Multiple Quadrotor Unmanned Aerial Vehicles Research Articles

Adaptive neural network based quadrotor UAV formation control under external disturbances

The formation control of a team comprised of multiple quadrotor Unmanned Aerial Vehicles (UAVs) may severely be affected by the unknown external disturbances. The external disturbances are caused by wind forces to create aero-dynamical disturbances. This article addresses the robust formation control problem of multiple UAVs system despite the effect of external disturbances that allow sustaining a stable network connection among the UAVs and maintaining different formations assigned to them. First, a Radial Basis Function Neural Network (RBFNN) based model is developed to reciprocate the external disturbances along the positional and the attitude subsystems. Then incorporating the estimated disturbance values a distributed adaptive formation controller is devised using the Lyapunov theory. It consists of a positional and an attitude controller associated with the translational and the rotational movements of the UAVs. The stability is validated by satisfying the criteria of the Lyapunov stability function. The UAVs are connected through variable adjacency matrix based directed network topology and the network connectivity is established through the properties of the Laplacian Matrix. The robustness of the designed controller is justified via rigorous simulation studies for different sets of desired formations such as triangular, squared, tetrahedron, octahedron and cube shaped. The reference trajectories are considered as spiral, straight line and circular shaped. The time varying external disturbances are considered of sinusoidal waveform of different magnitudes. The simulation results signifies that the proposed RBFNN based formation controller reciprocate different sinusoidal waveforms to achieve the desired formations successfully. Extensive comparative studies demonstrate the efficacy of the proposed adaptive formation controller over the existing controllers presented in the literature for different shapes of trajectories and desired formations.

Multi quadrotors coverage optimization using reinforcement learning with negotiation

<p>This paper proposes an optimization scheme to maximize the area coverage of multiple quadrotor unmanned aerial vehicles that are deployed to monitor an operational area/space. Each quadrotor initially performs a single agent reinforcement learning to determine target points with optimal coverage area. Whenever each quadrotor encounters the others within a predetermined negotiation region that is defined by an inter-agent distance threshold, it will activate a multiagent reinforcement learning with action negotiation algorithm and coordinate its movement policies to maximize the total coverage area and avoids inter-agent coverage overlaps. Results of simulation evaluations are shown to illustrate the performance of the proposed learning-based coverage optimization method.</p>

Barrier Lyapunov function‐based nonlinear cooperative control approach for dual‐quadrotor slung load system

AbstractDue to the limited payload capacity of single‐quadrotor unmanned aerial vehicles (UAVs) in cargo transportation, there is a pressing need for the collaborative transportation of heavy goods using multiple quadrotor UAVs. In this context, the dual‐quadrotor aerial transportation system stands out for its heightened research significance, driven by its stringent requirements for controller swing reduction performance. In practical transportation scenarios, uncertainties such as disturbances and emergency avoidance constraints elevate the risk associated with excessive variations in the relative distances between quadrotor aircraft during collaborative transportation. Building upon the established nonlinear model by D'Alembert, this paper proposes a nonlinear coordinated control strategy based on the barrier Lyapunov function. The strategy employs an innovative energy storage approach, incorporating cooperative error constraints to effectively restrict relative errors between quadrotor aircraft, thereby mitigating the risk of collisions. Furthermore, this control strategy facilitates rapid convergence of the positions of quadrotor aircraft and the swing angles of suspended loads. Theoretical analyses, grounded in LaSalle's invariance theorem, provide evidence of the stability of the closed‐loop system. Finally, comparative assessments conducted using the Matlab/Simulink visualization platform validate the superior control performance of the designed controller in collaborative transportation scenarios.

Distributed Optimal Attitude Synchronization Control of Multiple QUAVs via Adaptive Dynamic Programming.

This article proposes a distributed optimal attitude synchronization control strategy for multiple quadrotor unmanned aerial vehicles (QUAVs) through the adaptive dynamic programming (ADP) algorithm. The attitude systems of QUAVs are modeled as affine nominal systems subject to parameter uncertainties and external disturbances. Considering attitude constraints in complex flying environments, a one-to-one mapping technique is utilized to transform the constrained systems into equivalent unconstrained systems. An improved nonquadratic cost function is constructed for each QUAV, which reflects the requirements of robustness and the constraints of control input simultaneously. To overcome the issue that the persistence of excitation (PE) condition is difficult to meet, a novel tuning rule of critic neural network (NN) weights is developed via the concurrent learning (CL) technique. In terms of the Lyapunov stability theorem, the stability of the closed-loop system and the convergence of critic NN weights are proved. Finally, simulation results on multiple QUAVs show the effectiveness of the proposed control strategy.

Reinforcement learning-based formation-surrounding control for multiple quadrotor UAVs pursuit-evasion games

This paper proposes a reinforcement learning-based formation-surrounding control method for multiple quadrotor unmanned aerial vehicles (UAVs) pursuit-evasion (MPE) games system subject to external disturbances. In the framework of the MPE games, the pursuers aim to equally surround the evaders which try to avoid being surrounded when forming the desired formation. By constructing position and attitude tracking error subsystems of quadrotor UAV, this paper proposes two control strategies which combines the feedforward control technique and reinforcement learning (RL) method. First, two novel cost functions are presented for the quadrotor UAV with external disturbances. Then, two control schemes based on RL have been developed to guarantee the stability of the tracking error subsystem. Subsequently, two critic-only neural networks (NN) weight update laws that only satisfy finite excitation conditions are proposed to estimate the optimal cost function. Furthermore, Nash equilibrium for multiple quadrotor UAVs is achieved by means of RL strategy to solve the Hamilton-Jacobi-Isaacs (HJI) equations. And the property of equally surrounding is proved for the first time by utilizing Euler's formula in this paper. Finally, the numerical simulation results are given to show the effectiveness and superior performance of the proposed control method.

Distributed Formation Control Algorithms for QUAVs Based on Aggregative Games

In this article, the formation problems of multiple quadrotor unmanned aerial vehicles (QUAVs) are studied. In contrast to existing results, the problems are investigated from the perspective of aggregative games (AGs). An interesting discovery is that the desired formation can be constituted by the Nash equilibrium of a quadratic AG. To realize the desired formation for QUAVs, we design two distributed game-based formation algorithms. With the developed algorithms, the QUAVs do not need to measure the (relative) positions and (relative) velocities of neighboring QUAVs, which differs from existing algorithms. By constructing suitable Lyapunov functions, we rigorously analyze the two formation algorithms. The first formation algorithm converges exponentially to the desired formation for the QUAVs without parameter uncertainties. The other algorithm converges asymptotically to the desired formation, even if the QUAVs contain uncertain parameters. Finally, we provide illustrative examples.

Fixed-Time Distributed Adaptive Formation Control for Multiple QUAVs With Full-State Constraints

This paper devises a fixed-time distributed adaptive formation control algorithm under the event-triggered framework to guarantee the expected formation pattern for multiple quadrotor unmanned aerial vehicles (QUAVs) with full-state constraints. The multiple QUAVs subject to full-state constraints are transformed into the ones that are free from any constraints via a time-varying nonlinear transformation function (NTF), which is effective to handle the case regardless of whether there exist state constraints. The issue of “explosion of complexity” (EOC) as well as singularity problem are fully coped via the fixed-time command filter and the smooth switch function, respectively. To further improve the control performance of multiple QUAVs, the nonsmooth error compensation mechanism is constructed to compensate the filtered error resulting from command filter. The rigorous stability analysis of the developed event-trigger-based distributed formation control scheme proves that all signals of the closed-loop system are fixed-time bounded, and the states of multiple QUAVs will not violate the prescribed constraints and the formation tracking errors converge to a small region around the origin in a fixed time. Finally, simulation examples are performed to delineate the validity of the proposed distributed formation control algorithm.

Fixed-Time Disturbance Observer-Based Distributed Formation Control for Multiple QUAVs

In this article, the problem of fixed-time distributed formation control for multiple quadrotor unmanned aerial vehicles (QUAVs) suffering from external disturbances is investigated. Resorting to the fixed-time disturbance observer (FTDO), the unknown disturbances are accurately estimated without requiring the information of initial observer errors. Based on the fusion of command filter technique, nonsmooth error compensation mechanism and backstepping design method, the proposed singularity-free distributed formation controllers ensure that the whole closed-loop system is practical fixed-time stable, and the formation tracking errors converge to a small neighbourhood of the zero in a fixed time. Finally, a numerical simulation reveals the validity of the devised distributed formation control scheme.

Fully distributed prescribed performance formation control for UAVs with unknown maneuver of leader

This paper focuses on the formation control for multiple quadrotor unmanned aerial vehicles (UAVs) in a leader-follower structure. Taking account of the unknown maneuver of the leader and external disturbances, a novel fully distributed prescribed performance formation controller is proposed. Firstly, to obtain a quick convergence rate of errors and desired control precision, a performance function is introduced into a time-varying barrier Lyapunov function (TV-BLF). Then, a distributed adaptive extended state observer (AESO) is constructed to estimate the maneuver of the leader and external disturbances simultaneously. A distributed formation controller is established further based on the TV-BLF and estimations of the AESO. It is proved rigorously that by using the proposed formation controller, the desired formation pattern can be formed while the prescribed performance is guaranteed. Moreover, no global topology information is required in the proposed AESO and controller, which means that the formation can be realized in a fully distributed way. Last, simulations are performed to validate the effectiveness and the superiority of the presented scheme.

Disturbance observer-based backstepping formation control of multiple quadrotors with asymmetric output error constraints

This paper presents a distributed formation tracking control strategy which acts on multiple quadrotor unmanned aerial vehicles (QUAVs) formation control under external disturbance and asymmetric output error constraints. An asymmetric barrier Lyapunov function (BLF) is applied to ensure the constraint of output error. Based on graph theory and backstepping control method, a distributed formation controller is designed to achieve the formation and maintenance of formations, where external disturbance is handled by disturbance observer (DO). In the framework of Lyapunov theory, the bounded stability of the closed-loop system is proved, and the output error is remained within the constraint range. The superiority and effectiveness of the designed control strategy is verified by the compared simulation.

Finite-time distributed event-triggered formation control for quadrotor UAVs with experimentation

This paper deals with the finite-time event-triggered formation control problem for multiple quadrotor unmanned aerial vehicles (UAVs) with unknown external disturbances. Novel adaptive distributed robust event-triggered controllers without/with finite-time extended state observer (FTESO) are developed based on adaptive terminal sliding mode (TSM) control method to save control update times and reduce chattering times simultaneously. It is proved that the proposed control strategies guarantee that all the closed-loop signals are practically finite-time stable. Moreover, the proposed controllers are fully distributed without the global information that is related to the communication topology matrix H. Meanwhile, the controller with FTESO is anti-disturbance. Zeno behavior is excluded by the strictly positive sampling intervals. Finally, the efficiency of the proposed control algorithms is illustrated by numerical simulations and three bebop2 quadrotor UAVs experimentation.

Distributed wind measurements with multiple quadrotor unmanned aerial vehicles in the atmospheric boundary layer

Abstract. In this study, a fleet of quadrotor unmanned aerial vehicles (UAVs) is presented as a system to measure the spatial distribution of atmospheric boundary layer flow. The big advantage of this approach is that multiple and flexible measurement points in space can be sampled synchronously. The algorithm to obtain horizontal wind speed and direction is designed for hovering flight phases and is based on the principle of aerodynamic drag and the related quadrotor dynamics. During the FESST@MOL campaign at the boundary layer field site (Grenzschichtmessfeld, GM) Falkenberg of the Lindenberg Meteorological Observatory – Richard Assmann Observatory (MOL-RAO), 76 calibration and validation flights were performed. The 99 m tower equipped with cup and sonic anemometers at the site is used as the reference for the calibration of the wind measurements. The validation with an independent dataset against the tower anemometers reveals that an average accuracy of σrms<0.3 m s−1 for the wind speed and σrms,ψ<8∘ for the wind direction was achieved. Furthermore, we compare the spatial distribution of wind measurements with the fleet of quadrotors to the tower vertical profiles and Doppler wind lidar scans. We show that the observed shear in the vertical profiles matches well with the tower and the fluctuations on short timescales agree between the systems. Flow structures that appear in the time series of a line-of-sight measurement and a two-dimensional vertical scan of the lidar can be observed with the fleet of quadrotors and are even sampled with a higher resolution than the deployed lidar can provide.

Iterative learning-based formation control for multiple quadrotor unmanned aerial vehicles

A double-layer formation control is proposed to solve the repeated tasks for multiple quadrotor unmanned aerial vehicle systems. The first layer aims at achieving a formation target in which the iterative learning control is designed based on relative distance with neighbor unmanned aerial vehicles and absolute distance with virtual leader unmanned aerial vehicle. The formation controller is responsible for keeping the formation shape and generating the desired flying trajectories for each drones. The second layer control aims at achieving a high-precision tracking to desired flying trajectories which are generated from the formation controller. A double closed-loop proportional–derivative strategy is designed to ensure the accuracy of trajectory tracking for each individual drone. Simulations for the circle formation mission of the multiple quadrotor unmanned aerial vehicle system are given to verify the efficiency of the proposed method.

Finite-time formation tracking control with collision avoidance for quadrotor UAVs

In this paper, the distributed finite-time formation tracking control problem with collision avoidance is investigated for multiple quadrotor Unmanned Aerial Vehicles (UAV) subject to external disturbances. Firstly, two finite-time observers are designed to estimate the external disturbance force and torque without upper bound, respectively. Then, utilizing the precisely observed information deriving from the observers, a novel sliding mode surface-like variable based distributed position formation tracking control strategy and a fast terminal sliding mode based attitude tracking control strategy are proposed, respectively. It achieves that the multiple quadrotor UAVs can track the desired trajectory in a specific formation configuration within the safe distance and avoid dynamic obstacles. Meanwhile, the rapid attitude synchronization and tracking is also achieved. The entire multiple UAVs distributed formation tracking closed-loop control system are proved to be globally fast finite-time stable by using Lyapunov-like analysis. Finally, the effective and fine performance of the proposed schemes are demonstrated by a numerical simulation example with a group of quadrotor UAVs.

A sectorial fuzzy consensus algorithm for the formation flight of multiple quadrotor unmanned aerial vehicles

This paper presents an algorithm based on fuzzy theory for the formation flight of the multi-quadrotors. For this purpose, the mathematical model of N-quadrotor unmanned aerial vehicles is presented using the Newton-Euler formulation. The strategy of the formation flight is based on a structure composed by a sectorial fuzzy controller and the linear systems whose state variables are the position and velocity of the ith quadrotor. The stability analysis is described as a generalized form for N-quadrotor unmanned aerial vehicles and it is based on the Lyapunov theory. This analysis demonstrates that the closed-loop system is globally asymptotically stable so that the quadrotors unmanned aerial vehicles reach the consensus. Numerical simulation demonstrates the robustness of the proposed scheme for the formation flight even in the presence of disturbances. Finally, experimental results show the feasibility of the proposed algorithm for the formation flight of multiple unmanned aerial vehicles.

Event-Based Time Varying Formation Control for Multiple Quadrotor UAVs with Markovian Switching Topologies

Time varying formation control problem for a group of quadrotor unmanned aerial vehicles (UAVs) under Markovian switching topologies is investigated through a modified dynamic event-triggered control protocol. The formation shape is specified by a time varying vector, which prescribes the relative positions and bearings among the whole agents. Instead of the general stochastic topology, the graph is governed by a set of Markov chains to the edges, which can recover the traditional Markovian switching topologies in line with the practical communication network. The stability proof for the state space origin of the overall closed-loop system is derived from the singular perturbation method and Lyapunov stability theory. An event-triggered formation control protocol in terms of a dynamically varying threshold parameter is delicately carried out, while acquiring satisfactory resource efficiency, and Zeno behavior of triggering time sequences is excluded. Finally, simulations on six quadrotor UAVs are given to verify the effectiveness of the theoretical results.

Trajectory optimization of multiple quad-rotor UAVs in collaborative assembling task

A hierarchic optimization strategy based on the offline path planning process and online trajectory planning process is presented to solve the trajectory optimization problem of multiple quad-rotor unmanned aerial vehicles in the collaborative assembling task. Firstly, the path planning process is solved by a novel parallel intelligent optimization algorithm, the central force optimization-genetic algorithm (CFO-GA), which combines the central force optimization (CFO) algorithm with the genetic algorithm (GA). Because of the immaturity of the CFO, the convergence analysis of the CFO is completed by the stability theory of the linear time-variant discrete-time systems. The results show that the parallel CFO-GA algorithm converges faster than the parallel CFO and the central force optimization-sequential quadratic programming (CFO-SQP) algorithm. Then, the trajectory planning problem is established based on the path planning results. In order to limit the range of the attitude angle and guarantee the flight stability, the optimized object is changed from the ordinary six-degree-of-freedom rigid-body dynamic model to the dynamic model with an inner-loop attitude controller. The results show that the trajectory planning process can be solved by the mature SQP algorithm easily. Finally, the discussion and analysis of the real-time performance of the hierarchic optimization strategy are presented around the group number of the waypoints and the equal interval time.