Motion Capability Analysis for Multiple Fixed-Wing UAV Formations With Speed and Heading Rate Constraints

This manuscript presents an application of the multi-layer control scheme to guide a formation of unmanned aerial vehicles (UAV) in positioning and trajectory tracking missions. In such case, each part of the formation control problem is dealt by an individual layer, which is independent module dealing with a specific part of the navigation problem. These layers are responsible to generate the desired path of the formation, to provide the desired posture of the robots, and to establish the control signal of each robot to reach their desired positions. The formation controller here introduced is able to coordinate the robots to the desired formation, including the possibility of time-varying position and/or shape, while a nonlinear underactuated controller previously proposed is responsible to guide the UAVs to their desired positions. The stability analysis of the closed-loop system is demonstrated in the sense of Lyapunov, resulting that the formation errors are ultimately bounded. In the sequel, a strategy to guide a formation of multiple unmanned aerial vehicles (MUAV) to accomplish positioning and trajectory-tracking tasks is also proposed. Delaunay triangulation is here used to split the platoon of UAVs in N-triangles, which are individually guided by the multi-layer control scheme (MLCS). The advantage of the MLCS in such proposal is the maintenance of the the Jacobian matrix order, regardless of the number of robots in the formation, which reduces the complexity of the control problem. Simulation results demonstrate the ability to accomplish the aforementioned tasks, thus validating the proposal.

This paper proposes an algorithm for cooperative guidance of an unmanned aerial vehicle (UAV) formation following a set of waypoints. Waypoints are linked by straight lines to form a sequence, which effectively tasks the formation to cooperatively follow a rectilinear path between these points. Unlike in earlier known papers, we propose a Fermat’s spiral rather than a circular arc as a fillet to smooth the trajectory. This is because a Fermat’s spiral has zero curvature at its origin, which helps avoid breaks in curvature observable when using straight lines and circular arcs. To make a UAV formation and to follow the path, we propose a novel guidance vector field that is direction- and magnitude-nonuniform. This is where the novelty of this paper lies, as it proposes the vector field method for following a Fermat’s spiral. Known studies used line-of-sight (LOS) guidance to that end. In that case, the UAV formation shapes itself using consensus-based coordination topology, i.e. the group has no leader. To test the algorithm for cooperative waypoint following when smoothing is provided by fillets, we utilized complete nonlinear models of two fixed-wing UAVs, each equipped with a tuned autopilot. Numerical simulation shows the proposed approach efficient.

This paper proposes a method of unmanned aerial vehicle (UAV) formation with connecting devices to take advantage of the aerodynamic advantages of the UAV fleet during close range formation flight. By combining the theory of multi-rigid-body system with the traditional six-degree-of-freedom equations of aircraft, a multi-rigid-body dynamics model of the connected UAV formation is established, which can describe the dynamic characteristics of the UAV formation when the formation changes. On this basis, the attitude control system of the UAV formation consisting of UAVs with only one control surface is designed, and the full-state non-linear numerical simulation model of the UAV formation is built in Simulink. The simulation results validate the rationality of the modeling method, and show that the attitude control system designed can effectively deal with the difficulties caused by the coupling of longitudinal and lateral controls and the complex aerodynamic forces of UAV formation.

This paper addressed a well-documented open problem regarding unmanned aerial vehicles (UAVs) formation. Considering dynamic system features, a novel optimal formation algorithm for UAV system is presented in this paper. The UAV formation optimization problem can be first modeled as a linear quadratic optimization problem based on the analysis of the discrete UAV tracking error dynamic model. Subsequently, in order to achieve the purpose of minimizing the error of the system state including speed and position, a new optimal formation control design method is proposed. Finally, the effectiveness of the algorithm is verified by numerical simulation.

Local and asymmetrical potential field approach to leader tracking problem in rigid formations of fixed-wing UAVs

This paper addressed a well documented open problem on the asymptotic flight-stability of the closed-loop system in the process of the windy field for a three fixed-wing unmanned aerial vehicles (UAVs) formation. The problem of stability can be converted to the analysis of dynamic response and steady-state error. The robust control compared with phononic crystal control method, it can realize the high speed and close cooperative formation of precision combat. Adding phononic crystal is not only capable of reducing the body vibration and steady-state tracking error, but also owns better robustness. The horizontal dynamic analysis of the formation of UAVs is carried out in the presence of external unknown factors. Then, an “integrated” controller is designed to control leader’s speed so that “follower” can track the “leader” in the case of decoupling, while maintaining the consensus of relative speed. The simulation results show that the “integrated” control method integrates the advantages of robust control and phononic crystal vibration attenuation.

The traditional leader-follower Unmanned Aerial Vehicle (UAV) formation cooperative positioning (CP) algorithm, based on relative ranging, requires at least four leader UAV positions to be known accurately, using relative distance with leader UAVs to achieve the unknown position follower UAV’s high-precision positioning. When the number of the known position leader UAVs is limited, the traditional CP algorithm is not applicable. Aiming at the minimum cooperative unit, which consists of a known position leader UAV and an unknown position follower UAV, this paper proposes a CP method based on the follower UAV’s moving vector. Considering the follower UAV can only acquire the single distance with the leader UAV at each distance-sampling period, it is difficult to determine the follower UAV’s spatial location. The follower UAV’s moving vector is used to construct position observation of the follower UAV’s inertial navigation system (INS). High-precision positioning is achieved by combining the follower UAV’s moving vector. In the process of CP, the leader UAV obtains a high-precision position by an INS/Global Positioning System (GPS) loosely integrated navigation system and transmits its position information to the follower UAV. Based on accurate modeling of the follower UAV’s INS, the position, velocity and heading observation equation of the follower UAV’s INS are constructed. The improved extended Kalman filtering is designed to estimate the state vector to improve the follower UAV’s positioning accuracy. In addition, considering that the datalink system based on radio signals may be interfered with by the external environment, it is difficult for the follower UAV to obtain relative distance information from the leader UAV in real time. In this paper, the availability of the relative distance information is judged by a two-state Markov chain. Finally, a real flight test is conducted to validate the performance of the proposed algorithm.

The development of hierarchical structures of unmanned aerial vehicles (UAVs) increases the efficiency of unmanned aerial systems. The grouping of UAVs increases the region of recognition or force of assault. Achieving these requirements is possible through a UAV formation. The UAVs in the formation must be controlled and managed by a commander, but the commander cannot control individual UAVs. The UAVs within the formation have assigned specific individual tasks, so is possible to achieve the flight of the formation with minimum collisions between UAVs and maximized equipment utilization. This paper aims to present a method of formation control for multiple UAVs that allows dynamic changes in the constellations of UAVs. The article includes the results of tests and research conducted in real-world conditions involving a formation capable of adapting its configuration. The results are presented as an element of research for the autonomy swarm, which can be controlled by one pilot/operator. The control of a swarm consisting of many UAVs (several hundred) by one person is now a current problem. The article presents a fragment of research work on high-autonomy UAV swarms. Here is presented a field test that focuses on UAV constellation control.

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.

With the development of technologies related to unmanned vehicles, including unmanned aerial vehicle and unmanned surface vehicle, unmanned vehicles play a pivotal role in maritime management and cruise activities. Meanwhile, lots of evidence shows that a single unmanned vehicle is not qualified for operation under complex environment, whereas the unmanned aerial vehicle–unmanned surface vehicle formation consists of multiple unmanned aerial vehicles and unmanned surface vehicles, which can enhance the performance of the single unmanned vehicle significantly. However, the cooperative operation performance of the unmanned aerial vehicle–unmanned surface vehicle formation is constrained by its communication capacity. Consequently, a cooperative communication framework is designed for the unmanned aerial vehicle–unmanned surface vehicle formation. A distributed dynamic network topology is constructed for fulfilling effective communication for the unmanned aerial vehicle–unmanned surface vehicle formation based on ad hoc network. Experiments are carried out by employing the built unmanned aerial vehicle–unmanned surface vehicle formation platform. Results verify the superiority of the designed cooperative communication framework for the unmanned aerial vehicle–unmanned surface vehicle formation.

Unmanned Aerial Vehicle (UAV) is a new type of flying work tool with the ability to execute tasks. Under strong electromagnetic interference, communication can be carried out by ultraviolet (UV) .Ultraviolet Communication was a communication mode used 200nm~280nm wavelength ultraviolet light as an information carrier. It has the advantage of all-weather operations, non-line-of-sight (NLOS) communication and strong anti- interference. When performing a mission, the UAV formation usually needs to form a fixed formation during the flight to ensure the stability of the link communication network. Moreover, due to the limited energy carried by the UAV, the energy consumption of UAV network should be reduced as much as possible on the basis of maintaining communication in order to maximize the flight time of UAV. The UAVs form persistent formation that need to know the location information of all the UAVs in the formation. When the GPS is not working properly, a kind of two dimensional (2D) localization algorithm which based on the wireless UV ranging method provides relative location information for the UAV formation. This paper proposes a persistent formation generation algorithm for the location information of UAVs that are not shared, the algorithm enables the UAV formation to generate a stable network topology with the least communication links and low energy consumption.

In multiple unmanned aerial vehicle (UAV) formation control systems, high scalability can guarantee the formation stability when UAVs join in or leave the system, and thus improves the robustness of the formation flight. This paper investigates the scalability problem for multi-UAV formation control with double-integrator dynamics. To be more specific, we focus how to build communication links with fixed control parameters such that the formation can always keep stable when adding/removing arbitrary number of UAVs. A bio-inspired method - Veteran Rule is proposed to solve this problem. Compared to the existing methods, our proposed method does not require to re-design or adaptively adjust the control parameters/gains for the changed Laplacian matrix. Furthermore, the convergence rate of the system under the Veteran Rule is analyzed. Surprisingly, the convergence rate of the system reaches the maximum value when all the in-degrees equal a particular value, rather than goes to infinity. Moreover, to guarantee the robustness of the formation system, we study the tolerance on undesired communication links (which break our proposed Veteran Rule). An upper bound for the coupling strength of the undesired communication links is provided by using Gershgorin circle theorem. Finally, simulation results corroborate the effectiveness of our results.

A new consensus theory-based method for formation control and obstacle avoidance of UAVs

For an Unmanned Aerial Vehicle (UAV) formation in “leader-follower” mode, a bran-new concept of nonlinear elastic distance vector is proposed, which helps achieve much more excellent control effect. Based on the nonlinear elastic distance vector, through a big mountain of mathematical work the mathematical model of UAV formation flight is built and linearized skillfully. A new model of UAV formation flight is obtained, which has distinct physical meaning and simple form. And on this basis, by introducing some simplification and symmetry of feedback gain, a fire-new high-efficiency distributed controller of elastic formation is designed. Even better, it expends munch small control cost while providing such better control effect. Then the stability of the controller is proved subtly and a very important theorem offers sufficient conditions for the stability of the model is proposed. Simulation results show that controller can keep the formation much more effectively. In addition, the control quantity is only related to two elements: One is the speed of controlled vehicle itself, the other is the relative distance between controlled vehicle and its neighbor. For this reason, the control problem becomes more simple.

In this study, the time‐varying formation control problem for the second‐order discrete‐time multi‐agent systems is investigated, where both the non‐uniform communication time‐delays and switching topology are taken into account. A linear discrete‐time formation protocol is developed based on the neighbouring relative position and velocity information. Using the state transformation method and properties of the stochastic matrix, the formation feasibility condition is given and sufficient conditions for the discrete‐time multi‐agent systems to accomplish the time‐varying formation are established. An unmanned aerial vehicles (UAVs) formation experiment platform is constructed. Using four quadrotor UAVs, UAV formation flying experiments are performed to verify the effectiveness and reliability of the discrete‐time formation protocol. The experimental results show that the theoretical results can be used to deal with the time‐varying formation control problem for multiple UAVs system with communication delays and switching topology.