multi-UAV Control Research Articles

Distributed adaptive disturbance observer-based multi-channel event-triggered finite-time coordinated control for multi-UAVs with actuator failures

A finite-time coordination controller based on an adaptive disturbance observer and a multi-channel event-triggered strategy is proposed for the multi-UAVs control problem with actuator faults, external disturbances, and communication constraints. First, an adaptive finite-time disturbance observer based on a hyperbolic tangent function is established to compensate for actuator faults and external disturbances without knowing the upper limit of the total disturbance derivative in advance. Second, a multi-channel event-triggered strategy is proposed to reduce communication between neighboring UAVs, between the controller and actuator, and between the disturbance observer and the controller at the same time. Then, the event-triggered strategy integrates the designed disturbance observer and controller, and the proposed control strategy is analyzed for finite-time stability using Lyapunov stability theory and finite-time theory without applying the separation principle. Finally, the effectiveness of the proposed control scheme is verified by numerical simulations.

Fixed-Time Collision-Free Fault-Tolerant Formation Control of Multi-UAVs Under Actuator Faults.

When cooperating through an intensive formation, the safe distancing of unmanned aerial vehicles (UAVs) is a delicate issue, especially if UAVs are subjected to actuator faults that cause rapid maneuvers. This article investigates the fixed-time fault-tolerant formation control of multiple quadrotor UAVs under actuator faults, which considers the collision avoidance among UAVs when faults occur, and the convenience of engineering application. First, an augmented fixed-time observer with measurement noise oppression is adopted to estimate and compensate actuator faults and disturbance in rotational and translational dynamics. Then, a baseline attitude controller, a command filter, and a velocity controller are proposed for each quadrotor UAV to track the desired velocity within a fixed time. Next, a distributed fixed-time sliding-mode controller that integrates the gradient of repulsive potential function into the sliding manifold is designed to achieve leader-follower formation control and collision avoidance simultaneously. The control scheme is proven to be fixed-time convergent via Lyapunov stability analysis and is normalized in accordance with the compatibility of hardware implementation. Finally, the designed algorithm is embedded into PX4 architecture to illustrate the effectiveness and practicality of the control strategy.

Distributed Prescribed Performance Fault-Tolerant Control of Multi-UAVs With Input Delays via Dynamic Event-Triggered Observers

Distributed Prescribed Performance Fault-Tolerant Control of Multi-UAVs With Input Delays via Dynamic Event-Triggered Observers

Multi UAV Cooperative Reconnaissance based on Dynamic Programming VDN Algorithm

This paper proposes a multi agent value decomposition network (VDN) based multi UAV collaborative reconnaissance and control method to address the issue of insufficient strategies for multi UAV collaborative reconnaissance and control. By designing corresponding algorithm networks and training processes, the goal of autonomy, collaboration, and intelligence among multiple unmanned aerial vehicle systems has been achieved, assisting unmanned aerial vehicle combat forces in achieving collaborative operations and decision-making. This article uses AirSim as the simulation verification environment to verify the effectiveness of the proposed algorithm. The experimental results show that the algorithm proposed in this paper can achieve multi UAV collaborative reconnaissance tasks in complex environments, providing an intelligent solution for UAV collaborative control.

Distributed Robust Formation Control of Heterogeneous Multi-UAVs With Disturbance Rejection

Distributed Robust Formation Control of Heterogeneous Multi-UAVs With Disturbance Rejection

Fuzzy Model Predictive Formation Maneuver Control of Multi-UAVs with Interval Output-Constrained

Fuzzy Model Predictive Formation Maneuver Control of Multi-UAVs with Interval Output-Constrained

Distributed event-triggered fractional-order fault-tolerant control of multi-UAVs with full-state constraints

Distributed event-triggered fractional-order fault-tolerant control of multi-UAVs with full-state constraints

Neural Adaptive Distributed Formation Control of Nonlinear Multi-UAVs With Unmodeled Dynamics.

The problem of neural adaptive distributed formation control is investigated for quadrotor multiple unmanned aerial vehicles (UAVs) subject to unmodeled dynamics and disturbance. The quadrotor UAV system is divided into two parts: the position subsystem and the attitude subsystem. A virtual position controller based on backstepping is designed to address the coupling constraints and generate two command signals for the attitude subsystem. By establishing the communication mechanism between the UAVs and the virtual leader, a distributed formation scheme, which uses the UAVs' local information and makes each UAV update its position and velocity according to the information of neighboring UAVs, is proposed to form the required formation flight. By designing a neural adaptive sliding mode controller (SMC) for multi-UAVs, the compound uncertainties (including nonlinearities, unmodeled dynamics, and external disturbances) are compensated for to guarantee good tracking performance. The Lyapunov theory is used to prove that the tracking error of each UAV converges to an adjustable neighborhood of zero. Finally, the simulation results demonstrate the effectiveness of the proposed scheme.

Deep deterministic policy gradient based multi-UAV control for moving convoy tracking

Applications of multiple unmanned aerial vehicles (UAV) involve complex control dynamics for accomplishing any task. This paper employs a multi-UAV system for continuous tracking and end-to-end coverage of a moving convoy of vehicles to provide security and surveillance cover. The coverage is achieved by maintaining the moving convoy within the overlapping Field-of-Views (FoVs) of the UAVs. To learn the controls of the autonomous multi-UAV system, we propose a deep reinforcement learning based multi-agent actor-critic method called GPR-MADDPG. The proposed method makes use of Gaussian Process Regression (GPR) to estimate an unbiased and stable target value of the critic. Further, the kernel function of the GPR model has been adapted to keep the high variance in the convoy trajectory in check. The rewards for training the multi-UAV system are formulated to maximize the end-to-end convoy coverage by optimizing the overlaps between the FoVs along with minimizing the tracking error. Experiments were performed on real-world road trajectories of varying complexities along with varying convoy speeds and the number of UAVs. Further tests were performed using a simulator with a real-world physics engine. The experiments show that the proposed GPR-MADDPG model results in the least amount of overlapping error and accumulates maximum reward as compared to other prevalent approaches in the literature.

Fault Detection and Fault-Tolerant Cooperative Control of Multi-UAVs under Actuator Faults, Sensor Faults, and Wind Disturbances

Fault detection (FD) and fault-tolerant cooperative control (FTCC) strategies are proposed in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) under actuator faults, sensor faults, and wind disturbances. Firstly, the faulty model is introduced while the effectiveness loss, deviation of thrust throttle setting, and pitot sensor faults are considered. Secondly, the faulty UAV model with wind disturbances is linearized and the system is then converted into two subsystems by using state and output transformations. Further, cooperative unknown input observers (UIOs) are developed to estimate the faults, disturbances, and states. By combining with the observers’ estimations, adaptive thresholds are designed to detect actuator and sensor faults in the system. Then, considering state constraints, a backstepping-based FTCC scheme is proposed for multiple UAVs (multi-UAVs) suffering from actuator faults, sensor faults, and wind disturbances. It is shown by Lyapunov analysis that the tracking errors are fixed-time convergent. Finally, the effectiveness of the FD and FTCC scheme is verified by numerical simulation.

The Viability of a Grid of Autonomous Ground-Tethered UAV Platforms in Agricultural Pest Bird Control

Pest birds are a salient problem in agriculture all around the world due to the damage they can cause to commercial or high-value crops. Recent advancements in Unmanned Aerial Vehicles (UAVs) have motivated the use of drones in pest bird deterrence, with promising success already being demonstrated over traditional bird control techniques. This paper presents a novel bird deterrence solution in the form of tethered UAVs, which are attached and arranged in a grid-like fashion across a vineyard property. This strategy aims to bypass the power and endurance limitations of untethered drones while still utilising their dynamism and scaring potential. A simulation model has been designed and developed to assess the feasibility of different UAV arrangements, configurations, and strategies against expected behavioural responses of incoming bird flocks, despite operational and spatial constraints imposed by a tether. Attempts at quantifying bird persistence and relative effort following UAV-induced deterrence are also introduced through a novel bird energy expenditure model. This aims to serve as a proxy for selecting control techniques that reduce future foraging missions. The simulation model successfully isolated candidate configurations, which were able to deter both single and multiple incoming bird flocks using a centralised multi-UAV control strategy. Overall, this study indicates that a grid of autonomous ground-tethered UAV platforms is viable as a bird deterrence solution in agriculture, a novel solution not seen nor dealt with elsewhere to the authors’ knowledge.

Distributed MPC for Trajectory Tracking and Formation Control of Multi-UAVs With Leader-Follower Structure

Distributed MPC for Trajectory Tracking and Formation Control of Multi-UAVs With Leader-Follower Structure

Cooperative Multiagent Deep Reinforcement Learning for Reliable Surveillance via Autonomous Multi-UAV Control

CCTV-based surveillance using unmanned aerial vehicles (UAVs) is considered a key technology for security in smart city environments. This paper creates a case where the UAVs with CCTV-cameras fly over the city area for flexible and reliable surveillance services. UAVs should be deployed to cover a large area while minimize overlapping and shadow areas for a reliable surveillance system. However, the operation of UAVs is subject to high uncertainty, necessitating autonomous recovery systems. This work develops a multi-agent deep reinforcement learning-based management scheme for reliable industry surveillance in smart city applications. The core idea this paper employs is autonomously replenishing the UAV's deficient network requirements with communications. Via intensive simulations, our proposed algorithm outperforms the state-of-the-art algorithms in terms of surveillance coverage, user support capability, and computational costs.

QoE-Driven Adaptive Deployment Strategy of Multi-UAV Networks Based on Hybrid Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) serve as aerial base stations to provide controlled wireless connections for ground users. Due to their constraints on both mobility and energy consumption, a key problem is how to deploy UAVs adaptively in a geographic area with changing traffic demand of mobile users, while meeting the aforementioned constraints. In this article, we propose a Quality of Experience (QoE)-driven and energy-efficient adaptive deployment strategy for multi-UAV networks based on hybrid deep reinforcement learning (DRL) to solve the problem of incomplete information game, where the UAVs can adjust their moving directions and distance to serve users who move randomly in the target area. Through the hybrid DRL with centralized training and distributed testing, UAVs can be trained offline to obtain the global state information and learn a completely distributed control strategy, with which each UAV only needs to take actions based on its observed state in the real deployment to be fully adaptive. Moreover, in order to improve the speed and effect of learning, we improve hybrid reinforcement learning, by adding genetic algorithms and temporal difference error-based resampling optimization mechanism. The simulation results show that the hybrid DRL algorithm has better efficiency and robustness in multi-UAV control, and has better performance in terms of QoE, energy consumption, and average throughput, by which average throughput can be increased by 20%–60%.

A Leader-Follower Formation Control of Multi-UAVs via an Adaptive Hybrid Controller

The purpose of this study is to offer an adaptive hybrid controller for the formation control of multiple unmanned aerial vehicles (UAVs) leader-follower configurations with communication delay. Although numerous studies about the control of the formation exist, very few incorporate the delay in their model and are adaptive as well. The motivation behind this article is to bridge that gap. The strategy consists of an adaptive fuzzy logic controller and a Proportional, Integral, and Derivative (PID) controller where the logic controller fines/tunes the PID controller gains. The controller also consists of an integrator that raises the order of the system which helps reduce the noise and steady-state errors. The simulations confirm that the proposed technique is robust and satisfies mission requirements. Moreover, the flying formations of the swarm were created by a B-spline curve based on a simple waypoint. The main contribution of this study is to present a model where the formation remains stable during the whole flight, errors are within the optimal range, and the time delays are manageable.

Integrating human experience in deep reinforcement learning for multi-UAV collision detection and avoidance

PurposeThis paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low sample efficiency in DRL and speed up the training. To improve the applicability and reliability of the DRL-based approach in multi-UAV control problems.Design/methodology/approachIn this paper, a fully distributed collision detection and avoidance approach for multi-UAV based on DRL is proposed. A method that integrates human experience into policy training via a human experience-based adviser is proposed. The authors propose a hybrid control method which combines the learning-based policy with traditional model-based control. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the approach.FindingsA fully distributed multi-UAV collision detection and avoidance method based on DRL is realized. The reward curve shows that the training process when integrating human experience is significantly accelerated and the mean episode reward is higher than the pure DRL method. The experimental results show that the DRL method with human experience integration has a significant improvement than the pure DRL method for multi-UAV collision detection and avoidance. Moreover, the safer flight brought by the hybrid control method has also been validated.Originality/valueThe fully distributed architecture is suitable for large-scale unmanned aerial vehicle (UAV) swarms and real applications. The DRL method with human experience integration has significantly accelerated the training compared to the pure DRL method. The proposed hybrid control strategy makes up for the shortcomings of two-dimensional light detection and ranging and other puzzles in applications.

Front-End of Vehicle-Embedded Speech Recognition for Voice-Driven Multi-UAVs Control

For reliable speech recognition, it is necessary to handle the usage environments. In this study, we target voice-driven multi-unmanned aerial vehicles (UAVs) control. Although many studies have introduced several systems for voice-driven UAV control, most have focused on a general speech recognition architecture to control a single UAV. However, for stable voice-controlled driving, it is essential to handle the environmental conditions of UAVs carefully, including environmental noise that deteriorates recognition accuracy, and the operating scheme, e.g., how to direct a target vehicle among multiple UAVs and switch targets using speech commands. To handle these issues, we propose an efficient vehicle-embedded speech recognition front-end for multi-UAV control via voice. First, we propose a noise reduction approach that considers non-stationary noise in outdoor environments. The proposed method improves the conventional minimum mean squared error (MMSE) approach to handle non-stationary noises, e.g., babble and vehicle noises. In addition, we propose a multi-channel voice trigger method that can control multiple UAVs while efficiently directing and switching the target vehicle via speech commands. We evaluated the proposed methods on speech corpora, and the experimental results demonstrate that the proposed methods outperform the conventional approaches. In trigger word detection experiments, our approach yielded approximately 7%, 12%, and 3% relative improvements over spectral subtraction, adaptive comb filtering, and the conventional MMSE, respectively. In addition, the proposed multi-channel voice trigger approach achieved approximately 51% relative improvement over the conventional approach based on a single trigger word.

Research on the Multiagent Joint Proximal Policy Optimization Algorithm Controlling Cooperative Fixed-Wing UAV Obstacle Avoidance.

Multiple unmanned aerial vehicle (UAV) collaboration has great potential. To increase the intelligence and environmental adaptability of multi-UAV control, we study the application of deep reinforcement learning algorithms in the field of multi-UAV cooperative control. Aiming at the problem of a non-stationary environment caused by the change of learning agent strategy in reinforcement learning in a multi-agent environment, the paper presents an improved multiagent reinforcement learning algorithm—the multiagent joint proximal policy optimization (MAJPPO) algorithm with the centralized learning and decentralized execution. This algorithm uses the moving window averaging method to make each agent obtain a centralized state value function, so that the agents can achieve better collaboration. The improved algorithm enhances the collaboration and increases the sum of reward values obtained by the multiagent system. To evaluate the performance of the algorithm, we use the MAJPPO algorithm to complete the task of multi-UAV formation and the crossing of multiple-obstacle environments. To simplify the control complexity of the UAV, we use the six-degree of freedom and 12-state equations of the dynamics model of the UAV with an attitude control loop. The experimental results show that the MAJPPO algorithm has better performance and better environmental adaptability.

Decentralized fractional-order backstepping fault-tolerant control of multi-UAVs against actuator faults and wind effects

Concurrent occurrences of actuator faults and wind effects can significantly threaten the flight safety of multiple unmanned aerial vehicles (multi-UAVs). To address this difficult control problem against actuator faults and wind effects, a composite decentralized fractional-order (FO) backstepping adaptive neural fault-tolerant control (FTC) method is presented for the attitude synchronization tracking of multi-UAVs, which is integrated with neural networks (NNs), disturbance observers (DOs), FO calculus, and high-order sliding-mode differentiators (HOSMDs). The distinctive feature of this work is addressing the attitude synchronization tracking control problem with actuator faults and wind effects in a decentralized framework and proposing a composite approximation method for multi-UAVs. It is shown that by using Lyapunov methods the synchronization tracking control is achieved even when multi-UAVs simultaneously encounter wind effects and actuator faults. Comparative simulation results illustrate the theoretical feasibility.

Fusion algorithm for information interaction control of multi-UAVs based on intelligent algorithm

This paper is devoted to designing a kind of UAV robust information interaction detection and tracking control system suitable for external interference suppression. Firstly, a model UAV rotor is viewed as a Linear Parameter-Varying (LPV) system, take it as an objective to make system design, and consider information interaction detection and isolation scheme through an observer library, to detect and isolate sensor information interaction. Then, improve a kind of existing adaptive variable space algorithm, introduce the algorithm thought into improvement of particle swarm optimisation, and when evolutional generation of population reaches to an integer multiple of a preset period, automatically expand or shrink the size of search space according to improved adaptive variable space algorithm, which automatically seeks for proper search space, improves convergence rate and accuracy, and effectively prevents premature convergence of particle swarm optimisation. Finally, effectiveness of the algorithm is verified through experiment in simulation model.