Control Of Unmanned Aerial Vehicles Research Articles

Research on the role of multi-sensor system information fusion in improving hardware control accuracy of intelligent system

Abstract Multi-sensor management and control technology generally constructs a reasonable objective function to solve the optimal control command set to control a limited number of sensors to obtain higher quality measurement information, thus obtaining better target tracking performance. In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. A sensor fusion algorithm combined with global optimization algorithm is innovatively proposed. According to the key frames saved in the previous steps, feature points in local maps, sensor information, and loop information, a global optimization algorithm based on graph optimization model is constructed to optimize the position and pose of intelligent hardware system and the position of spatial feature points. Moreover, this work studies and experiments on multi-sensor fusion simultaneous localization and mapping (SLAM) comprehensively and systematically, and the experimental results show that the algorithm proposed in this work is superior to common open-source SLAM algorithm in positioning accuracy and mapping effect under special circumstances. Therefore, the method proposed in this work can be applied to intelligent driving of vehicles, vision-assisted movement of robots and intelligent control of unmanned aerial vehicles, thus effectively improving the hardware control accuracy of intelligent systems.

Dynamics and Control of UAVs

In recent years, the study of unmanned aerial vehicles (UAVs) has attracted attention because of their diverse applications [...]

Tree structures may be the leadership structures employed by group motions exhibiting hierarchy

Collective behaviors leading to various fascinating movement patterns are believed to be the product of complex interplay among individuals. Previous studies have identified two types of leadership structures in pigeon flocks, i.e., hierarchical networks and reciprocal relationships. However, both of these leadership structures are predicated based on data analysis and lack substantial empirical evidence. Additionally, it is difficult to delineate a direct correspondence between leadership structures and trajectory data for pigeon flocks because birds cannot report their leadership structure. Herein, based on experiments involving volunteers, we found that tree structures may serve as the leadership structures employed by group motions exhibiting hierarchy. In the tree structure, each follower follows its only leader during collective motions, and the single top leader determines the direction of collective motions. By employing the tree structure, we introduced a model for describing collective motions. A method for obtaining the leadership structure through experimental trajectory data was proposed. This strategy was used to analyze flight trajectory data from a pigeon flock and elucidate the pigeons' leadership relationships. Our model can simulate the collective behavior of pigeon flocks, thus accurately replicating the findings of previous experimental studies. The results of this study provide insights regarding the leadership structure in pigeon flocks and have implications for artificial collective systems, e.g., autonomous formation control of multiple unmanned aerial vehicles or unmanned surface vehicles. Published by the American Physical Society 2024

A graphics-based digital twin (GBDT) framework for accurate UAV localization in GPS-denied environments

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) is crucial for effective assessment of large-scale civil infrastructure, as manual UAV control is time consuming and prone to mishaps. Autonomous navigation outdoors typically employs GPS signals to enable accurate localization and reduce long-term drifts. However, in many civil engineering applications, GPS signals are either poor or unavailable due to interference and/or multi-path effects. Current approaches for UAV localization in GPS-denied environments, track geometric features such as corners; however, these natural features can be misrepresented due to the presence of occlusions and/or image processing errors, resulting in significant localization errors that can grow with time. AprilTags can reduce localization errors, but installation on large-scale civil infrastructure can be challenging. Therefore, this paper proposes a framework that leverages the wealth of visual and geometric information encoded in a Graphics-Based Digital Twin (GBDT) of a target infrastructure to provide accurate localization of a UAV. The GBDT is comprised of a computer graphics model that faithfully represents a target structure’s geometry, structural features, and visual textures. The visual details of the GBDT are exploited to design an object recognition algorithm that detects GBDTtags, which are distinctive objects (or a collection of components) on the target structure in advance; GBDTtags provide functionality similar to AprilTags. When the camera attached to a UAV detects one or more GBDTtags, the vertices of the GBDTtags are mapped from the image plane into the GBDT coordinate system, allowing for the UAV to be localized. The framework, termed herein as GBDTpose, is first validated numerically using Blender, Gazebo, and Mavros software-in-the-loop (SITL). Subsequently, field validation is carried out using the Kavita and Lalit Bahl Smart Bridge at the University of Illinois Urbana-Champaign (UIUC). Results show that localization in GPS-denied environments can be achieved with 5-50 cm accuracy without the need for physical markers being placed on the structure.

Design and implementation of voice-command controller for fixed-wing unmanned aerial vehicles using automatic speech recognition and natural language processing techniques

This paper explores the development of a voice command controller leveraging the capabilities of an automatic speech recognition (ASR) system and natural language processing (NLP) technique to manage a fixed-wing unmanned aerial vehicle (UAV). The controller is designed to interpret voice commands for controlling fixed-wing UAVs. The implementation of the system involved two key stages: (1) implementation of a voice command controller using integrated ASR and NLP techniques deployed in a simulated plane in the SITL simulator followed by (2) deployment of the controller to an actual Sky Surfer plane fixed-wing aircraft. The results indicate that the algorithm achieved an average confidence rate of 91.86 % in transcribing voice commands to words, with a Word Error Rate (WER) of approximately 0.021. The developed system demonstrated the ability to interpret both low-level and high-level commands for UAV control interfaces. Such an interface offers greater intuitiveness compared to traditional RC controls, potentially requiring less training to operate effectively. Moreover, it reduces human workload, as once commands are issued, the system can execute them without the need for continuous supervision.

Event-triggered finite-time adaptive neural network control for quadrotor UAV with input saturation and tracking error constraints

In this article, an event-triggered finite-time adaptive neural network control tracking strategy is proposed for quadrotor unmanned aerial vehicle (UAV) with input saturation and error constraints. Firstly, the radial basis function neural networks (RBFNNs) are adopted to identify the unknown uncertainty of quadrotor UAV model from the installation errors, gyroscope errors and so on. An auxiliary equation is constructed to deal with input physical saturation from the actuator motors. Additionally, by combining the performance function and error transformation, the issue of error constraint is solved. Based on the Lyapunov stability theory and event-triggered mechanisms, a finite-time adaptive neural network scheme is developed to ensure that the closed-loop quadrotor UAV system is semi-globally practically finite-time stable, and save the computation, resources, and transmission load. Finally, the simulation results illustrate the good tracking performance of quadrotor UAV by using the proposed control strategy.

Fixed‐Time State Observer‐Based Robust Adaptive Neural Fault‐Tolerant Control for a Quadrotor Unmanned Aerial Vehicle

ABSTRACTThis paper presents a fixed‐time state observer‐based robust adaptive neural fault‐tolerant control (RANFTC) for attitude and altitude tracking and control of quadrotor unmanned aerial vehicles (UAVs), considering multiple actuator faults, parametric uncertainty, and unknown external disturbances simultaneously. A novel fixed‐time state error estimation based on sliding mode observer is designed, which is independent of initial conditions. A proportional–integral–derivative (PID) based sliding mode control (SMC) is proposed to handle actuator faults and unknown disturbances in combination with the fixed‐time observer within the fault‐tolerant control (FTC) design scheme. The radial basis function neural network (RBFNN) is employed with the controller to approximate the uncertain parameters of the system. Furthermore, two new adaptive laws are designed to estimate the sudden actuator fault and the unknown upper bound of disturbances independently. Implementing these estimation schemes avoids overestimation, enhances the robustness of the presented controller, and substantially eliminates the control chattering problem. By applying the Lyapunov stability concept, the suggested control strategy guarantees that the states of the quadrotor UAV converge to the origin in a finite time. Finally, simulation studies are conducted to demonstrate the tracking performance and highlight the effectiveness of the proposed FTC design compared to the existing FTC methods.

Distributed robust tracking control for multiple unmanned aerial vehicles: theory and experimental verification

The distributed trajectory tracking problem for multiple UAVs under unknown external disturbance is investigated. A new nonlinear robust trajectory tracking strategy for multiple UAVs is proposed. The dynamic model for the UAVs' formation is illustrated in the Tangent frame. For the trajectory tracking problem, a Robust formation tracking control strategy based on robust integral of the signum of error (RISE) is designed to compensate for the effects of unknown external disturbances, which improves the robustness of the UAVs' formation system. Based on the Lyapunov stability analysis, it is proved that the global asymptotic convergence of the coordination errors and the semi-global asymptotic convergence of the UAVs' position tracking errors are achieved. The proposed formation control strategy is validated via real-time experiments that are performed on the self-build UAVs' formation flight control testbed. Flights without wind disturbance and flights with disturbance are all performed. The performance comparison experiment with the conventional sliding mode control algorithm is performed. The experimental results show that the designed control strategy can achieve good coordinated trajectory tracking of multiple UAVs, and has better control performance compared with normal sliding mode control law.

Unmanned Surface Vessel–Unmanned Aerial Vehicle Cooperative Path Following Based on a Predictive Line of Sight Guidance Law

This paper explores the cooperative control of unmanned surface vessels (USVs) and unmanned aerial vehicles (UAVs) in maritime rescue and coastal surveillance. The USV-UAV system faces challenges of disturbances and substantial inertia-induced overshooting during path following. A novel position prediction line of sight (LOS) guidance law is proposed to address these issues for USV path following control. Radial basis function-based neural networks (RBF-NNs) are used to estimate disturbances, and a high-order differentiator is used to design a velocity observer for unknown USV velocity. The UAV control system employs proportional–derivative (PD) control with feedforward compensation for quadrotor control design and utilizes a finite-time converging third-order differentiator to differentiate non-continuous functions. The simulation results demonstrate strong robustness in the proposed USV-UAV cooperative control algorithm. It achieves path following control in the presence of wind and wave disturbances and exhibits minimal overshoot.

Automatic Landing Control for Fixed-Wing UAV in Longitudinal Channel Based on Deep Reinforcement Learning

The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used control method, which requires the tuning of its parameters to account for the specific characteristics of the landing environment and the potential for external disturbances. In contrast, neural networks can be modeled to operate under given inputs, allowing for a more precise control strategy. In light of these considerations, a control system based on reinforcement learning is put forth, which is integrated with the conventional PID guidance law to facilitate the autonomous landing of fixed-wing UAVs and the automated tuning of PID parameters through the use of a Deep Q-learning Network (DQN). A traditional PID control system is constructed based on a fixed-wing UAV dynamics model, with the flight state being discretized. The landing problem is transformed into a Markov Decision Process (MDP), and the reward function is designed in accordance with the landing conditions and the UAV’s attitude, respectively. The state vectors are fed into the neural network framework, and the optimized PID parameters are output by the reinforcement learning algorithm. The optimal policy is obtained through the training of the network, which enables the automatic adjustment of parameters and the optimization of the traditional PID control system. Furthermore, the efficacy of the control algorithms in actual scenarios is validated through the simulation of UAV state vector perturbations and ideal gliding curves. The results demonstrate that the controller modified by the DQN network exhibits a markedly superior convergence effect and maneuverability compared to the unmodified traditional controller.

Drone motion prediction from flight data: a nonlinear time series approach

In this paper, we explore the application of data-driven predictive systems in enhancing unmanned aerial vehicle (UAV) control capabilities. We introduce a new model for predicting the motion of individual drones by utilizing fundamental flight control data. The model aims to improve the autonomy of individual drones and circumvent the complexity of traditional flight control systems, thus eliminating intricate nested controls. The proposed model lays the foundation for studying collective behaviours within a cluster of drones, thereby advancing the research into swarm behaviour exhibited by drones. The research findings demonstrate the potential of data-driven methods in the construction of UAV control systems. In particular, we here show a comparison of the prediction performances between two neural network architectures using real drone flight data involved in various kinds of motions. We explore the utility of using long short term memory (LSTM) and nonlinear autoregressive with exogenous inputs (NARX) family of nonlinear time series models in developing a virtual drone model using real experimental data.

Dynamic event-triggered neuroadaptive fault-tolerant control of quadrotor UAV with a novel cosine kernel

The fault-tolerant control (FTC) for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) has attracted researchers. The non-linear model of the UAV, coupled with model uncertainties, external disturbances, and actuator failures, requires the function approximation of the lumped non-linearity for controller design. One of the most efficient ways to approximate non-linearity is using radial basis function neural networks (RBFNNs). To date, RBFNNs have been formulated, trained, and used directly for function approximation, which requires considerable computation to derive the control laws. The proposed control and parameter estimation laws approximate the non-linearity by using RBFNNs indirectly. The proposed laws with virtual parameter estimation do not require the actual formulation of RBFNNs and their weights, thus saving on computational resources. For kernel optimization, the Gaussian kernels with exponential terms in RBFNNs are replaced by cosine kernels with algebraic terms, which shows faster convergence as per simulation results. To save on communication bandwidth, static event-triggering communication mechanisms (SECM) and dynamic event-triggering mechanisms (DECM) have been proposed. As DECM works on dynamically changing variables, it saves more communication bandwidth, as tested in simulation. Lyapunov stability analysis proves that errors are uniformly ultimately bounded (UUB). The performance of the proposed algorithm has been tested through numerical simulations, which show superior performance when compared with similar studies. The proposed algorithm has been validated in a real-time Gazebo simulator.

Robust Linear Discrete Control for a Hexacopter: Experimental Results

This paper presents a discrete-time robust linear control method for tracking a hexacopter's trajectory in the presence of external disturbances. The control of multi-rotor type unmanned aerial vehicles (UAVs) has gained considerable attention recently due to their various applications, such as crop spraying in precision agriculture. The control of UAVs requires robustness to reject disturbances and accommodate dynamic uncertainties. To achieve this goal, the robust discrete-time control action is designed in two stages. The first stage utilizes the solution of a difference Riccati-equation to guarantee system stability in an optimal sense. The second stage provides system robustness against external disturbances and uncertain dynamics. Furthermore, the Lyapunov stability theory for discrete linear systems is used to derive system asymptotic stability. Finally, experimental results of the hexacopter flight are provided to illustrate the effectiveness of the presented control law.

Relational Maneuvering of Leader-Follower Unmanned Aerial Vehicles for Flexible Formation.

In this article, we propose a new formation scheme for a leader-follower unmanned aerial vehicle (UAV) system inspired by a human pilot's behavior wherein the formation geometry does not necessarily remain fixed as the vehicles maneuver. In other words, the position and the orientation of the follower with respect to the leader are subject to change as they maneuver while satisfying some constraints. Our strategy ensures that the follower UAV maintains a desired fixed relative distance with respect to the leader UAV, whereas its orientation with respect to the leader UAV may change to reduce its control effort and provide it with a tactical advantage. We call this new relational maneuvering scheme flexible since the set of feasible positions for the follower UAV is not fixed, as is common in close proximity two-ship formations in air-to-air combat. By assigning the follower UAV's linear and angular velocities as its control inputs, our approach tries to emulate a human pilot's behavior in UAVs by taking anticipatory maneuvers when the leader UAV makes aggressive turns. The proposed flexible-geometry formation scheme is robust to the leader's maneuver changes since the follower UAV's control law does not need the information of the leader's angular speed control and only uses relative measurements. This makes the design lucrative even when the vehicles are heterogeneous, global measurements are unavailable, or if the leader UAV is noncooperative. Finally, we present multiple simulations to highlight the merits of the flexible formation control laws.

Practical three-dimensional path following and control for unmanned combat aerial vehicles based on expansion of L1+ guidance logic and disturbance rejection

An effective virtual-target-based three-dimensional path-following algorithm is proposed for unmanned combat aerial vehicles, which extends from the planar [Formula: see text] guidance. The separated implementation of the planar guidance law in two perpendicular planes provides the longitudinally-laterally decoupled acceleration commands. In order to obtain a non-unique virtual-target-point, a numerical iterative method using auxiliary path-related variables is proposed. Moreover, two protection improvements are discussed for real-word implementation: one involves the modification of adaptive [Formula: see text] length for vehicle position far away from the desired path; the other involves a switching strategy for multi-segment paths. The improved algorithm eliminates complex coordinate transformation and easily connects different types of path segments. For the control loop, technology enhanced with the active disturbance rejection control was used to facilitate the design of a bank-to-turn autopilot. For the pitch and roll channels, a rate-damping augmentation loop was used first to transform the original system into a generalized plant. Then the second-order active disturbance rejection control was structured to estimate the total disturbance, including time-varying dynamics, model mismatch, and coupling of channels, making the system an ideal integral-series type. The non-smooth optimization technique in the H-infinity methodology was used to facilitate tuning of the fixed-structure active disturbance rejection control.

Robust convex LPV control of a Helicopter-UAV transporting a suspended payload

This paper develops a robust integral sliding mode controller for a helicopter-type unmanned aerial vehicle with a suspended payload. The nonlinear model is rewritten by considering a convex Linear Parameter Varying model that captures the nonlinear system dynamics. Then, a controller is developed by considering a nominal integrator comparator scheme and a discontinuous control based on sliding modes. The first part of the controller is dedicated to tracking the desired trajectory, while the second is devoted to attenuating the effect of external disturbance through a sliding surface. A set of linear matrix inequalities obtained from a Lyapunov quadratic function gives sufficient conditions to compute the controller gains. Numerical simulations are carried out to illustrate the effectiveness of the proposed approach in tracking the desired trajectory and attenuating the load oscillations under the presence of external disturbance.

Multiple Unmanned Aerial Vehicles Coalition Formation and Control for Collaborative Defense Mission

Multiple Unmanned Aerial Vehicles Coalition Formation and Control for Collaborative Defense Mission

Fuzzing drones for anomaly detection: A systematic literature review

Drones, also referred to as Unmanned Aerial Vehicles (UAVs), are becoming popular today due to their uses in different fields and recent technological advancements which provide easy control of UAVs via mobile apps. However, UAVs may contain vulnerabilities or software bugs that cause serious safety and security concerns. For example, the communication protocol used by the UAV may contain authentication and authorization vulnerabilities, which may be exploited by attackers to gain remote access over the UAV. Drones must therefore undergo extensive testing before being released or deployed to identify and fix any software bugs or security vulnerabilities. Fuzzing is one commonly used technique for finding bugs and vulnerabilities in software programs and protocols. This article reviews various approaches where fuzzing is applied to detect bugs and vulnerabilities in UAVs. Our goal is to assess the current state-of-the-art fuzzing approaches for UAVs, which are yet to be explored in the literature. We identified open challenges that call for further research to improve the current state-of-the-art.

Adaptive quadrotor control using online dynamic mode decomposition

Precision control of Unmanned Aerial Vehicles (UAVs) is essential for deployment in a wide range of applications. However, real-world flight conditions often deviate from ideal operating scenarios, presenting uncertainties such as external disturbances and unmodeled dynamics. These can dramatically impact tracking accuracy and stability. This study proposes a novel adaptive control technique for quadrotors based on Windowed Dynamic Mode Decomposition (DMDc) techniques. This techniques efficiently identifies dynamic models directly from data, and updates this model in real-time, allowing the controller to compensate for changing conditions. To facilitate realistic validation, the proposed system is integrated within a Hardware-in-the-Loop (HiL) framework. In a series of simulated experiments, the adaptive controller demonstrates improvement in trajectory tracking under disturbances when compared to a conventional inverse dynamics approach. This research underscores the promise of DMDc-based techniques combined with adaptive control to enhance UAV operation, enabling safer and more robust performance in demanding scenarios.

Parameter tuning for active disturbance rejection control of fixed-wing UAV based on improved bald eagle search algorithm

PurposeThis paper aims to develop a method for tuning the parameters of the active disturbance rejection controller (ADRC) for fixed-wing unmanned aerial vehicles (UAVs). The bald eagle search (BES) algorithm has been improved, and a cost function has been designed to enhance the optimization efficiency of ADRC parameters.Design/methodology/approachA six-degree-of-freedom nonlinear model for a fixed-wing UAV has been developed, and its attitude controller has been formulated using the active disturbance rejection control method. The parameters of the disturbance rejection controller have been fine-tuned using the collaborative mutual promotion bald eagle search (CMP-BES) algorithm. The pitch and roll controllers for the UAV have been individually optimized to obtain the most effective controller parameters.FindingsInspired by the salp swarm algorithm (SSA), the interaction among individual eagles has been incorporated into the CMP-BES algorithm, thereby enhancing the algorithm's exploration capability. The efficient and accurate optimization ability of the proposed algorithm has been demonstrated through comparative experiments with genetic algorithm, particle swarm optimization, Harris hawks optimization HHO, BES and modified bald eagle search algorithms. The algorithm's capability to solve complex optimization problems has been further proven by testing on the CEC2017 test function suite. A transitional function for fitness calculation has been introduced to accelerate the ability of the algorithm to find the optimal parameters for the ADRC controller. The tuned ADRC controller has been compared with the classical proportional-integral-derivative (PID) controller, with gust disturbances introduced to the UAV body axis. The results have shown that the tuned ADRC controller has faster response times and stronger disturbance rejection capabilities than the PID controller.Practical implicationsThe proposed CMP-BES algorithm, combined with a fitness function composed of transition functions, can be used to optimize the ADRC controller parameters for fixed-wing UAVs more quickly and effectively. The tuned ADRC controller has exhibited excellent robustness and disturbance rejection capabilities.Originality/valueThe CMP-BES algorithm and transitional function have been proposed for the parameter optimization of the active disturbance rejection controller for fixed-wing UAVs.