Search Results (456)

In response to the non-linear and underactuated characteristics of quadrotor UAV suspension transportation system, this paper proposes a novel control strategy aimed at achieving precise position control, attitude control, and anti-swing capabilities. Firstly, a dynamical model required for controller design is established through the Newton-Euler method. In the controller design process, the paper employs the energy method and barrier Lyapunov function to design a double-closed-loop nonlinear controller. This controller is capable of not only accurately controlling the position and attitude angles of the quadrotor UAV suspension transportation system but also effectively suppressing the swing of the payload. Building on this, considering the elastic deformation of the lifting cable, and by analyzing the forces in the Newton-Euler equations, this paper proposes an adaptive control design for the case where the length of the cable connecting the UAV and the payload is unknown. To validate the effectiveness of the proposed control scheme, comparative experiments were conducted in the MATLAB simulation environment, and the results indicate that the method proposed in this paper exhibits superior control performance compared to traditional controllers. Full article

In practical missions, quadrotor drones frequently face the challenge of navigating through multiple predetermined waypoints in cluttered environments where the sequence of the waypoints is not specified. This study presents a comprehensive multi-waypoint motion planning framework for quadrotor drones, comprising multi-waypoint trajectory planning and waypoint sequencing. To generate a trajectory that follows a specified sequence of waypoints, we integrate uniform B-spline curves with a bidirectional A* search to produce a safe, kinodynamically feasible initial trajectory. Subsequently, we model the optimization problem as a quadratically constrained quadratic program (QCQP) to enhance the trackability of the trajectory. Throughout this process, a replanning strategy is designed to ensure the traversal of multiple waypoints. To accurately determine the shortest flight time waypoint sequence, the fast marching (FM) method is utilized to efficiently establish the cost matrix between waypoints, ensuring consistency with the constraints and objectives of the planning method. Ant colony optimization (ACO) is then employed to solve this variant of the traveling salesman problem (TSP), yielding the sequence with the lowest temporal cost. The framework’s performance was validated in various complex simulated environments, demonstrating its efficacy as a robust solution for autonomous quadrotor drone navigation. Full article

This paper shows the results obtained by using MATLAB/Simulink and X-Plane as co-simulation tools for the comparison of control schemes for takeoff and landing maneuvers of a quadrotor. Two control schemes based on nested saturations are compared to ensure the convergence of $\theta$ and $\varphi$ angles to the equilibrium point, each with its own specific characteristics in its design and tuning procedure. Furthermore, in both proposals, a Generalized Proportional Integral (GPI) control is used for the height part, while a feedforward PID control is used for the $\psi$ angle. The control schemes are proposed from a local geodetic coordinate system East, North, Up (ENU). Feedback data for the control schemes are obtained from X-Plane via User Datagram Protocol (UDP)-based interface; they are used in MATLAB/Simulink for the calculation of the control actions; the control actions are then entered into a transformation matrix that converts the actions into rotor angular velocities, which are sent to X-Plane. Several numerical simulations are presented to demonstrate the effectiveness and robustness of the proposed schemes, considering the presence of disturbances mainly due to wind speed. Finally, different performance indices are used to evaluate the schemes based on error; in this way, the use of X-Plane as a Model-in-Loop (MIL) environment is validated, which helps to identify errors or problems of the proposed controllers before their coding and physical implementation. Full article

In this study, a novel control scheme based on spiking neural networks (SNNs) has been proposed to accomplish the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs). The update rules for the network parameters have been derived using the Lyapunov stability theorem. Three different trajectories have been utilized in the simulated and experimental studies to verify the efficacy of the proposed control scheme. The acquired results have been compared with the responses obtained for proportional–integral–derivative (PID) and traditional neural network controllers. Simulated and experimental studies demonstrate that the proposed SNN-based controller is capable of providing better tracking accuracy and robust system response in the presence of disturbing factors. Full article

To expand the application prospects of quadrotors in challenging scenes such as those with dense obstacles and narrow corridors, task-driven reconfigurable quadrotors are highly desirable. Aiming to address hazard missions, in this paper, translational reconfigurable quadrotors and rotational reconfigurable quadrotors are proposed with their assumptions and mathematical models. Related motion control laws were designed using model reference adaptive control (MRAC) theory based on Lyapunov stability theory, whose validity was demonstrated by sufficient numerical simulations. The simulation results verify the feasibility of the proposed control laws and reveal the important effect of time delay on the stability of the motion control system. Additionally, the dependence of motion control’s stability on the time constant of reference system was discussed. Full article

This paper investigates the formation control problem of multi-unmanned aerial vehicle (UAV) systems with multi-operating modes. While mode switching enhances the flexibility of multi-UAV systems, it also introduces dynamic model switching behaviors in UAVs. Moreover, obtaining an accurate dynamic model for a multi-UAV system is challenging in practice. In addition, communication link failures and time-varying unknown disturbances are inevitable in multi-UAV systems. Hence, to overcome the adverse effects of the above challenges, a hybrid iterative learning formation control strategy is proposed in this paper. The proposed controller does not rely on precise modeling and exhibits its learning ability by utilizing historical input–output data to update the current control input. Furthermore, two convergence theorems are proven to guarantee the convergence of state, disturbance estimation, and formation tracking errors. Finally, three simulation examples are conducted for a multi-UAV system consisting of four quadrotor UAVs under multi-operating modes, switching topologies, and external disturbances. The results of the simulations show the strategy’s effectiveness and superiority in achieving the desired formation control objectives. Full article

In recent years, the cost-effectiveness and versatility of Unmanned Aerial Vehicles (UAVs) have led to their widespread adoption in both military and civilian applications, particularly for operations in remote or hazardous environments where human intervention is impractical. The use of multi-agent UAV systems has notably increased for complex tasks such as surveying and monitoring, driving extensive research and development in control, communication, and coordination technologies. Evaluating and analysing these systems under dynamic flight conditions present significant challenges. This paper introduces a mathematical model for leader–follower structured Quadrotor UAVs that encapsulates their dynamic behaviour, incorporating a novel multi-agent ad hoc coordination network simulated via COOJA. Simulation results with a pipeline surveillance case study demonstrate the efficacy of the coordination network and show that the system offers various improvements over contemporary pipeline surveillance approaches. Full article

This article studies a nonlinear model predictive control (NMPC) scheme for the trajectory tracking efficiency of a quadcopter UAV. A cost function is first proposed that incorporates weighted increments of control forces in each direction, followed by a weighted summation. Furthermore, a contraction constraint for the cost function is introduced based on the numerical convergence of the system for the sampling period of the UAV control force. Then, an NMPC scheme based on improved continuous/generalized minimum residuals (C/GMRES) is proposed to obtain acceptable control performance and reduce computational complexity. The proposed control scheme achieves efficient and smooth tracking control of the UAV while guaranteeing the closed-loop stability of the system. Finally, simulation results are presented to illustrate the effectiveness and superior performance of the proposed NMPC control scheme. Full article

This paper proposes a novel adaptive finite-time controller for a quadrotor unmanned aerial vehicle (UAV) model with stochastic perturbations and parameter-unknown terms, under the constraints of a state-constrained system. The controller is designed based on full-state quantization, where the error system is defined to be a function of the quantized error signal. An adaptive method is employed to address the quadrotor UAV system model with nonlinear terms and unknown perturbations. The controller utilizes Barrier Lyapunov function (BLF) bounds with adaptive effective time performance to ensure full-state constraint of the system. The stability of the system is proven using Lyapunov’s stability theorem. The effectiveness of the designed full-state constrained controller for quadrotor UAV based on full-state quantization is verified through a physical experimental simulation platform. Full article

In this paper, we present a vision-based leader–follower strategy for formation control of multiple quadrotors. The leaders use a decoupled visual control scheme based on invariant features. The followers use a control scheme based only on bearing measurements, and a robust control is introduced to deal with perturbations generated by the unknown movement of the leaders. Using this formulation, we study a geometrical pattern formation that can use the distance between the leaders to scale the formation and cross constrained spaces, such as a window. A condition is defined for which a formation has rigidity properties considering the constrained field of view of the cameras, such that invariance to translation and scaling is achieved. This condition allows us to specify a desired formation where the followers do not need to share information between them. Results obtained in a dynamic simulator and real experiments show the effectiveness of the approach. Full article

The development of unmanned aerial vehicle (UAV) formation systems has brought significant advantages across various fields. However, formation change and obstacle avoidance control have long been fundamental challenges in formation flight research, with the majority of studies concentrating primarily on quadrotor formations. This paper introduces a novel approach, proposing a new method for designing a formation adaptive factor fuzzy controller (AFFC) and an artificial potential field (APF) method based on an enhanced repulsive potential function. These methods aim to ensure the smooth completion of fixed-wing formation flight tasks in three-dimensional (3D) dynamic environments. Compared to the traditional fuzzy controller (FC), this approach introduces a fuzzy adaptive factor and establishes fuzzy rules to address parameter-tuning uncertainties. Simultaneously, improvements to the obstacle avoidance algorithm mitigate the issue of local optimal values. Finally, multiple simulation experiments were conducted. The findings show that the suggested method outperforms the proportional–integral–derivative (PID) control and fuzzy control methods in achieving formation transformation tasks, resolving formation obstacle avoidance challenges, enabling formation reconstruction, and enhancing formation safety and robustness. Full article

With the rapid development of the Internet of Things, the Internet of Vehicles (IoV) has quickly drawn considerable attention from the public. The cooperative unmanned aerial vehicles (UAVs)-assisted vehicular networks, as a part of IoV, has become an emerging research spot. Due to the significant limitations of the application and service of a single UAV-assisted vehicular networks, efforts have been put into studying the use of multiple UAVs to assist effective vehicular networks. However, simply increasing the number of UAVs can lead to difficulties in information exchange and collisions caused by external interference, thereby affecting the security of the entire cooperation and networking. To address the above problems, multiple UAV cooperative formation is increasingly receiving attention. UAV cooperative formation can not only save energy loss but also achieve synchronous cooperative motion through information communication between UAVs, prevent collisions and other problems between UAVs, and improve task execution efficiency. A multi-UAVs cooperation method based on arithmetic optimization is proposed in this work. Firstly, a complete mechanical model of unmanned maneuvering was obtained by combining acceleration limitations. Secondly, based on the arithmetic sine and cosine optimization algorithm, the mathematical optimizer was used to accelerate the function transfer. Sine and cosine strategies were introduced to achieve a global search and enhance local optimization capabilities. Finally, in obtaining the precise position and direction of multi-UAVs to assist networking, the cooperation method was formed by designing the reference controller through the consistency algorithm. Experimental studies were carried out for the multi-UAVs’ cooperation with the particle model, combined with the quadratic programming problem-solving technique. The results show that the proposed quadrotor dynamic model provides basic data for cooperation position adjusting, and our simplification in the model can reduce the amount of calculations for the feedback and the parameter changes during the cooperation. Moreover, combined with a reference controller, the UAVs achieve the predetermined cooperation by offering improved navigation speed, task execution efficiency, and cooperation accuracy. Our proposed multi-UAVs cooperation method can improve the quality of service significantly on the UAV-assisted vehicular networks. Full article

In this paper, a novel robust finite-time control scheme is specifically designed for a class of under-actuated nonlinear systems. The proposed scheme integrates a reaching phase-free integral backstepping method with an integral terminal fractional-order sliding mode to ensure finite-time stability at the desired equilibria. The core of the algorithm is built around proportional-integral-based nonlinear virtual control laws that are systematically designed in a backstepping manner. A fractional-order integral terminal sliding mode is introduced in the final step of the design, enhancing the robustness of the overall system. The robust nonlinear control algorithm developed in this study guarantees zero steady-state errors at each step while also providing robustness against matched uncertain disturbances. The stability of the control scheme at each step is rigorously proven using the Lyapunov candidate function to ensure theoretical soundness. To demonstrate the practicality and benefits of the proposed control strategy, simulation results are provided for two systems: a cart–pendulum system and quadcopter UAV. These simulations illustrate the effectiveness of the proposed control scheme in real-world scenarios. Additionally, the results are compared with those from the standard literature to highlight the superior performance and appealing nature of the proposed approach for underactuated nonlinear systems. This comparison underscores the advantages of the proposed method in terms of achieving robust and stable control in complex systems. Full article

Deep reinforcement learning has demonstrated flexibility advantages in the control field of quadrotor aircraft. However, when there are sudden disturbances in the environment, especially special disturbances beyond experience, the algorithm often finds it difficult to maintain good control performance. Additionally, due to the randomness in the algorithm’s exploration of states, the model’s improvement efficiency during the training process is low and unstable. To address these issues, we propose a deep reinforcement learning framework guided by Multi-PID Self-Attention to tackle the challenges in the training speed and environmental adaptability of quadrotor aircraft control algorithms. In constructing the simulation experiment environment, we introduce multiple disturbance models to simulate complex situations in the real world. By combining the PID control strategy with deep reinforcement learning and utilizing the multi-head self-attention mechanism to optimize the state reward function in the simulation environment, this framework achieves an efficient and stable training process. This experiment aims to train a quadrotor simulation model to accurately fly to a predetermined position under various disturbance conditions and subsequently maintain a stable hovering state. The experimental results show that, compared with traditional deep reinforcement learning algorithms, this method achieves significant improvements in training efficiency and state exploration ability. At the same time, this study deeply analyzes the application effect of the algorithm in different complex environments, verifies its superior robustness and generalization ability in dealing with environmental disturbances, and provides a new solution for the intelligent control of quadrotor aircraft. Full article

A folding wing vertical take-off and landing (VTOL) UAV is capable of transitioning between quadrotor and fixed-wing modes, but significant alterations occur in its dynamics model and maneuvering mode during the transformation process, thereby imposing greater demands on the adaptability of its control system. In this paper, a multi-level switching control scheme based on dynamic allocation is proposed for the deformation stage. Firstly, according to the physical characteristics of the wing folding mechanism, a dynamic model is established. The influence of the incoming flow on the rotors is considered, and the dynamic coupling characteristics in its transition process are analyzed. Secondly, by inverting the changes in rotor position and axial direction, a dynamic allocation algorithm for the rotors is designed. Then, the quadrotor controller and the fixed-wing controller are switched and mixed in multiple loops to form a multi-level switching control scheme. Finally, the simulation results show that the designed multi-level switching control scheme is effective and robust in forward and backward deformation processes, and its anti-interference ability is stronger compared with that of the control scheme without dynamic allocation. Full article