is required to track the desired trajectory. Consequently, the formation tracking control consists of a gravity compensation term at the equilibrium point and a formation tracking control term based on differential graphical games. Then, a PID control strategy is displayed for the inner loop control, and the altitude control adopts feedback linearization. Finally, numerical simulations have validated the effectiveness of the designed algorithm.

Design, control, aerodynamic performances, and structural integrity investigations of compact ducted drone with co-axial propeller for high altitude surveillance

Compact multi-rotor unmanned aerial vehicles (UAVs) can be operated in many challenging environmental conditions. In case the UAV requires certain considerations in designing like lightweight, efficient propulsion system and others depending upon the application, the hybrid UAV comes into play when the usual UAV types cannot be sufficient to meet the requirements. The propulsion system for the UAV was selected to be coaxial rotors because it has a high thrust-to-weight ratio and to increase the efficiency of the propulsion system, a unique propeller was proposed to achieve higher thrust. The proposed propeller was uniquely designed by analyzing various airfoil sections under different Reynolds’s number using X-Foil tool to obtain the optimum airfoil section for the propellers. Since the design with duct increases efficiency, the Hybrid UAV presented in this paper has the modified novel convergent–divergent (C–D)-based duct which is a simplified model of a conventional C–D duct. The yawing and rolling maneuverings of the UAV could be achieved by the thrust vectoring method so that the design is simpler from a structural and mechanical perspective. The use of UAVs has risen in recent years, especially compact UAVs, which can be applied for applications like surveillance, detection and inspection, and monitoring in a narrow region of space. The design of the UAV is modeled in CATIA, and its further performance enactment factors are picked from advanced computational simulations relayed bottom-up approach. The predominant computational fluid dynamics (CFD) and fluid structure interaction (FSI) investigations are imposed and optimized through Computational Analyses using Ansys Workbench 17.2, which includes analysis of structural behaviour of various alloys, CFRP and GFRP based composite materials. From the structural analysis Titanium alloy came out to be the best performing materials among the others by having lower total deformation and other parameters such as normal and equivalent stress. The dynamics control response is obtained using MATLAB Simulink. The validations are carried out on the propeller using a thrust stand for CFD and on the duct through a high-jet facility for structural outcomes to meet the expected outcome.

A conical beam antenna array with filter characteristics for multi-rotor unmanned aerial vehicle measurement system

ABSTRACT This paper proposed a novel filter antenna with the conical beam, which consists of four magnetoelectric (ME) dipole antennas. By printing parasitic patches and etching open slots on the bottom of the substrate, two radiation nulls near the high frequency of the passband are introduced. Combined with the intrinsic radiation null of the ME-dipole antenna, the filtering response is obtained. In order to verify the design method, a prototype is manufactured and measured. The measurement results show that the impedance bandwidth (VSWR <2) of the antenna is 1.48 ~ 2.61 GHz (55.3%). In addition, the out-of-band suppression is more than 17.4 dB and the maximum radiation direction of the conical beam is stable around to Theta = 30°. The proposed antenna array can be promising candidates for multi-rotor unmanned aerial vehicle (UAV) measurement systems.

EVALUATION OF VEGETATION COVER DYNAMICS IN A SUB-TROPICAL CLIMATE USING HIGH-RESOLUTION MULTISPECTRAL DATA FROM UAVS

The study of vegetation status is of great importance as it reveals environmental factors and their relationship with the availability of water resources, components that are relevant in the planning, drafting and execution of numerous civil engineering projects. Detecting changes in vegetation cover often requires data from space-based platforms, which may have limitations in terms of spatial or temporal resolution and high costs for acquiring high-resolution data. For relatively small areas, the use of Unmanned Aerial Vehicles (UAVs) provides a solution. These devices facilitate the acquisition of land cover information with high spatial and temporal resolution through the use of photographic cameras or specialized sensors. This research analyzes the vegetation status within a study area characterized by distinct climatic seasons (dry and humid) within a warm sub-tropical climate. The primary focus lies in detecting changes in vegetation cover and evaluating the overall state of the vegetation. To accomplish this objective, a multirotor UAV equipped with a multispectral camera that captures RED and NIR bands is deployed. Various vegetation indices based on distance and slope are applied to access the vegetation’s condition. The analysis of the results indicates that it is possible to evaluate the vegetation cover dynamics using the very high-resolution data obtained with the VANT approach (10 cm per pixel). Notably, the distance-based vegetation indices demonstrate superior efficacy in delineating the vegetation status and the stress induced by the scarcity of water resources during the transition between dry and humid seasons.

Design, Multiperspective Investigations, and Performance Analysis of Multirotor Unmanned Aerial Vehicle for Precision Farming

Farming and agriculture are the oldest professions, but they are adapting to the technology revolution to accommodate the world’s growing population. UAV technology is part of the agriculture revolution, which aims to boost crop yields, properly monitor fields, and handle manpower shortages and resource efficiency. Rural India’s tiny farmers cannot afford UAV technology; therefore, it has not yet spread. Payload capacity, endurance, and selective spraying are other considerations. Thus, a low-cost, long-lasting UAV is necessary. This study modified the arm assembly to create a cheap hexacopter UAV. The endurance increased by 10% when 1.5 kg was lost. ABS plastic was used to make the modular arm. For working loads of 9 kg and 10 kg, pesticide/fertilizer spraying saves time, money, and manpower. Thus, a pressure-area coverage-cone angle connection is needed. This study examined spray patterns at different pressures and heights by varying flat fan nozzle and complete cone nozzle orifice diameters. These factors were linked, helping farmers choose the right nozzle. This nozzle was installed in the UAV and field-tested for paddy crops, showing a significant production improvement and lower operational cost. Chemical use pollutes and leaves traces in produce. Precision farming with artificial intelligence (AI) has solved this problem. In this experiment, AI algorithms were used to lemon leaves. Three AI systems were tested on different datasets to forecast plant stress by analyzing leaves due to technical constraints. CNN’s accuracy and computing speed make it ideal for precision farming. This work’s UAV was 30% cheaper than commercial UAVs and had more durability. Farmers will also benefit from the flat fan and complete cone nozzles’ pressure-area coverage connection.

A branch-and-price-and-cut algorithm for the unmanned aerial vehicle delivery with battery swapping

In the UAV-based urban delivery setting, the battery capacity serves as one of the key factors affecting the delivery range of drones. For a typical multi-rotor unmanned aerial vehicle (UAV), the maximum effective working range of a lithium battery for a single flight leg is approximately 20 km. In practice, the lithium battery is replaced each time the UAV completes a delivery, even though the remaining power of the replaced lithium battery is still capable of supporting the following flight legs. Therefore, the one-order-one-swapping strategy significantly brings down the battery utilisation as well as the efficiency of the delivery system. In the present paper, the UAV-based delivery problem with battery swapping decision is considered, where multiple en route pickup and deliveries are allowed. To obtain optimal decisions in terms of flight route, time and location of each battery swapping, a branch-and-price-and-cut algorithm is introduced. First we formulate this problem as a mixed integer linear programming (MILP) model, and then decompose it into a master problem and a pricing sub-problem, where the master problem is considered as a set covering model, and the sub-problem as an elementary shortest path problem with resource constraints (ESPPRC). To speed up the process of column generation, efficient heuristics are also integrated to provide basic feasible solutions. In the pricing sub-problem, a combination of heuristic algorithm and dynamic programming is implemented to improve the computational efficiency. In the numerical experiments, the proposed algorithm is tested on various scales of UAV-based urban delivery instances, the performance of which is analysed and compared with the off-the-shelf commercial solver.

Design and stabilization of a Coandă effect-based UAV: Comparative study between fuzzy logic and PID control approaches

Recent years have experienced a notable surge in unmanned aerial vehicles (UAV) research, prompting exploration into innovative concepts. This paper introduces a compact UAV harnessing the Coandă effect, an underexplored phenomenon in fluid mechanics. Featuring a single lift motor and two types of flaps, this UAV offers exceptional maneuverability, presenting significant challenges compared to conventional multi-rotor UAVs. To address these challenges, we explore the theoretical study, mechatronic design, and manufacturing complexities of the Coandă UAV. Emphasizing the distinctiveness of our work, we assess a Fuzzy Logic Controller (FLC) for UAV stabilization, marking the first application of such techniques to a Coandă-effect UAV, in contrast to the Proportional-Integral-Derivative (PID) control employed by other researchers. This innovative application of Fuzzy logic, particularly the Sugeno model, proves advantageous, offering faster and more robust control in uncertain or noisy environments. The proposed FLC strategy is systematically compared against a classical PID control approach, formulated based on the Mamdani and Sugeno models, optimized and manually tuned using a genetic algorithm. Our results showcase a significantly improved settling time of 0.417 s with the FLC strategy, surpassing the PID control approach by 35.23%. To substantiate our findings, we present comprehensive experimentation conducted at both software and hardware levels using Matlab® and Simulink for a microcontroller-based UAV. This groundbreaking fusion of novel design and advanced control techniques not only addresses the unique challenges posed by the Coandă UAV's aerodynamics but also contributes significantly to the field of UAV research.

Exploring the geological structure of a large-scale landslide using semi-airborne TEM in the Karst area of southwest China

Abstract The karst strata in the southwestern mountainous areas of China are extensively developed, creating a fragile geological environment. Landslide geological disasters occur frequently in these areas due to high annual rainfall, concentrated time periods, and frequent human engineering activities. Conventional ground geophysical methods face challenges in complex terrains, making it difficult to quickly and reliably obtain underground structures in landslide-prone areas. The semi-airborne transient electromagnetic method (SATEM) combines ground-based transmission and airborne reception of electromagnetic responses. This method, characterized by large emission magnetic moment and rapid data collection in the air, offers advantages in detecting deep geological structures in complex terrain areas. This article presents the application of a newly developed loop source SATEM system, integrated with multi-rotor unmanned aerial vehicles (UAVs), to conduct deep geological structure detection experiments on a large-scale landslide. The inversion results clearly depict the spatial distribution of the Feixianguan Formation and Longtan Formation, as well as vertical fractures developed in the strata due to underground coal mining. The detection test demonstrates the applicability of the SATEM method in detecting underground structures in Karst Plateau special landform areas, providing a new detection approach for similar regions.

A Comparative Study of Multi-Rotor Unmanned Aerial Vehicles (UAVs) with Spectral Sensors for Real-Time Turbidity Monitoring in the Coastal Environment

Complex coastal environments pose unique logistical challenges when deploying unmanned aerial vehicles (UAVs) for real-time image acquisition during monitoring operations of marine water quality. One of the key challenges is the difficulty in synchronizing the images acquired by UAV spectral sensors and ground-truth in situ water quality measurements for calibration, due to a typical time delay between these two modes of data acquisition. This study investigates the logistics for the concurrent deployment of the UAV-borne spectral sensors and a sampling vessel for water quality measurements and the effects on the turbidity predictions due to the time delay between these two operations. The results show that minimizing the time delay can significantly enhance the efficiency of data acquisition and consequently improve the calibration process. In particular, the outcomes highlight notable improvements in the model’s predictive accuracy for turbidity distribution derived from UAV-borne spectral images. Furthermore, a comparative analysis based on a pilot study is conducted between two multirotor UAV configurations: the DJI M600 Pro with a hyperspectral camera and the DJI M300 RTK with a multispectral camera. The performance evaluation includes the deployment complexity, image processing productivity, and sensitivity to environmental noises. The DJI M300 RTK, equipped with a multispectral camera, is found to offer higher cost-effectiveness, faster setup times, and better endurance while yielding good image quality at the same time. It is therefore a more compelling choice for widespread industry adoption. Overall, the results from this study contribute to advancement in the deployment of UAVs for marine water quality monitoring.

Inspection of Floating Offshore Wind Turbines Using Multi-Rotor Unmanned Aerial Vehicles: Literature Review and Trends

Operations and maintenance (O&amp;M) of floating offshore wind turbines (FOWTs) require regular inspection activities to predict, detect, and troubleshoot faults at high altitudes and in harsh environments such as strong winds, waves, and tides. Their costs typically account for more than 30% of the lifetime cost due to high labor costs and long downtime. Different inspection methods, including manual inspection, permanent sensors, climbing robots, remotely operated vehicles (ROVs), and unmanned aerial vehicles (UAVs), can be employed to fulfill O&amp;M missions. The UAVs, as an enabling technology, can deal with time and space constraints easily and complete tasks in a cost-effective and efficient manner, which have been widely used in different industries in recent years. This study provides valuable insights into the existing applications of UAVs in FOWT inspection, highlighting their potential to reduce the inspection cost and thereby reduce the cost of energy production. The article introduces the rationale for applying UAVs to FOWT inspection and examines the current technical status, research gaps, and future directions in this field by conducting a comprehensive literature review over the past 10 years. This paper will also include a review of UAVs’ applications in other infrastructure inspections, such as onshore wind turbines, bridges, power lines, solar power plants, and offshore oil and gas fields, since FOWTs are still in the early stages of development. Finally, the trends of UAV technology and its application in FOWTs inspection are discussed, leading to our future research direction.

Modelling, Analysis, and Control of OmniMorph: an Omnidirectional Morphing Multi-rotor UAV

This paper introduces for the first time the design, modelling, and control of a novel morphing multi-rotor Unmanned Aerial Vehicle (UAV) that we call the OmniMorph. The morphing ability allows the selection of the configuration that optimizes energy consumption while ensuring the needed maneuverability for the required task. The most energy-efficient uni-directional thrust (UDT) configuration can be used, e.g., during standard point-to-point displacements. Fully-actuated (FA) and omnidirectional (OD) configurations can be instead used for full pose tracking, such as, e.g., constant attitude horizontal motions and full rotations on the spot, and for full wrench 6D interaction control and 6D disturbance rejection. Morphing is obtained using a single servomotor, allowing possible minimization of weight, costs, and maintenance complexity. The actuation properties are studied, and an optimal controller that compromises between performance and control effort is proposed and validated in realistic simulations. Preliminary tests on the prototype are presented to assess the propellers’ mutual aerodynamic interference.

An adaptive trajectory tracking control scheme for a novel fully actuated multirotor unmanned aerial vehicle without using velocity signals

SummaryThis paper introduces a novel fully actuated multirotor unmanned aerial vehicle (UAV) and develops an adaptive control scheme for it without relying on velocity signals. Conventional multirotors are limited by their strong coupling between translation and rotation motion, which makes it difficult to operate one motion without affecting the other. However, this novel multirotor's fully actuated design allows for independent translation and rotation motion, leading to better operational performance and expanded application scope. Additionally, velocity measurement signals can be prone to noise pollution and signal drift, making them unreliable for control purposes. Therefore, this proposed control scheme is developed without using velocity signals. The study utilizes a control performance improving method and guarantees stability of the closed‐loop system using the direct Lyapunov method. Finally, numerical simulations are employed to demonstrate the effectiveness of the proposed control scheme.

Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator

Inspired by the limited battery life of multi-rotor unmanned aerial vehicles (UAVs), this research investigated hierarchical real-time control of UAVs with the generation of energy-optimal reference trajectories. The goal was to design a reference generator and controller based on optimal-control theory that would guarantee energy consumption close to optimal with lower computational cost. First, a least-squares-estimation-(LSE) algorithm identified the parameters of the UAV mathematical model. Then, by considering a precise electrical model for the brushless DC motors and rest-to-rest maneuvers, the extraction of clear rules to compute the optimal mission time and generate ’energetic trajectories’ was performed. These rules emerged from analyzing the optimal-control strategy results that minimized the consumption over many simulations. Afterward, a hierarchical controller tracked those desired energetic trajectories identified as sub-optimal. Numerical experiments compared the results regarding trajectory tracking, energy performance index, and battery state of charge (SOC). A co-simulation framework consisting of commercial software tools, Simcenter Amesim for the physical modeling of the UAV, and Matlab-Simulink executed numerical simulations of the implemented controller.

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.

Formation Planning for Tethered Multirotor UAV Cooperative Transportation With Unknown Payload and Cable Length

This study investigates the formation planning problem of tethered multirotor unmanned aerial vehicle (UAV) cooperative transportation with unknown payload and cable length. Normally, the transportation formation and trajectory are given in advance or designed based on the coupled system model. It is challenging to dynamically generate flexible formations in response to changing environments when the payload and cable length are unknown. This paper proposes an online formation planning method for multirotor UAVs. First, by analyzing the tension on cables, we propose some formation criteria and further construct a corresponding performance function of optimization. Then, desired trajectories/formations that can reduce the cost functions are generated by using the admittance model. Next, an estimation-based formation tracking control is designed, which ensures that multirotor UAVs follow the desired trajectories/formations. Finally, numerical simulations and experiments are conducted to demonstrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the formation planning problem of tethered multirotor UAV cooperative transportation. In industry and production applications, a team of multirotor UAVs has a larger load capacity than a single one. Nevertheless, the formation planning of the tethered cooperative transportation is challenging, especially when the payload and cable length are unknown. Rather than give a predefined formation or trajectory, this paper suggests an online formation planning method for multirotor UAVs in case of unknown payload and cable length. The method is implemented through the following three parts: 1) By analyzing the tension on cables, we propose some formation criteria and further construct a corresponding performance function of optimization. 2) By using the admittance model, we generate desired trajectories/formations that can minimize the proposed cost function. 3) By estimating cable tension, we design formation tracking control laws for multirotor UAVs to follow the desired trajectories/formations. The proposed formation planning method does not rely on the knowledge of the payload and length of cables, which makes it can be easily applied to extensive industry, production, and military practice. Finally, numerical simulations and experiments are conducted to demonstrate the feasibility of the proposed method.

Fault tolerant control of an octorotor UAV using sliding mode for applications in challenging environments

This paper presents the development of fault-tolerant controller and their application for multirotor unmanned aerial vehicles – specifically an octorotor – in challenging environments e.g. nuclear power plant inspection or other dull, dirty and dangerous applications. This paper considers a combination of sliding mode control robustness properties (to deal with actuator faults) and control allocation (to automatically redistribute the control signals to healthy actuators, especially in the event of actuator failures). The resultant controller has the ability to operate in both fault-free and fault/failure conditions without reconfiguring the main baseline controller. The proposed controller also has the ability to operate for up to six rotor failures which represent an under-actuation condition i.e., a case when only two rotors are available. The under-actuation scenarios are conditions when most FTC schemes are not able to operate due to the lack of redundancy. The simulation results conducted on the nonlinear model with wind/gusts and sensor noise, show a good tracking performance under various fault-free and fault/failure scenarios (over-actuation, sufficient actuation and under-actuation conditions).