Close Formation Control Research Articles

Distributed observer-based close formation control for UAV swarm under outside disturbances and wake interferences

This work investigates the issue on distributed close formation tracking control for a class of fixed-wing UAVs under prescribed performance, outside disturbances, and wake interferences. Firstly, by considering the existence of uncertain terms, a second-order kinematical model is established for each UAV system. Secondly, in order to ensure the safe flight of each UAV, an improved prescribed performance method is proposed to constrain the tracking error within the given restraint. Thirdly, based on the exogenous models of the outside disturbance and wake interference, two kinds of distributed disturbance observers are respectively presented to derive their estimations, in which the approximations of the uncertainties are included. Fourthly, according to the safe distance, estimations of the disturbances and uncertainties, and tracking error, a distributed cooperative controller is designed and an overall closed-loop system is deduced on the basis of Kronecker product. Then, a sufficient condition is established to ensure the asymptotical stability for the close-loop system and later, an easy-to-test method of checking the controller gains, observer ones, and adaptive law is presented. Finally, the effectiveness and superiorities of the proposed control strategy are verified by resorting to a simulated example.

Reinforcement learning based UAV formation control in GPS-denied environment

Highly accurate positioning is a crucial prerequisite of multi Unmanned Aerial Vehicle close-formation flight for target tracking, formation keeping, and collision avoidance. Although the position of a UAV can be obtained through the Global Positioning System (GPS), it is difficult for a UAV to obtain highly accurate positioning data in a GPS-denied environment (e.g., a GPS jamming area, suburb, urban canyon, or mountain area); this may cause it to miss a tracking target or collide with another UAV. In particular, UAV close-formation control in GPS-denied environments faces difficulties owing to the low-accuracy position, close distance between vehicles, and nonholonomic dynamics of a UAV. In this paper, on the one hand, we develop an innovative UAV formation localization method to address the formation localization issues in GPS-denied environments; on the other hand, we design a novel reinforcement learning based algorithm to achieve the high-efficiency and robust performance of the controller. First, a novel Lidar-based localization algorithm is developed to measure the localization of each aircraft in the formation flight. In our solution, each UAV is equipped with Lidar as the position measurement sensor instead of the GPS module. The k-means algorithm is implemented to calculate the center point position of UAV. A novel formation position vector matching method is proposed to match center points with UAVs in the formation and estimate their position information. Second, a reinforcement learning based UAV formation control algorithm is developed by selecting the optimal policy to control UAV swarm to start and keep flying in a close formation of a specific geometry. Third, the innovative collision risk evaluation module is proposed to address the collision-free issues in the formation group. Finally, a novel experience replay method is also provided in this paper to enhance the learning efficiency. Experimental results validate the accuracy, effectiveness, and robustness of the proposed scheme.

Robust Control for UAV Close Formation Using LADRC via Sine-Powered Pigeon-Inspired Optimization

This paper designs a robust close-formation control system with dynamic estimation and compensation to advance unmanned aerial vehicle (UAV) close-formation flights to an engineer-implementation level. To characterize the wake vortex effect and analyze the sweet spot, a continuous horseshoe vortex method with high estimation accuracy is employed to model the wake vortex. The close-formation control system will be implemented in the trailing UAV to steer it to the sweet spot and hold its position. Considering the dynamic characteristics of the trailing UAV, the designed control system is divided into three control subsystems for the longitudinal, altitude, and lateral channels. Using linear active-disturbance rejection control (LADRC), the control subsystem of each channel is composed of two cascaded first-order LADRC controllers. One is responsible for the outer-loop position control and the other is used to stabilize the inner-loop attitude. This control system scheme can significantly reduce the coupling effects between channels and effectively suppress the transmission of disturbances caused by the wake vortex effect. Due to the cascade structure of the control subsystem, the correlation among the control parameters is very high. Therefore, sine-powered pigeon-inspired optimization is proposed to optimize the control parameters for the control subsystem of each channel. The simulation results for two UAV close formations show that the designed control system can achieve stable and robust dynamic performance within the expected error range to maximize the aerodynamic benefits for a trailing UAV.

Reinforcement learning-based close formation control for underactuated surface vehicle with prescribed performance and time-varying state constraints

This paper studies close formation control problem with prescribed performance and time-varying state constraints for a group of 4-degrees-of-freedom (DOF) underactuated surface vehicles (USVs) subject to actuator faults, input saturation and input delay. A finite-time sliding mode control (SMC) scheme based on reinforcement learning (RL) algorithm is introduced to guarantee prescribed formation performance without violating velocity error constraints. By using actor-critic neural network (NN)-based RL algorithm, the actuator faults and system uncertainties are accurately estimated. Afterwards, an exponential decreasing boundary function is developed to suppress overshoot more reasonably, and a novel mechanism of switching gain is given to alleviate chattering inherent in SMC while the RL-based compensation term is constructed to handle the formation accuracy problem caused by the reduced switching gain. Besides, auxiliary nonlinear continuous function and Pade approximation have been successfully applied to process actuator saturation and input delay, respectively. Numerical simulations and experimental results are exhibited to verify the effectiveness and superior formation performance of the proposed control method.

A review on fault-tolerant cooperative control of multiple unmanned aerial vehicles

This paper presents the recent developments in Fault-Tolerant Cooperative Control (FTCC) of multiple unmanned aerial vehicles (multi-UAVs). To facilitate the analyses of FTCC methods for multi-UAVs, the formation control strategies under fault-free flight conditions of multi-UAVs are first summarized and analyzed, including the leader-following, behavior-based, virtual structure, collision avoidance, algebraic graph-based, and close formation control methods, which are viewed as the cooperative control methods for multi-UAVs at the pre-fault stage. Then, by considering the various faults encountered by the multi-UAVs, the state-of-the-art developments on individual, leader-following, and distributed FTCC schemes for multi-UAVs are reviewed in detail. Finally, conclusions and challenging issues towards future developments are presented.

Leader–follower close formation control for underactuated surface vessel via terminal hierarchical sliding mode

This article is concerned with the close formation problem of multiple underactuated surface vessels in the presence of model uncertainties, roll motion, and environmental disturbances. To effectively address these issues, a novel control scheme considering roll stabilization is designed by combing terminal hierarchical sliding mode control with Lyapunov direct method, which can quickly ensure a small formation error in a finite-time for vessels. Meanwhile, a new switching gain adaptation mechanism is utilized to reduce chattering and acquire faster adaptive rate without the excessive temporary tracking errors. Radial basis function neural network and finite-time observer are employed to deal with model uncertainties and disturbances, respectively. Furthermore, dynamic surface control technology is introduced to reduce the complexity of control law. Various simulations and comparison results are conducted to verify the effectiveness of theoretical results.

Charged-Spacecraft Formation: Concept, Deployment and Coulomb-Force Control

Proximate spacecraft formation flying has many applications in high accurate earth observation and astronomy. In comparison to conventional or electronic thrust, Coulomb thrust has obvious advantages in close-formation control, such as fast throttling, nearly propellantless features and no thruster plume impingement. This paper presents the concept of charged-spacecraft formation and deployment, also investigates the Coulomb-force control for desired spacecraft formations. It is assumed that the charged chief spacecraft has several controllable charged spheres distributed around it and deputies are charged spheres. The deputies are deployed from the chief spacecraft to the desired formation orbits under the active charge control. In order to deploy these deputies subject to the constraints on the limited controllable charges, the transition trajectories are planned by using pseudo-spectral discretization method. Then a charge feedback controller is designed to track the transition trajectories and the desired formation. Numerical simulation results show that one or more deputies can be deployed by controlling the limited charge of the chief spacecraft.

UDE-Based Robust Command Filtered Backstepping Control for Close Formation Flight

This paper presents an uncertainty and disturbance estimator (UDE)-based nonlinear robust controller for close formation flight in light of the command filtered backstepping technique. To maximize the formation aerodynamic benefits at different flight maneuvers, the formation geometry is described in the wind frame of the leader aircraft. A novel nonlinear robust close formation control algorithm with a two-degree-of-freedom nature is developed. The command filtered backstepping technique is employed to design the baseline formation controller, whereas the UDE is introduced to enhance the robustness of the baseline formation control. The proposed control law can lead to desired tracking performance for the close formation flight under different maneuvers without using the acceleration information of the leader aircraft, asymptotical stability for the close formation flight at a level and straight flight with constant speeds, and enough robustness against the formation aerodynamic effects by purely observing system states and inputs. Numerical simulations are performed to show the feasibility and efficiency of the proposed controller.

Multiple UAV distributed close formation control based on in-flight leadership hierarchies of pigeon flocks

Owing to the similarity between bird flocks and unmanned aerial vehicle (UAV) swarms among intelligence, autonomy and local interactions, the collective motion mechanisms in bird flocks have reference significance for the design of distributed control algorithms for multiple UAVs. In this paper, a pigeon flocking model is proposed based on the in-flight leadership hierarchy in pigeon flocks. Graph theory is adopted to describe the transformation process from an arbitrary connected topology to the leadership hierarchy required for a line formation. The leader–follower relations are defined as the guidance from leaders to followers on the relative positions. On the basis of the pigeon flocking model, a distributed close formation control method to coordinate multiple UAVs to fly in a line close formation is presented. The simulation results based on the UAV close formation model, modified by the additional nonlinear aerodynamic interactions, are elaborated to show the feasibility and validity of our proposed method.

Unmanned aerial vehicle close formation control based on the behavior mechanism in wild geese

This paper focuses on the study of multiple unmanned aerial vehicle (UAV) biomimetic formation control problem. It shows that the wild geese formation can save energy by analysing the behavior in the migration of wild geese. The bionic mechanism of multiple UAV formation has been discussed, and a formation keeping and toplogy changing controller based on the wild geese behavior mechanism has been designed. Simulation results are presented to show that the UAVs can form a stable flight formation due to the effect of the formation controller. With the model of the fuel consumption, the designed fomation can use the less fuel. The simulation verified the validity and stability of UAV close fomation control based on the wild geese behavior mechanism.

Receding horizon control for multi-UAVs close formation control based on differential evolution

Close formation flight is one of the most complicated problems on multi-uninhabited aerial vehicles (UAVs) coordinated control. Based on the nonlinear model of multi-UAVs close formation, a novel type of control strategy of using hybrid receding horizon control (RHC) and differential evolution algorithm is proposed. The issue of multi-UAVs close formation is transformed into several on-line optimization problems at a series of receding horizons, while the differential evolution algorithm is adopted to optimize control sequences at each receding horizon. Then, based on the Markov chain model, the convergence of differential evolution is proved. The working process of RHC controller is presented in detail, and the stability of close formation controller is also analyzed. Finally, three simulation experiments are performed, and the simulation results show the feasibility and validity of our proposed control algorithm.

Nonlinear adaptive close formation control of unmanned aerial vehicles

This paper treats the question of formationflight control of multiple unmanned aerial vehicles (UAVs). Inclose formation the wing UAV motion is affected by the vortexof the adjacent lead aircraft. The forces produced by these vorticesare complex functions of the relative position coordinates ofthe UAVs. In this paper, these forces are treated as unknownfunctions. For simplicity, it is assumed that the UAVs have autopilotsfor heading-, altitude-, and Mach-hold in the inner loops. Anadaptive control law is derived for the position control of thewing aircraft based on a backstepping design technique. In theclosed-loop system, commanded separation trajectories are asymptoticallytracked by each wing aircraft while the lead UAV is maneuvering.It is seen that an overparametrization in the design is essentialfor the decentralization of the control system. These resultsare applied to formation flight control of two UAVs and simulationresults are obtained. These results show that the wing UAV followsprecisely the reference separation trajectories in spite of theuncertainties in the aerodynamic coefficients, while the leadaircraft maneuvers.