Quadrotor Unmanned Aerial Vehicle Research Articles

FTESO-adaptive neural network based safety control for a quadrotor UAV under multiple disturbances: algorithm and experiments

PurposeQuadrotors have been applied in various fields. However, because the quadrotor is subject to multiple disturbances, consisting of external disturbances, actuator faults and parameter uncertainties, it is difficult to control the unmanned aerial vehicle (UAV) to achieve high-precision tracking performance. This paper aims to design a safety controller that uses observer and neural network method to improve the tracking performance of UAV under multiple disturbances. The experiments prove that this method is effective.Design/methodology/approachFirst, to actively estimate and compensate the synthetic uncertainties of the system, a finite-time extended state observer is investigated, and the disturbances are transformed into the extended state of the system for estimation. Second, an adaptive neural network controller that does not accurately require the dynamic model knowledge is designed based on the estimated value, where the weights of the neural network can be dynamically adjusted by the adaptive law. Furthermore, the finite-time bounded convergence of the proposed observer and the stability of the system are proved through homogeneous theory and Lyapunov method.FindingsThe figure-“8” climbing flight simulation and real flight experiments illustrate that the proposed safety control strategy has good tracking performance.Originality/valueThis paper proposes the safety control structure of the UAV, which combines the extended state observer with the neural network method. Numerical simulation results and actual flight experiments demonstrate the effectiveness of the proposed control strategy.

Fuzzy wavelet neural adaptive finite-time self-triggered fault-tolerant control for a quadrotor unmanned aerial vehicle with scheduled performance

This paper focuses on fuzzy wavelet neural networks-based adaptive finite-time self-triggered fault-tolerant control design with assured tracking performance for a quadrotor unmanned aerial vehicle subject to unknown actuator faults. First, an improved finite-time performance function is integrated with the command-filtered backstepping control framework to assure the scheduled tracking performance, which can effectively constrain the transient fluctuations of the tracking error at fault occurrence. Then, two compensation mechanisms are developed to weaken the adverse impact induced by actuator faults and filter errors. Further, a fuzzy wavelet neural adaptive self-triggered fault-tolerant controller with guaranteed performance is designed to improve the fault insensitivity and tracking accuracy of the controlled vehicle, where the fuzzy wavelet neural networks are employed to approximate the unknown nonlinearities and the control signals are allowed to achieve irregular updating only when the pre-specified trigger protocol is violated. Stability analysis proves that the designed controller guarantees the attitude and position subsystems are practical finite-time stable, and the tracking errors are strictly confined to a preassigned region and never cross its allowed bounds. Finally, the validity of the developed control scheme is confirmed by the simulation results.

THE IMPACT OF GLOBAL DYNAMICS ON THE FRACTALS OF A QUADROTOR UNMANNED AERIAL VEHICLE (QUAV) CHAOTIC SYSTEM

In this paper, we have extended the concept of advanced Julia function for the discovery of new type of trajectories existing inside outer and inner wings. A dynamical system based on four rotors, referred to as quadrotor unmanned aerial vehicle (QUAV), is considered for the first time to seek the generation of extra wings using fractal theory. Moreover, we have used Julia and advanced Julia function techniques for the creation of outer and inner wings, respectively. Furthermore, the trajectories achieved using both techniques are analyzed and compared with the aid of basin of attraction and bifurcation diagram. This work covers a gap in the literature, that what is the impact of global dynamics on the hidden fractals. Therefore to fill this gap, sets of initial points are plotted from each basin constructed for fractals-based QUAV systems. Finally, numerical simulations are carried out using MATLAB, whereas the data generated for the corresponding basins are attached for the convenience of readers to reproduce each figure.

Finite-Time Convergence Control for a Quadrotor Unmanned Aerial Vehicle With a Slung Load

This work investigates the control technique for a quadrotor unmanned aerial vehicle with a slung load. By taking the advantage of the cascade property, a new hierarchical control scheme is designed that divides the control problem into three parts. For the attitude motion control of the quadrotor, the non-singularity terminal sliding mode based control law is proposed. For the translation motion control of the quadrotor, the super-twisting based control law is designed. Additional, for the swing attenuation of the slung load, a nonlinear anti-swing control design is provided. A Lyapunov stability analysis is presented to show the stability of the closed loop system, together with the fast convergence of the quadrotor's translational and attitude tracking, and exponential convergence of the slung load's swing motion. The real time experiments verifies the well performance of the proposed controller.

An improved nonsingular adaptive super twisting sliding mode controller for quadcopter.

This paper presents an improved nonsingular adaptive super twisting sliding mode control for tracking of a quadrotor system in the presence of external disturbances and uncertainty. The initial step involves developing a dynamic model for the quadrotor that is free from singularities, achieved through the utilization of the Newton-Quaternion formalism. Then, the super twisting algorithm is used to develop a novel sliding mode control that mitigates chattering. Particle Swarm Optimization (PSO) is employed for the adjustment of the controller gains. Moreover, to maintain stable control of the quadcopter, even in scenarios where the upper limit of disturbances is unknown, an adaptive rule grounded in Lyapunov stability is applied. Simulation results demonstrate that the proposed controller reduces tracking errors to 0.1% for roll, 0.05% for pitch, and 2.2% for altitude, outperforming other state-of-the-art sliding mode controllers. Additionally, the proposed controller effectively rejects disturbances, maintaining minimal steady-state errors of 0.01° for roll, 0.02° for pitch, and 0.001° for yaw, significantly better than conventional controllers. These results highlight tracking and disturbance rejection capabilities of the proposed controller, making its real-time implementation for quadrotor Unmanned Aerial Vehicles (UAVs) feasible.

Adaptive Learning Control for a Quadrotor Unmanned Aerial Vehicle Landing on a Moving Ship

The shipboard landing problem of a quadrotor without prior knowledge of the ship is investigated in this paper. The relative motion model of the quadrotor and the ship is established and transformed into an affine form for the landing controller design. To improve the adaptability to different ships, a neuron-adaptive neural network is adopted to estimate the model uncertainties caused by unknown ship parameters, and the disturbance observer is developed to measure the lumped disturbance, including the external disturbances and the approximation error. Moreover, a novel backstepping integral evolution sliding mode controller is developed for the relative position, and nonsingular fast terminal sliding mode control is adopted for others. Combined with the auxiliary systems, the input saturation and filter errors are considered in the closed-loop system. The theoretical analysis illustrates that the states of the relative motion system are guaranteed to be bounded. The simulation examples demonstrate the effectiveness and advantages of the proposed method.

Fixed-Time Extended Observer-Based Adaptive Sliding Mode Control for a Quadrotor UAV under Severe Turbulent Wind

This paper presents a fixed-time extended state observer-based adaptive sliding mode controller evaluated in a quadrotor unmanned aerial vehicle subject to severe turbulent wind while executing a desired trajectory. Since both the state and model of the system are assumed to be partially known, the observer, whose convergence is independent from the initial states of the system, estimates the full state, model uncertainties, and the effects of turbulent wind in fixed time. Such information is then compensated via feedback control conducted by a class of adaptive sliding mode controller, which is robust to perturbations and reduces the chattering effect by non-overestimating its adaptive gain. Furthermore, the stability of the closed-loop system is analyzed by means of the Lyapunov theory. Finally, simulation results validate the feasibility and advantages of the proposed strategy, where the observer enhances performance. For further demonstration, a comparison with an existent approach is provided.

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.

Optimized intelligent tracking control for a quadrotor unmanned aerial vehicle with actuator failures

This work investigates the implementation of tracking control for a quadrotor unmanned aerial vehicle exposed to disturbances and actuator failures. Cost reduction in control processes is not usually considered in conventional control methods. To overcome this limitation, this work proposes an optimized intelligent control scheme that utilizes adaptive dynamic programming. Firstly, a critic-only structure is employed to learn the nominal control strategies, including two critic neural network update laws, lifting the persistent excitation condition. Subsequently, two specifically designed observers are developed on the radial basis function neural network (RBFNN) to reconstruct the disturbances and actuator failures and compensate for the nominal strategies. The observers achieve fixed-time convergence by employing the fixed-time update laws of the RBFNNs. Moreover, two variable vectors using the Gaussian error function are introduced to reduce the impact caused by the observation errors. Additionally, the superior performance of the proposed control scheme is validated through numerical simulations.

An Adaptation of a Sliding Mode Classical Observer to a Fractional-Order Observer for Disturbance Reconstruction of a UAV Model: A Riemann–Liouville Fractional Calculus Approach

This paper proposes a modification of a Sliding Mode Classical Observer (SMCO) to adapt it to the fractional approach. This adaptation involves using a set of definitions based on fractional calculus theory, particularly the approach developed by Riemann–Liouville, resulting in a Sliding Mode Fractional Observer (SMFO). Both observers are used to perform disturbance reconstruction considered additive in a Quadrotor Unmanned Aerial Vehicle (UAV) model. Then, this work presents the fractional-order sliding mode observer’s mathematical formulation and integration into the Quadrotor UAV model. To validate the quality of the disturbance reconstruction process of the proposed SMFO observer scheme, numerical simulations are carried out, where a reconstruction quality indicator (BQR) is proposed based on the analysis of performance indices such as the Mean Square Error (MSE), the First Probability Moment (FPM), and Second Probability Moment (SPM), which were obtained for both the SMCO and the SMFO. The simulation results demonstrate the efficacy of the proposed observer in accurately reconstructing disturbances under various environmental conditions. Comparative analyses with SMCO highlight the advantages of the fractional-order approach in terms of reconstruction accuracy and improvement of its transitory performance. Finally, the presented SMFO offers a promising avenue for enhancing the reliability and precision of disturbance estimation, ultimately contributing to the advancement of robust control strategies for Quadrotor UAV systems.

Backstepping Sliding Mode Control Algorithm for Unmanned Aerial Vehicles Based on Fractional-Order Theory

Aiming at the problems of slow convergence speed and low tracking accuracy in attitude control and position tracking of quadrotor unmanned aerial vehicles (UAVs). This paper combines the fractional-order calculus theory with the backstepping sliding mode control algorithm, using the backstepping control to compensate for the nonlinearity of the system and the fractional-order theory to eliminate the jitter brought about by the sliding mode control, and proposes a new fractional-order backstepping sliding mode control strategy for the trajectory tracking control of the quadrotor UAV. The proposed fractional-order sliding mode surface increases the control flexibility and improves the robustness and anti-interference ability of the system to some extent. The stability analysis of the system is carried out using the Lyapunov stability theory, and the results prove the stability of the proposed controller. Finally, the effectiveness and feasibility of the proposed method are verified by comparing it with the traditional backstepping sliding mode controller. The simulation results show that the fractional-order reverse-step sliding mode control algorithm proposed in this paper is significantly better than other control algorithms in terms of convergence speed and also has a certain degree of superiority in terms of error elimination.

Control Schemes for Quadrotor UAV: Taxonomy and Survey

Quadrotor Unmanned Aerial Vehicle (UAV) is an unstable system, so it needs to be controlled efficiently and intelligently. Moreover, due to its non-linear, coupled, and under-actuated nature, the quadrotor has become an important research platform to study and validate various control theories. Different control approaches have been used to control the quadrotor UAV. In this context, a comprehensive study of different control schemes is presented in this research. First, an overview of the working and different applications of quadrotor UAVs is presented. Second, a mathematical model of the quadrotor is discussed. Later, the experimental results of various existing control techniques are discussed and compared. The various control schemes discussed and described for quadrotors are; Proportional Integral and Derivative (PID), Linear Quadratic Regulator (LQR), H-infinity ( H ∞ ), Sliding Mode Control (SMC), Feedback Linearization (FBL), Model Predictive Control (MPC), Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), Iterative Learning Control (ILC), Reinforcement Learning Control (RLC), Brain Emotional Learning Control (BELC), Memory Based Control (MBC), Nested Saturation Control (NSC), and Hybrid Controllers (HC). Comparison is done among all the control techniques and it is concluded that the hybrid control method gives improved results. This survey presents a broad overview of the state-of-the-art in UAV design, control, and implementation for real-life applications.

Adaptive-Dynamic-Programming-Based Robust Control for a Quadrotor UAV with External Disturbances and Parameter Uncertainties

Thiswork addresses the trajectory-tracking-control problem for a quadrotor unmanned aerial vehicle with external disturbances and parameter uncertainties. A novel adaptive-dynamic-programming-based robust control method is proposed to eliminate the effects of lumped uncertainties (including external disturbances and parameter uncertainties) and to ensure the approximate optimal control performance. Its novelty lies in that two radial basis function neural network observers with fixed-time convergence properties were first established to reconstruct the lumped uncertainties. Notably, they tune only the scalar parameters online and have low computational complexities. Subsequently, two actor–critic neural networks were designed to approximate the optimal cost functions and control policies for the nominal system. In this design, two new actor–critic neural network weight update laws are proposed to eliminate the persistent excitation condition. Then, two adaptive-dynamic-programming-based robust control laws were obtained by integrating the observer reconstruction information and the nominal control policies. The uniformly ultimately bounded stability of the closed-loop tracking control systems was ensured using the Lyapunov methodology. Finally, numerical results are shown to verify the effectiveness and superiority of the proposed control scheme.

Multi-UAV Airborne UV Area Coverage and Task Assignment Method

To address the area coverage problem in large-scale reconnaissance and detection tasks, Unmanned Aerial Vehicles(UAVs) utilize Ultraviolet(UV) light for flight guidance. Additionally, a UV light power control method is implemented to assist UAVs in adapting to various terrains. Based on the flight characteristics of quadrotor UAVs, the target area is divided into a grid-like format. This transformation helps convert the area coverage task into a multi-UAV path planning problem on a raster map. To address this, constraints are established for assigning tasks to multiple UAVs. An algorithm is proposed to assign the target area uniformly based on the initial position of the UAV, ensuring full coverage. Additionally, the algorithm considers the differences in endurance among multiple UAVs. This improved algorithm is highly suitable for real-life application scenarios. The simulation results demonstrate the power control changes in wireless UV-guided UAV flight. Moreover, the assignment results of the improved algorithm align closely with the theoretical assignment results, considering different parameters like the number of UAVs, initial position, and endurance capacity.

Vision-Based Deep Reinforcement Learning of UAV-UGV Collaborative Landing Policy Using Automatic Curriculum

Collaborative autonomous landing of a quadrotor Unmanned Aerial Vehicle (UAV) on a moving Unmanned Ground Vehicle (UGV) presents challenges due to the need for accurate real-time tracking of the UGV and the adjustment for the landing policy. To address this challenge, we propose a progressive learning framework for generating an optimal landing policy based on vision without the need of communication between the UAV and the UGV. First, we propose the Landing Vision System (LVS) to offer rapid localization and pose estimation of the UGV. Then, we design an Automatic Curriculum Learning (ACL) approach to learn the landing tasks under different conditions of UGV motions and wind interference. Specifically, we introduce a neural network-based difficulty discriminator to schedule the landing tasks according to their levels of difficulty. Our method achieves a higher landing success rate and accuracy compared with the state-of-the-art TD3 reinforcement learning algorithm.

Learning‐based collision avoidance and robust H∞ optimal formation control for uncertain quadrotor UAV systems

AbstractThis paper investigates the robust optimal formation control for the position subsystem of quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances and collision constraints. To prevent collision with both members of the formation and external obstacles, a collision avoidance potential function is constructed using relative position and velocity information. The basic bounded control input can ensure the stable flight and collision avoidance of a quadrotor UAV system. Based on the approximate dynamic programming (ADP) framework and two‐player zero‐sum differential game theory, the optimal controller is designed to further enhance the control performance of the system. The optimal value function is approximated by a single layer neural network, which avoids solving complex nonlinear Hamilton‐JacobiIsaac (HJI) equation. The stability of the closed loop system is proved. The effectiveness of the robust optimal formation controller based on learning is validated through MATLAB simulation in two distinct scenarios.

Modeling and analysis of nonlinear backstepping controller for the crazyflie quadrotor trajectory tracking

This paper illustrates the Newton-Euler laws-based nonlinear dynamic model of a Crazyflie Quadrotor Unmanned Aerial Vehicle (CF-QUAV). It demonstrates the state-space-form equations that govern how the CF-QUAV moves around its frame (body). A Proposed nonlinear control strategy is used to regulate the height (z), position (x, y), attitude (roll and pitch), and heading (yaw) angles. This strategy is based on a Backstepping nonlinear controller defined by Lyapunov theory. The proposed controller can replace the Proportional-Integration-Derivative (PID) controller utilized in the CF-QUAV model to follow the desired trajectories gleaned from CF lab tests. In addition, the parameters of the proposed controller are tuned using the Genetic Algorithm (GA) in order to achieve the best performance and stability of the CF-QUAV model. Simulation-based experiments with statistical analysis are carried out to evaluate the performance of the proposed nonlinear controller and compare its performance and stability with the linear PID controller.

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.

Two‐order cooperative optimization of swarm control based on reinforcement learning

AbstractThis paper presents a study of the cooperative optimal swarm control problem for two‐order multi‐agent systems with partially unknown nonlinear functions. Unlike traditional approaches that consider a single error, this paper proposes to use multi‐order errors in the performance index function to achieve optimal control performance. Additionally, different proportional coefficients are assigned to illustrate the varying influences of each sequence error, and a two‐order cooperative (TOC)performance index function is designed. To address the influence of unknown nonlinear functions, a swarm control system based on sliding mode control with an actor‐critic network is constructed, which increases the applicability of the proposed method to a variety of dynamic models. Furthermore, to alleviate the computational pressure caused by the multi‐order errors in the TOC performance index function, a new reinforcement learning (RL)‐based sliding mode swarm controller is designed. The stability of the proposed controller is demonstrated using the Lyapunov function. Finally, the control model and control rate are applied to a quadrotor unmanned aerial vehicle system, and simulation results demonstrate that the multi‐agent systems can effectively achieve swarm control.Impact Statement: This paper proposes a reinforcement learning‐based sliding mode control strategy for the cooperative optimal swarm control problem, where the nonlinear functions of two‐order multi‐agent systems are only partially known. In addition, we also propose a cooperative performance index function, which takes into account multi‐order errors for optimizing the performance. This contribution is significant for research in sliding mode control strategies and error co‐optimization.

QUAV attitude control based on linear active disturbance rejection control

Unmanned Aerial Vehicle (UAV) are currently gaining popularity. This paper proposes a method to apply Linear Active Disturbance Rejection Control (LADRC) to Quadrotor Unmanned Aerial Vehicle (QUAV) controller to optimize the traditional PID controller. Firstly, the application and shortcomings of traditional PID control in UAV are introduced, and the LADRC method is proposed. Then linear simplification and Parameter Setting of ADRC are carried out. In the Simulink environment, according to the mathematical model of the QUAV, a QUAV dynamics simulation platform is established. Finally, according to different control channels, different control algorithms are designed, and tracking models are introduced in various attitudes to simulate and verify the control effect of LADRC. The results show that the LADRC controller is effective, the LADRC can be effectively combined with the traditional PID control. The application in the QUAV can realize more precise QUAV speed tracking control and the stable flight of the QUAV. Compared with the traditional PID controller, under the experimental conditions of this paper, the LADRC controller has more precise control accuracy and more efficient control efficiency. Finally, this paper summarizes the design of LADRC and makes a brief outlook on the development of UAVs.