Multiple Fixed-wing Unmanned Aerial Vehicles Research Articles

Minimum time search using ant colony optimization for multiple fixed-wing UAVs in dynamic environments

The use of multiple fixed-wing unmanned aerial vehicles in search and rescue missions after natural disasters has become of great interest as they can search large areas and find survivors as quickly as possible. This paper discusses a minimum time cooperative search scheme that utilizes ant colony optimization and new heuristic functions to tackle various constraints in a dynamic environment. The study makes novel use of Dubins curves in the heuristic functions to consider the kinematic limitations of fixed-wing UAVs when planning tangent continuity paths. Furthermore, a novel probabilistic approach is introduced to model the uncertainties induced by dynamic obstacles and determine optimal search paths that are safe and practical in a grid search environment. The performance of the proposed search algorithm is tested through two-dimensional and three-dimensional simulations, statistical analysis, and comparison with other well-known optimization algorithms. To randomize the simulated cooperative search, different search scenarios with static and dynamic obstacles are run several times.

Cooperative Standoff Target Tracking using Multiple Fixed-Wing UAVs with Input Constraints in Unknown Wind

This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate moving target tracking in the presence of unknown background wind, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on a Lyapunov guidance vector field is proposed. The convergence analysis shows that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through a rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on the heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller meets the requirements of the airspeed constraint. Furthermore, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit, which is defined by the Lyapunov guidance vector field, to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results indicate that the temporal-phase-based speed controller can achieve a fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits a fast response speed and high estimation accuracy.

Distributed Adaptive Path-Following Control for Distance-Based Formation of Fixed-Wing UAVs under Input Saturation

This paper investigates the distance-based formation and cooperative path-following control problems for multiple fixed-wing unmanned aerial vehicles (UAVs). In this study, we design the distance-based formation control structure to achieve the virtual leader and followers pre-defined rigid formation pattern, ensuring simultaneously relative localization. A path-following control strategy based on adaptive dynamic surface and neural network control technology is proposed to approximate the uncertain disturbances of the environment and unmodeled dynamics. And the longitudinal and lateral subsystems’ adaptive fault-tolerant controllers are designed, respectively, to achieve the fault-tolerant control of UAVs’ formation in three-dimensional environments. Furthermore, the adaptive sliding mode controller with an auxiliary controller is designed to realize the UAVs path following with limited input saturation. Finally, simulation examples are given to clarify and verify the effectiveness of the theoretical results.

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.

Refined fault tolerant tracking control of fixed-wing UAVs via fractional calculus and interval type-2 fuzzy neural network under event-triggered communication

The refined fault tolerant tracking control (FTTC) scheme is developed for multiple fixed-wing unmanned aerial vehicles (UAVs) against actuator faults and wind effects under event-triggered communication. To decrease the communication overload caused by the continuous information transmissions among neighboring UAVs, an event-triggered communication mechanism is incorporated to dynamically reduce and increase the triggering frequency at the pre-fault and post-fault phases, respectively, thus reducing the communication burden while ensuring the synchronization tracking performance of faulty UAVs. Moreover, refined FTTC performance with event-triggered communications can be achieved by skillfully invoking fractional-order calculus. Furthermore, interval type-2 fuzzy neural networks with fractional-order adaptive modulation factors are constructed to compensate for the unknown nonlinearities. Lyapunov stability analysis has revealed that all attitudes can synchronously track their references. Simulation results are presented to demonstrate that communication resource-saving refined FTTC performance can be guaranteed with different fractional-order operators.

Adversarial Swarm Defence Using Multiple Fixed-Wing Unmanned Aerial Vehicles

This article proposes a coverage-based adversarial swarm defence algorithm. The defender swarm composed of fixed-wing unmanned aerial vehicles (UAVs) is assumed to have explosives onboard to intercept an adversarial swarm. The proposed approach consists of the following two steps: first, impact point optimization and, second, model predictive control (MPC)-based impact time control. The impact point optimization periodically optimizes impact points for the corresponding UAVs to maximize the coverage within the hostile swarm while minimizing the common impact time. The optimization domain is limited to a physically reachable area of UAVs with the common impact time. Besides, the MPC-based impact time controller is designed to ensure the multiple UAVs to arrive the generated time-varying impact points simultaneously. Numerical simulations are performed to prove the feasibility and efficiency of the proposed swarm defence algorithm.

Reconnaissance and Confirmation Task Planning of Multiple Fixed-Wing UAVs with Specific Payloads: A Comparison Study

In this study, the reconnaissance and confirmation task planning of multiple fixed-wing unmanned aerial vehicles (UAV) with specific payloads, which is an NP-hard problem with strong constraints and mixed variables, is decomposed into two subproblems, task allocation with “payload-target” matching constraints, and fast path planning of the UAV group, for which two mathematical models are respectively established. A bi-layer collaborative solution framework is also proposed. The outer layer optimizes the allocation scheme between the UAVs and targets, whereas the inner layer generates the UAV path and evaluates the outer scheme. In the outer layer, a unified encoding based on the grouping and pairing relationship between UAVs and targets is proposed. The corresponding combinatorial mutation operators are then designed for the representative NSGA-II, MOEA/D-AWA, and DMOEA-ϵC algorithms. In the inner layer, an efficient heuristic algorithm is used to solve the path planning of each UAV group. The simulation results verify the effectiveness of the cooperative bi-layer solution scheme and the combined mutation operators. At the same time, compared with the NSGA-II and MOEA/D-AWA, DMOEA-ϵC can obtain a significantly better Pareto front and can weigh the assigned number of UAVs and the total task completion time to generate more diversified reconnaissance confirmation execution schemes.

Flight Test of Autonomous Formation Management for Multiple Fixed-Wing UAVs Based on Missile Parallel Method

This paper reports on the formation and transformation of multiple fixed-wing unmanned aerial vehicles (UAVs) in three-dimensional space. A cooperative guidance law based on the classic missile-type parallel-approach method is designed for the multi-UAV formation control problem. Additionally, formation transformation strategies for multi-UAV autonomous assembly, disbandment, and special circumstances are formed, effective for managing and controlling the formation. When formulating the management strategy for formation establishment, its process is divided into three steps: (i) selecting and allocating target points, (ii) forming loose formations, and (iii) forming short-range formations. The management of disbanding the formation is formulated through reverse thinking: the assembly process is split and recombined in reverse, and a formation disbanding strategy that can achieve a smooth transition from close to lose formation is proposed. Additionally, a strategy is given for adjusting the formation transformation in special cases, and the formation adjustment is completed using the adjacency matrix. Finally, a hardware-in-the-loop simulation and measured flight verification using a simulator show the practicality of the guidance law in meeting the control requirements of UAV formation flight for specific flight tasks.

UAV Deployment and IoT Device Association for Energy-Efficient Data-Gathering in Fixed-Wing Multi-UAV Networks

This work studies the deployment of multiple fixed-wing unmanned aerial vehicles (UAVs) for data-gathering from ground IoT devices and the corresponding device association policy. Each UAV is assumed to follow a circular flight trajectory above its responsible network area in order to stay afloat. The device association and the UAVs’ trajectory centers and radii are jointly optimized to maximize the total energy savings of the devices. Given the trajectory centers and radii, the device association problem is modeled as a multiple 0–1 knapsack problem, and solved by a two-stage maximum energy-saving (MES) device association policy. Moreover, given the device association, the UAVs’ trajectory centers and radii are optimized by an iterative load-balancing (ILB) algorithm, where the trajectory centers are chosen as a load-dependent weighted sum of their associated devices’ locations. Furthermore, we also propose a collision-free scheduling policy that minimizes the total phase offset between the actual and ideal transmission phases of all devices, and a modified MES algorithm that provides higher association priority to devices whose data gathered by the UAV is more outdated. Simulation results show that the proposed MES and ILB algorithms outperform candidate schemes in terms of the total energy savings of IoT devices.

Hybrid Dec-POMDP/PID Guidance System for Formation Flight of Multiple UAVs

This paper proposes a guidance system with a new hybrid architecture for multiple fixed-wing unmanned aerial vehicles (UAVs) formation flight. The proposed architecture is hybrid in the sense of combining two control policies found in guidance systems: the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) policy and the well-known PID controller. The Dec-POMDP policy part enables every UAV to avoid collision with any other UAV from the formation. The PID part is activated for regions far from the collision risk zone and is implemented in such a way that each UAV is completely controlled by only its own data, independent of its neighbors. The major novelty of the proposed system, compared to other hybrid approaches, is its decentralized nature which favors overcoming the usual problems that come with centralization such as the high dependence on a central decision node, a leader, or a ground station. Inherited from both parts, the overall system performance is robust in noisy and uncertain environments. Simulations were extensively performed to show the effectiveness of the proposed guidance system in distinct scenarios.

Fault-Tolerant Time-Varying Elliptical Formation Control of Multiple Fixed-Wing UAVs for Cooperative Forest Fire Monitoring

This paper investigates the cooperative forest fire monitoring problem of multiple fixed-wing unmanned aerial vehicles (UAVs) in the presence of actuator faults during the fire monitoring mission. By using the fractional-order sliding-mode control strategy, a fault-tolerant time-varying elliptical formation control scheme is developed for multiple UAVs to monitor the elliptical spread of forest fire. To estimate the lumped disturbances induced by the external disturbances and actuator faults, sliding-mode disturbance observers are developed by introducing reference systems and sliding-mode differentiators. It is proved that all fixed-wing UAVs can be steered to elliptically monitor the forest fire and the cooperative tracking errors are uniformly ultimately bounded. Simulation results have demonstrated the effectiveness of the proposed control scheme.

Collaborative Target-Tracking Control Using Multiple Fixed-Wing Unmanned Aerial Vehicles with Constant Speeds

This paper considers a collaborative tracking control problem using a group of fixed-wing unmanned aerial vehicles (UAVs) with constant and nonidentical speeds. The dynamics of fixed-wing UAVs are modeled by unicycle-type equations with nonholonomic constraints, assuming that UAVs fly at constant altitudes in the nominal operation mode. The controller is designed such that all fixed-wing UAVs as a group can collaboratively track a desired target’s position and velocity. This paper first presents conditions on the relative speeds of tracking UAVs and the target to ensure that the tracking objective can be achieved when UAVs are subject to constant-speed constraints. A reference velocity is constructed that includes both the target’s velocity and position as feedback, which is to be tracked by the group centroid. In this way, all vehicles’ headings are controlled such that the group centroid follows a reference trajectory that successfully tracks the target’s trajectory. A spacing controller is further devised to ensure that all vehicles stay close to the group centroid trajectory. Tradeoffs in the controller design and performance limitations of the target tracking control due to the constant-speed constraint are also discussed in detail. Experimental results with three fixed-wing UAVs tracking a target rotorcraft are provided.

Obstacle avoidance in V-shape formation flight of multiple fixed-wing UAVs using variable repulsive circles

ABSTRACTA formation control and obstacle avoidance algorithm has been introduced in this paper for the V-shape formation flight of fixed-wing UAVs (Unmanned Aerial Vehicles) using the potential functions method. An innovative vector approach has been suggested to fix the conventional challenge in employing the artificial potential field (APF) approach (the creation of local minimums). A method called variable repulsive circles (VRC) has been then presented aimed at designing proper flight paths tailored with functional limitations of fixed-wing UAVs in facing obstacles. Finally, the efficiency of the designed algorithm has been examined and evaluated for different flight scenarios.

Distributed Cooperative Path Planning for Tracking Ground Moving Target by Multiple Fixed-wing UAVs via DMPC-GVD in Urban Environment

In this paper, a real-time distributed path planning method is developed for cooperatively tracking ground moving target in urban by multiple fixed-wing unmanned aerial vehicles (UAVs). For reasons of changeable movement of target, the commanded speed and turning rate of each UAV are both taken as control input variables. In urban environment, buildings may occlude the line of sight of on-board sensor. Hence the target coverage degree is proposed as objective function instead of distance. To save energy of UAV system as much as possible, the control input cost and sensor energy consumption are also taken as objectives. For preemptive priority requirement, the objective functions are fuzzified and the satisfactory degree order is designed to model priority. To guarantee the feasibility of solution, the varying domain is introduced to replace the strict order constraint. On this basis, generalized varying domain (GVD) method is developed to balance optimization and priority. In terms of the maneuverability of UAVs, the diverse constraints are considered, including real speed and turning rate, control input saturation, collision avoidance between UAVs, and obstacle avoidance between UAV and buildings. Consequently, distributed model predictive control (DMPC) strategy is designed to calculate the optimal path of each UAV, where the state information in finite period of UAV is transferred to the adjacent ones. The simulations show the effectiveness of proposed method by comparing with hierarchical optimization (HO).

Cooperative Path Following Control of Fixed-wing Unmanned Aerial Vehicles with Collision Avoidance

In this paper, we propose a novel curved path following scheme for multiple fixed-wing unmanned aerial vehicles (UAVs) that can achieve coordinated curved path following and handle collision avoidance simultaneously. The proposed solution is a hybrid system that combines a path following strategy and a collision avoidance method to ensure collision-free maneuvering of a group of UAVs cooperative flight. A strategy based on a virtual structure and a kinematic model is proposed to derive the cooperative curved path following. Meanwhile, a possible collision will occur when multi-UAVs flight in the form of dense formations, or there are path intersections when the formation is changed. However, all fixed-wing vehicles need to maintain a minimum airspeed and cannot stop before the collision. Therefore, there is a need for a fast and efficient method that can be implemented online, taking into account the physical limitations of the vehicle (minimum speed, turning rate, etc.) to avoid possible collision. We employ a modified vector field histogram (VFH) method to provide real-time collision avoidance, and evasive maneuvers work only if the distance is within the conflict zone. In order to verify the proposed control scheme, dense formation flight tests of small fixed-wing UAVs were implemented.

Leader-Following Consensus Control for Multiple Fixed-Wing UAVs’ Attitude System With Time Delays and External Disturbances

This paper proposes an algorithm of leader-following consensus control of multiple fixed-wing unmanned aerial vehicles’(UAVs) attitudes with time delays and unknown external disturbances. Firstly, a distributed controller based on undirected graph for leader-following consensus control of multi-UAVs’ attitudes is proposed. Then, the effects of time delays are compensated with the help of Lyapunov-Krasovskii function, Cauchy and Young’s inequality. Secondly, RBF neural network is introduced to estimate and reduce the negative effects of external disturbances. Finally, the effectiveness of the proposed distributed controller is demonstrated by Lyapunov theory and simulations.

Cooperative moving path following for multiple fixed-wing unmanned aerial vehicles with speed constraints

This paper is to address a cooperative moving path following (CMPF) problem, in which a fleet of fixed-wing unmanned aerial vehicles (UAVs) are required to converge to and follow a desired geometric moving path while satisfying prespecified speed and spatial constraints. A representative application of the CMPF problem is the challenging mission scenario where a group of UAVs are tasked to track a moving ground target. The proposed methodology is based on the insight that a vehicle can follow a given path only through attitude control, thus leaving its speed as an extra input to be used at the coordination level. To deal with moving path following (MPF) of a single UAV, a non-singular control law is derived to steer the vehicle along the desired moving path which avoids the singularity problem in the previous MPF strategy. For multi-UAV coordination, a pursuit strategy is employed with the introduction of a virtual leader. To account for speed constraints and collision avoidance, conditions are derived under which the combined MPF and multi-UAV coordination closed-loop system is asymptotically stable while speed and spatial constraints are satisfied. Further simulation has been performed to demonstrate the effectiveness of the proposed method.

Decentralized Multi-UAV Flight Autonomy for Moving Convoys Search and Track

This brief is concerned with integrated autonomous takeoff, target search, task assignment, and tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) in urban environments. The problem is to design flight autonomy that is embedded onboard each UAV to enable autonomous flight coordination and distributed tasking. Control logic design based on a finite state automaton model, integrating four modes of operations, namely, the takeoff mode, the fly-to-area of operation mode, the search mode, and the tracking mode, is presented. Different from the state-of-the-art of recent research, this brief provides a preliminary research on the autonomous cooperative takeoff for miniature fixed-wing UAVs, by considering collision avoidance, communication failure, etc. To make UAVs autonomously and cooperatively search roads in the urban environments, an efficient improved search algorithm is proposed based on recent research on the coverage search in the literature. For the target tracking, using geometric relations (relative position, orientations, speed ratio, and minimal turning radius), a systematic algorithm is developed to generate an optimal online path. All the algorithms in this work are developed based on realistic miniature fixed-wing UAV dynamic models. The main focus of the brief is to test the developed control logic and also the algorithms. The proposed framework is evaluated by our 3-D multi-UAV test bed.

Cooperative Tracking a Moving Target Using Multiple Fixed-wing UAVs

A cooperative tracking scheme is presented in this paper for multiple fixed-wing unmanned aerial vehicles (UAVs) to track an uncooperative, moving target. It is comprised of a target loitering algorithm and a formation flight algorithm. The loitering algorithm enables a constant speed UAV to circle around a moving target, whose speed is allowed to vary up to the UAV's speed. The formation algorithm enables cooperative tracking using multiple UAVs by keeping them flying in a circular formation with equal inter-vehicle angular separation. Under this formation algorithm, the formation center can be controlled independently to perform target loitering, and the admissible range of the target's speed would not be affected for given UAVs. The performance of the proposed tracking system is verified in numerical simulations.

Simultaneous arrival of multiple UAVs under imperfect communication

PurposeThe purpose of this paper is to propose a new algorithm for in‐mission trajectories and speed adjustment of multiple unmanned aerial vehicles (UAVs) participating in a mission that requires them to arrive at target location simultaneously with switching and imperfect communication among the vehicles.Design/methodology/approachThis algorithm, programmed at each UAV level, is based on the repeated consensus seeking among the participating vehicles about the time‐on‐target (ToT) through an imperfect inter‐vehicle communication link. The vehicles exchange their individual ToT values repeatedly for a particular duration to pick the highest value among all the vehicles in communication. A consensus confidence flag is set high when consensus is successful. After every consensus cycle with high confidence value, the mission adjustment is carried out by computing difference value between ToT consensus and a threshold value. For the difference values higher than a certain limit, vehicle's trajectory is adjusted by in‐mission insertion of new waypoint (WP) and for lower values the vehicle's speed is varied under allowable limits. The consensus seeking followed by the mission adjustment is repeated periodically to quash the imperfect communication effects.FindingsA mathematical analysis has been carried out to establish the conditions for convergence of the algorithm. The simultaneous arrival of the vehicles subjected to switching communication is achieved only when the union of the switching links during the consensus period enables a vehicle to receive information from all the other vehicles and the switching rate is sufficiently high. This algorithm has been tested in a 6‐degree‐of‐freedom (DoF) multiple UAV simulation environment and achieves simultaneous arrival of multiple fixed wing UAVs under imperfect communication links that meets the aforementioned conditions.Research limitations/implicationsThe presented algorithm and design strategy can be extended for other types of cooperative control missions where certain variable of interest is shared among all the vehicles over imperfect communication environment. The design is modular in functionality and can be incorporated into existing vehicles or simulations.Originality/valueThis research presents a new consensus algorithm that repeatedly performs polling of ToT among the vehicles through intermittent communication. The continual nature of consensus seeking covers the weakness of the imperfect communication. A two‐level mission adjustment provides better accuracy in simultaneous arrival at the target location.