Quadrotor Unmanned Aerial Vehicle Research Articles

Missing data reconstruction in attitude for quadrotor unmanned aerial vehicle based on deep regression model with different sensor failures

Forming a UAV cluster with multiple UAVs to collaborate in accomplishing tasks is the future development of battlefield. The UAV cluster can collaborate by communicating through inter-aircraft links and can quickly and accurately perform complex tasks such as path planning, collaborative reconnaissance, collaborative sensing and attack. In the process of Unmanned Aerial Vehicle (UAV) cooperative operation, obtaining the real-time attitude information of each UAV is preliminary to implement collaborative cooperation. However, in practice, due to the internal malfunction of sensors and mutual interference among UAVs, the acquired attitudes often suffer from data missing. This paper proposed a novel missing data reconstruction method for UAV’s attitude in the case of sensor failures. The attitude data of UAV is obtained through an advanced UAV simulation platform AirSim. Through fusing the temporal convolutional network (TCN) and Bi-directional long short-term memory (Bi-LSTM), a deep regression framework is established for missing data construction. In addition, we evaluate the reliability of the proposed method by comparing with different baseline models and different combinations of data missing. The performance of our method is quantified using metrics of root-mean-square-error (RMSE), mean absolute error (MAE), determination coefficient (DC), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) score. The averaged performance values of our proposed method outperform other baseline algorithms. The results of the experiment show that the study could well-address the issue of missing data reconstruction and provide reliable attitude data.

Deep-Reinforcement-Learning-Based Multitarget Coverage With Connectivity Guaranteed

Deriving a distributed, time-efficient, and connectivity-guaranteed coverage policy in multitarget environment poses huge challenges for a multirobot team with limited coverage and limited communication. In particular, the robot team needs to cover multiple targets while preserving connectivity. In this article, a novel deep-reinforcement-learning-based approach is proposed to take both multitarget coverage and connectivity preservation into account simultaneously, which consists of four parts: a hierarchical observation attention representation, an interaction attention representation, a two-stage policy learning, and a connectivity-guaranteed policy filtering. The hierarchical observation attention representation is designed for each robot to extract the latent features of the relations from its neighboring robots and the targets. To promote the cooperation behavior among the robots, the interaction attention representation is designed for each robot to aggregate information from its neighboring robots. Moreover, to speed up the training process and improve the performance of the learned policy, the two-stage policy learning is presented using two reward functions based on algebraic connectivity and coverage rate. Furthermore, the learned policy is filtered to strictly guarantee the connectivity based on a model of connectivity maintenance. Finally, the effectiveness of the proposed method is validated by numerous simulations. Besides, our method is further deployed to an experimental platform based on quadrotor unmanned aerial vehicles and omnidirectional vehicles. The experiments illustrate the practicability of the proposed method.

Sveobuhvatan pregled različitih strategija upravljanja za bespilotne letelice kuadrotor

The Quadrotor Unmanned Aerial Vehicles (UAVs) have gained significant attention in recent years due to their versatile applications in civilian and military sectors. It is a highly nonlinear and underactuated system and an outdoor flight; it is always subjected to external disturbances due to wind gusts and other environmental factors. Implementing the control strategies could be improved in terms of high computational time, uncertainty, approximation error, and a large amount of training data. One of the major challenges in the development of quadrotors is the design of an efficient and reliable control system. This paper presents a comprehensive review of various control strategies for quadrotors and evaluates their performance. The review includes classical control techniques such as PID (Proportional-Integral-Derivative) and LQR (Linear Quadratic Regulator) and modern control strategies such as adaptive and fuzzy control. Comparative analysis of various control strategies has been presented with key challenges and future directions. This study provides a useful guide for researchers and engineers in the design of control systems for quadrotors.

Adaptive backstepping position tracking control of quadrotor unmanned aerial vehicle system

<abstract><p>In this work, an adaptive backstepping position tracking control using neural network (NN) approximation mechanism is proposed with respect to the translational system of quadrotor unmanned aerial vehicle (QUAV). Concerning the translational system of QUAV, on the one hand, it does not satisfy the matching condition and is an under-actuation dynamic system; on the other hand, it is with strong nonlinearity containing some uncertainty. To achieve the control objective, an intermediary control is introduced to handle the under-actuation problem, then the backstepping technique is combined with NN approximation strategy, which is employed to compensate the uncertainty of the system. Compared with traditional adaptive methods, the proposed adaptive NN position control of QUAV can alleviate the computation burden effectively, because it only trains a scalar adaptive parameter instead of the adaptive parameter vector or matrix. Finally, according to Lyapunov stability proof and computer simulation, it is proved that the control tasks can be accomplished.</p></abstract>

SPT-Based Composite Hierarchical Antidisturbance Control Applied to a Quadrotor UAV

Although the flight control of quadrotor unmanned aerial vehicles have been widely studied, it is still very challenging to suppress multiple disturbances and efficiently utilize the inherent multi time-scale characteristics. As a well-known antidisturbance strategy, composite hierarchical antidisturbance control (CHADC) can compensate and attenuate disturbances simultaneously, thus enhancing the control performance of complex systems significantly. In the meantime, the singular perturbation theory (SPT) is employed to analyze the multi time-scale properties, and the original system is decomposed into the slow and fast subsystems. In this article, we present an antidisturbance control scheme based on the SPT and CHADC to enhance the capability of antidisturbance. A novel composite hierarchical antidisturbance (CHAD) predictor–corrector is proposed for the slow subsystem to integratively handle the disturbances from guidance and control perspective, and a CHAD <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{H}_{\infty }}$</tex-math></inline-formula> controller is employed for the fast subsystem subject to external disturbances and inertial uncertainties. Meanwhile, the compensability of disturbance is explicitly analyzed. Finally, experiments are carried out to validate the effectiveness of the proposed scheme.

Safety Control for the Quadrotor Unmanned Aerial Vehicle Despite Complete Lose of One Rotor

Safety Control for the Quadrotor Unmanned Aerial Vehicle Despite Complete Lose of One Rotor

Adaptive Fault-Tolerant Formation Control for Heterogeneous UAVs-UGVs Systems With Multiple Actuator Faults

This paper investigates the fault-tolerant formation control problem for heterogeneous multi-agent systems (MASs) consisting of quad-rotor unmanned aerial vehicles (UAVs) and two-wheel driven unmanned ground vehicles (UGVs) in the presence of multiple actuator faults. The heterogeneous dynamic characteristics and the uncertainties of the control gain matrix generated by actuator faults, especially the sudden changes to system structure due to finite sequential faults increase the difficulty of the formation control design. The dynamic models of UAVs and UGVs are first transformed to obtain the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$XOY$</tex-math></inline-formula> two-dimensional position formation systems of UAVs-UGVs and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Z$</tex-math></inline-formula> -axis altitude system of UAVs. Then, a distributed adaptive direct fault compensation control strategy is designed for the position system of the UAVs-UGVs and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Z$</tex-math></inline-formula> -axis altitude subsystems of UAVs, respectively, which can guarantee the expected formation structure under the influence of finite sequential faults. A simulation study based on the UAVs-UGVs cooperative systems is adopted to demonstrate the validity of the proposed fault compensation strategy.

Control of Quadrotor Based on RBF Neural Network Adaptive Fast Terminal Sliding-Mode Strategy

I The purpose of this paper is to propose a fast adaptive terminal sliding mode control method for quadrotor unmanned aerial vehicles (UAVs) under dynamic uncertainties and external interference. Firstly, a complete mathematical dynamics model of a quadrotor UAV is presented. Secondly, a robust dual-loop nonlinear control law for the quadrotor velocity and attitude tracking is designed. Through the introduction of equivalent control inputs, the attitude control channels can be decoupled and attitude stabilized controllers can be designed separately. For velocity tracking and altitude tracking control, coupled controllers are designed to ensure velocity tracking based on the stability of altitude tracking control. The attitude loop adopts RBF neural network sliding mode control, to compensate for model disturbance uncertainty, adjust its gain in real time, and suppress chattering problems. Finally, the experimental results show that the designed controller can not only suppress external interference, but also achieve accurate tracking of the desired flight command.

Research on Dynamic Path Exploration and Planning Method of Quadrotor Unmanned Aerial Vehicle in Complex Environment

As a research hotspot in the field of mobile robots in recent years, UAV(Unmanned Aerial Vehicle) has been improving its autonomous performance. Compared with traditional UAV, UAV has many advantages, such as vertical lifting, strong maneuverability and convenient operation. However, the dynamic path planning of quadrotor UAV, as a typical autonomous ability of mobile robot, has always been the research focus in the field of UAV. In this regard, this paper will try to study the dynamic path exploration planning method of quadrotor UAV in complex environment. In the complex environment model, the comparative simulation from the aspects of calculation time, calculation complexity and success rate shows that the selection of high-order polynomials can ensure high-order continuity, but it will also increase the calculation time and complexity. Compared with CF algorithm, the calculation time in this paper is 57% lower on average and the success rate is 17.6% higher on average, which shows that this algorithm is more suitable for online flight path planning of quadrotor. Comparatively speaking, the success rate of the other two methods decreases with the increase of environmental complexity, and the overall calculation time is longer.

Adaptive Control of Quadrotor Unmanned Aerial Vehicle With Time-Varying Uncertainties

This paper studies the real-time parameter estimation and adaptive tracking control problem for a six degrees of freedom (6-DOF) of quadrotor unmanned aerial vehicle (UAV) as an under-actuated system. A virtual proportional derivative (PD) is proposed to maintain position dynamics. Two adaptive control schemes are designed and compared to maintain the attitude dynamics of UAV while several parameters of UAV are unknown. In the first scheme, a classical adaptive scheme using the certainty equivalence principle is extended and designed for tracing control of the systems with unknown time-varying parameters. To improve the performance of the first scheme, a new control scheme is designed in the second scheme by proposing additional continuous function to handle the unknown parameters. An additional robust term is designed in both schemes to handle the perturbation caused by unknown time-varying parameters. The rigorous analytical proof and numerical simulation analysis are provided to support the efficacy of the proposed controller.

Anti-disturbance Trajectory Tracking Control for a Quadrotor UAV with Input Constraints⋆

This paper presents the trajectory tracking control for a quadrotor unmanned aerial vehicle(UAV) with disturbances and input constraints. The UAV's system is decomposed into an outer position loop and an inner attitude loop. The position tracking control is achieved by a class of saturated proportional-derivative(PD) control laws. Assuming that the unknown disturbances mainly effect on the translational dynamics, an adaptive control term is investigated to compensate the effects of the disturbances. The position tracking control force implies a coupled-attitude system that connects the inner attitude loop with the outer position loop. An intrinsic PD control law, which is directly designed on the special orthogonal group SO(3), is proposed to track the desired attitude. In order to reduce the calculational difficulty of the attitude tracking control law, an observer-based control law is further developed while the control law and observer can be designed separately. Simulation results complete this work.

Fixed-Time Neural Control of a Quadrotor UAV With Input and Attitude Constraints

This letter is concerned with the attitude control of a quadrotor unmanned aerial vehicle (UAV) subject to the input constraint, attitude constraint and model uncertainty. Firstly, we construct an auxiliary system to eliminate the adverse impact of the input saturation. Secondly, we introduce the nonlinear state-dependent function to deal with the attitude constraint directly. Thirdly, the neural network is utilized to identify the unknown terms in the system. Finally, with the help of the backstepping technology, a fixed-time control scheme is presented, which guarantees that the desired signal is followed with the fixed-time convergent rate. The effectiveness of the proposed scheme is validated by a simulation verification.

Fault Tolerant Control of a Quadrotor Unmanned Aerial Vehicle Based on Active Disturbance Rejection Control and Two-Stage Kalman Filter

Fault Tolerant Control of a Quadrotor Unmanned Aerial Vehicle Based on Active Disturbance Rejection Control and Two-Stage Kalman Filter

Fault-Tolerant Attitude Tracking Control Driven by Spiking NNs for Unmanned Aerial Vehicles.

In this article, we proposed a novel fault-tolerant control scheme for quadrotor unmanned aerial vehicles (UAVs) based on spiking neural networks (SNNs), which leverages the inherent features of neural network computing to significantly enhance the reliability and robustness of UAV flight control. Traditional control methods are known to be inadequate in dealing with complex and real-time sensor data, which results in poor performance and reduced robustness in fault-tolerant control. In contrast, the temporal processing, parallelism, and nonlinear capacity of SNNs enable the fault-tolerant control scheme to process vast amounts of sensory data with the ability to accurately identify and respond to faults. Furthermore, SNNs can learn and adjust to new environments and fault conditions, providing effective and adaptive flight control. The proposed SNN-based fault-tolerant control scheme demonstrates significant improvements in control accuracy and robustness compared with conventional methods, indicating its potential applicability and suitability for a range of UAV flight control scenarios.

Design and Simulation of Collision Avoidance Algorithm for an Unmanned Aerial Vehicle

In a complex environment, autonomous and agile flight for an Unmanned Aerial Vehicle (UAV) is an essential problem that must be solved. This paper describes the design and simulation of an obstacle avoidance algorithm for a quadrotor UAV using low-cost ultrasonic and infrared sensors. The quadrotor operated by the control algorithm was able to fly and reach a height of 1.2 m in 5 seconds, hover for 10 seconds, then proceed towards its target obstacle and by implementing a collision avoidance strategy with the optimal input signal from the Kalman filter, avoided the target obstacle at the 50cm mark, as designed. Our research has shown that the algorithm has a low computational cost, applied data fusion technique and can be implemented using low-cost sensors and thus provides a cheap and effective navigation method for UAVs.

An Active Landing Recovery Method for Quadrotor UAV: Localization, Tracking and Buffering Landing

This paper proposes a principle of fully autonomous ground mobile landing recovery of Unmanned Aerial Vehicles (UAV) for the problems of relatively fixed landing point, passive recovery, poor flexibility, and environmental adaptability, which mainly includes localization, landing point tracking, and buffering landing for quadrotor UAV. Firstly, aiming at the problem that it is difficult to accurately obtain the position of a UAV in dynamic mobile landing recovery, a target location method based on Asynchronous Multisensor Information Fusion(AMIF) and servo turntable focus tracking is proposed. Secondly, to achieve fast and high-precision tracking of UAVs, a tracking control strategy of an independently driven landing recovery system and a Stewart six-degree of freedom platform is proposed. Then, to solve the problems of large impact force and center of gravity instability in the landing process of UAV, a stationarity control algorithm based on model prediction and a compliance control algorithm based on adaptive variable impedance are designed to achieve active compliance control while adjusting the position and attitude of the receiving surface in real-time. Finally, a quadrotor unmanned landing and recovery experimental platform is built to verify the feasibility of the ground mobile landing and recovery strategy proposed in this paper and the effectiveness of the control algorithm.

Robust homography-based visual servo control for a quadrotor UAV tracking a moving target

In this study, a new robust homography-based visual tracking control approach for the quadrotor unmanned aerial vehicle (UAV) is developed. Specifically, employing the homography matrix as feedback, a hierarchical homography-based visual servoing (HBVS) scheme with a new command attitude extraction method to account for the underactuation of UAV is proposed. On this basis, a smooth hyperbolic tangent function is fulfilled as an augmented part of the backstepping control scheme, which guarantees the non-negative total thrust and avoid singularity. Additionally, a cascaded filter-based estimator and adaptive laws with integrable functions are embedded to counteract uncertainties including external perturbations, unknown acceleration of the moving target, and unknown image depth, and to facilitate the system’s asymptotic stability simultaneously. The theoretical analysis testifies that the whole close-loop system is asymptotically stable. Simulations further verify that the proposed HBVS controller can realize the visual tracking with a superior performance.

Robust state and output feedback prescribed performance interval type‐3 fuzzy reinforcement learning controller for an unmanned aerial vehicle with actuator saturation

AbstractThis paper presents a novel adaptive reinforcement learning control method with interval type‐3 fuzzy neural networks to improve the trajectory tracking control performance of quadrotor unmanned aerial vehicles in challenging flight conditions. The proposed reinforcement learning controller is independent of the system's dynamics, and only relies on measurable signals of the system. An adaptive robust controller in collaboration with the suggested reinforcement learning method is designed to significantly improve the robustness of the control system. The maximum overshoot/undershoot, convergence rate and final tracking accuracy are ensured a priori by the prescribed performance control methodology. To develop the proposed controller and to achieve a high‐performance closed‐loop system, a high‐gain observer is employed in order to estimate the velocity and acceleration of the quadrotor unmanned aerial vehicles system. The uniform ultimate boundedness stability of the proposed control algorithm is achieved by a Lyapunov‐based stability analysis. Finally, in the simulation section, it is shown that the presented intelligent controller with the proposed learning algorithm result in a better performance in contrast to the other kind of conventional control techniques.

Adaptive finite-time backstepping control tracker for quadrotor UAV with model uncertainty and external disturbance

In this study, for the purpose of finite-time tracking control of quad-rotor Unmanned Aerial Vehicle (UAV) system under modeling uncertainties and external disturbances, an adaptive command-filtered backstepping sliding mode control scheme is presented. At first, a state-space model of quad-rotor's system is considered in the existence of bounded model uncertainties and exterior perturbations. Then, a command-filter according to the virtual control input is planned to reduce the computing complications. Subsequently, the output of command filter is considered instead of high-order derivative of virtual input. Besides, in order to remove the differential signals which are obtained during design of command-filter, the compensation error is used. Afterward, the sliding surfaces are designed according to tracking errors which are defined between actual states of the quad-rotor, desired values and compensation error system. Furthermore, backstepping control procedure combined with Lyapunov stability theory is used for finite-time convergence of switching surface. Besides, the adaptive control strategy is applied to estimate the upper bound of uncertainty and disturbance which is unknown in advance. At last, in order to validate the high efficacy of the designed control scheme, simulation results based on the actual parameters of quadrotor are provided. In addition, the comparison outcomes are provided to show proficiency and validity of the recommended technique compared with the existing method.

Research on Attitude Control System of Quadrotor Unmanned Aerial Vehicle based on Fuzzy PID Control Algorithm

The control of quadrotor aircraft mainly includes attitude control and position control. Because the change of position is caused by the change of attitude, it is most important to control the attitude of quadrotor aircraft. PID control principle is simple, easy to master, convenient to use, strong adaptability and good robustness, and its control quality is insensitive to the change of the controlled object, so it is most widely used. Quadrotor UAV is a strongly coupled underactuated system, and its stability is easily affected by system uncertainty and external interference during flight. Therefore, an improved fuzzy PID control method is proposed. In this article, the significance and ideas of constructing the attitude control system of quadrotor UAV based on fuzzy PID control algorithm are analyzed. This control strategy not only keeps the advantages of fuzzy control algorithm, which is independent of accurate model, and flexible and fast control, but also combines the advantages of PID control algorithm with small static error, and can achieve good control of nonlinear complex system.