Search Results (297)

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have become widely used in many fields, ranging from agriculture to military operations, due to recent advances in technology and decreases in costs. Quadrotors are particularly important UAVs, but their complex, coupled dynamics and sensitivity to outside disturbances make them challenging to control. This paper introduces a new control method for quadrotors called Backstepping Sliding Mode Control (BSMC), which combines the strengths of two established techniques: Backstepping Control (BC) and Sliding Mode Control (SMC). Its primary goal is to improve trajectory tracking while also reducing chattering, a common problem with SMC that causes rapid, high-frequency oscillations. The BSMC method achieves this by integrating the SMC switching gain directly into the BC through a process of differential iteration. Herein, a Lyapunov stability analysis confirms the system’s asymptotic stability; a genetic algorithm is used to optimize controller parameters; and the proposed control strategy is evaluated under diverse payload conditions and dynamic wind disturbances. The simulation results demonstrated its capability to handle payload variations ranging from 0.5 kg to 18 kg in normal environments, and up to 12 kg during gusty wind scenarios. Furthermore, the BSMC effectively minimized chattering and achieved a superior performance in tracking accuracy and robustness compared to the traditional SMC and BC. Full article

Reinforcement learning (RL), and in particular Proximal Policy Optimization (PPO), has shown promise in high-precision quadrotor unmanned aerial vehicle (QUAV) control. However, the performance of PPO is highly sensitive to the choice of the clipping parameter, and inappropriate settings can lead to unstable training dynamics and excessive policy oscillations, which limit deployment in safety-critical aerial applications. To address this issue, we propose a stability-aware dynamic clipping parameter adjustment strategy, which adapts the clipping threshold ${ϵ}_t$ in real time based on a stability variance metric $S_t$. This adaptive mechanism balances exploration and stability throughout the training process. Furthermore, we provide a Lipschitz continuity interpretation of the clipping mechanism, showing that its adaptation implicitly adjusts a bound on the policy update step, thereby offering a deterministic guarantee on the oscillation magnitude. Extensive simulation results demonstrate that the proposed method reduces policy variance by 45% and accelerates convergence compared to baseline PPO, resulting in smoother control responses and improved robustness under dynamic operating conditions. While developed within the PPO framework, the proposed approach is readily applicable to other on policy policy gradient methods. Full article

Unmanned Aerial Vehicles (UAVs) have revolutionized various industries due to their adaptability, efficiency, and capability to operate in diverse environments. However, conventional UAV designs face trade-offs between flight endurance and maneuverability. This study explores the design, analysis, and optimization of a biplane quadrotor UAV, integrating the vertical takeoff and landing (VTOL) capabilities of multirotors with the aerodynamic efficiency of fixed-wing aircraft to enhance flight endurance while maintaining high maneuverability. The UAV’s structural design incorporates biplane wings with different NACA airfoil configurations (NACA4415, NACA0015, and NACA0012) to assess their impact on drag reduction, stress distribution, and flight efficiency. Computational Fluid Dynamics (CFD) simulations in ANSYS Fluent 2023 R2 (Canonsburg, PA, USA).reveal that the NACA0012 airfoil achieves the highest drag reduction (75.29%), making it the most aerodynamically efficient option. Finite Element Analysis (FEA) further demonstrates that NACA4415 exhibits the lowest structural stress (95.45% reduction), ensuring greater durability and load distribution. Additionally, a hybrid flight control system, combining Backstepping Control (BSC) and Integral Terminal Sliding Mode Control (ITSMC), is implemented to optimize transition stability and trajectory tracking. The results confirm that the biplane quadrotor UAV significantly outperforms conventional quadcopters in terms of aerodynamic efficiency, structural integrity, and energy consumption, making it a promising solution for surveillance, cargo transport, and long-endurance missions. Future research will focus on material enhancements, real-world flight testing, and adaptive control strategies to further refine UAV performance in practical applications. Full article

This review explores recent advancements in morphing structures for Unmanned Ariel Vehicles (UAVs), focusing on mechanical designs and control strategies of quadrotors that enable real-time geometric reconfiguration. Morphing mechanisms, ranging from closed-loop linkages to bioinspired and compliant structures, are evaluated in terms of adaptability, actuation simplicity, and flight stability. Control approaches, including model predictive control, reinforcement learning, and sliding mode control, are analyzed for their effectiveness in handling dynamic morphology. The review also highlights key morphing wing concepts such as GNATSpar and Zigzag Wingbox, which enhance aerodynamic efficiency and structural flexibility. A novel concept featuring an inverted slider-crank mechanism (ISCM) is introduced, enabling dual-mode UAV operation for both aerial and terrestrial missions, which is particularly useful in scenarios like wildfire suppression where stability and operation longevity are crucial. This study emphasizes the importance of integrated design approaches that align mechanical transformation with adaptive control. Critical gaps in real-world testing, swarm coordination, and scalable morphing architectures are identified, suggesting future research directions for developing robust, mission-adaptive UAV systems. Full article

This study addresses robust trajectory tracking for quadrotor unmanned aerial vehicles (QUAVs) under partial state measurements and bounded external disturbances. To this end, a control framework is introduced that integrates backstepping sliding mode control (BSMC) with a super-twisting observer (STO). In this scheme, only position and attitude are directly measured while the STO reconstructs the linear and angular velocities in real time. The estimated states are then fed into the control law, enabling accurate trajectory tracking and robust performance without full-state feedback or explicit disturbance compensation. The approach is validated through three simulation scenarios: nominal full-state feedback, observer-based control without disturbances, and observer-based control under bounded time-varying perturbations. Quantitative metrics confirm consistent tracking accuracy and closed-loop stability across all scenarios. These results demonstrate the effectiveness of the integrated BSMC–STO framework for QUAV operations in sensor-limited and disturbance-prone environments. Full article

Quadrotor UAV-based aeromagnetic survey for mineral exploration has become a crucial solution in modern airborne geophysics due to its prominent advantages of cost-effectiveness and high efficiency. During the detection process, the magnetic anomaly interference generated by the quadrotor UAV itself reduces the signal-to-noise ratio (SNR) of the target signal, and some noise overlaps with the target signal in both time and frequency domains. Traditional methods exhibit poor compensation capability for such noise. To address these issues, this paper proposes an aeromagnetic compensation method based on a time–frequency joint representation neural network. This method combines continuous wavelet transform (CWT) and bidirectional long short-term memory (Bi-LSTM) to establish a prediction model. It uses wavelet transform to extract the frequency variation characteristics of the UAV’s magnetic interference, and it inputs these frequency characteristics along with the original time-domain data into the Bi-LSTM network to predict the UAV’s noise. Bi-LSTM can effectively extract the temporal logical connections in time-series signals, thereby improving the accuracy of the compensation model and ensuring high robustness. In this study, magnetic interference data from quadrotor UAV compensation flights were collected for experiments to evaluate the performance of the proposed method. Experimental results show that the neural network fused with time–frequency features, when applied to UAV aeromagnetic compensation, significantly enhances the accuracy and robustness of the compensation method. To verify the method’s effectiveness in removing UAV-generated noise during actual exploration, aeromagnetic survey data from a specific area were compensated using this method. Full article

This paper introduces the ProVANT Simulator, a comprehensive environment for developing and validating control algorithms for Unmanned Aerial Vehicles (UAVs). Built on the Gazebo physics engine and integrated with the Robot Operating System (ROS), it enables reliable Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) testing. Addressing key challenges such as modeling complex multi-body dynamics, simulating disturbances, and supporting real-time implementation, the framework features a modular architecture, an intuitive graphical interface, and versatile capabilities for modeling, control, and hardware validation. Case studies demonstrate its effectiveness across various UAV configurations, including quadrotors, tilt-rotors, and unmanned aerial manipulators, highlighting its applications in aggressive maneuvers, load transportation, and trajectory tracking under disturbances. Serving both academic research and industrial development, the ProVANT Simulator reduces prototyping costs, development time, and associated risks. Full article

In this work, a fault detection method and a distributed consensus fault-tolerant control (FTC) scheme are proposed for multiple quadrotor unmanned aerial vehicles (multi-QUAVs) with actuator faults. In order to identify the actuator faults in time, an auxiliary state observer is constructed first. Subsequently, a fault detection scheme based on the observer error is presented, which can improve the early warning ability of the multi-QUAVs. Meanwhile, to handle unknown sudden faults, the Nussbaum function approach is combined with the consensus theory to design a distributed consensus FTC strategy for multi-QUAVs. Compared with the traditional direct fault estimation method using the projection function technique, the proposed Nussbaum-based FTC method can avoid the singularity problem of the controller in a simple way. Moreover, all error signals of the closed-loop system are proved to be uniformly ultimately bounded via Lyapunov stability theory and the consensus control algorithm. Finally, simulation comparison results indicate the early warning capability of the fault detection method and the formation maintenance performance of the developed fault-tolerant controller. Full article

This paper presents a reinforcement learning (RL)-based methodology for the online fine-tuning of PD controller gains, with the goal of bridging the gap between simulation-trained controllers and real-world quadrotor applications. As a first step toward real-world implementation, the proposed approach applies a Deep Deterministic Policy Gradient (DDPG) algorithm—an off-policy actor–critic method—to adjust the gains of a quadrotor attitude PD controller during flight. The RL agent was initially trained offline in a simulated environment, using MATLAB/Simulink 2024a and the UAV Toolbox Support Package for PX4 Autopilots v1.14.0. The trained controller was then validated through both simulation and experimental flight tests. Comparative performance analyses were conducted between the hand-tuned and RL-tuned controllers. Our results demonstrate that the RL-based tuning method successfully adapts the controller gains in real time, leading to improved attitude tracking and reduced steady-state error. This study constitutes the first stage of a broader research effort investigating RL-based PID, LQR, MRAC, and Koopman-integrated RL-based PID controllers for real-time quadrotor control. Full article

This paper proposes a robust control method of trajectory tracking for quadrotors under disturbance conditions, combining Model Predictive Control (MPC) and the Super-Twisting Algorithm (STA). MPC is a control strategy that solves an optimization problem by predicting the finite time future response from the model under control at each time step. However, MPC cannot guarantee control performance under disturbances such as modeling errors and wind gusts because it predicts future states of the control objects using a nominal model. To solve this problem, we propose a composite control method that uses Adaptive Super-Twisting Sliding Mode Disturbance Observer (ASTSMDO), which constrains the system to follow the MPC’s nominal model. The effectiveness of the proposed method is confirmed through numerical simulation. Compared to conventional MPC, the proposed controller achieves superior robustness and trajectory tracking performance under modeling error and wind disturbance. Full article

In response to the challenges faced by unmanned aerial vehicles (UAV) in cluttered environments such as forests, ruins, and pipelines, this study introduces a ground–air amphibious UAV specifically designed for personnel search and rescue in complex environments. By innovatively designing and applying a separation cage structure, the UAV’s capabilities for ground movement and aerial flight have been enhanced, effectively overcoming the limitations of traditional single-mode robots operating in narrow or obstacle-dense areas. This design addresses the occlusion issue of sensing components in traditional caged UAVs while maintaining protection for both the UAV itself and the surrounding environment. Additionally, through the innovative design of an H-shaped quadcopter frame skeleton structure, the UAV has gained the ability to perform steady-state aerial flight while also better adapting to the separation cage structure, achieving a reduced energy consumption and significantly improving its operational capabilities in complex environments. The experimental results demonstrate that the UAV prototype, weighing 1.2 kg with a 1 kg payload capacity, achieves a 40 min maximum endurance under full payload conditions at the endurance speed of 10 m/s while performing real-time object detection. The system reliably executes multimodal operations, including stable takeoff, landing, aerial hovering, directional maneuvering, and terrestrial locomotion with coordinated steering control. Full article

To enhance the rapid and stable tracking of a specified trajectory by quadcopter drones, while ensuring a degree of resistance to external wind disturbances, this paper proposes an integrated control strategy that combines an optimization algorithm and fuzzy control. In this system, both the position and attitude loops utilize second-order Linear Active Disturbance Rejection Control (LADRC) controllers, supplemented by fuzzy controllers. These controllers have been optimized using a modified crayfish optimization algorithm (MCOA), resulting in a dual-closed-loop control system. In comparisons with both the dual-closed-loop LADRC controller and the dual-closed-loop fuzzy control LADRC controller, the proposed method reduces the rise time by 52.87% in the X-channel under wind-free conditions, reduces the maximum trajectory tracking error by 86.37% under wind-disturbed conditions, and reduces the ITAE exponent by 66.2%, which demonstrates that the newly designed system delivers excellent tracking speed and accuracy along the specified trajectory. Furthermore, it remains effective even in the presence of external disturbances, it can reliably maintain the target position and the attitude angle, demonstrating strong resistance to interference and stability. Full article

In this paper, an image-based visual control scheme is proposed for a quadrotor aerial vehicle with unknown mass and moment of inertia. In order to reduce the impacts of underactuation in quadrotor dynamics, a virtual image plane is introduced and appropriate image moment features are defined to decouple the image features from the movement of the vehicle. Subsequently, based on the quadrotor dynamics, a backstepping method is used to construct the torque controller, ensuring that the control system has superior dynamic performance. Furthermore, an adaptive control scheme is then designed to enable online estimation of dynamic parameters. Finally, stability is formally verified through constructive Lyapunov methods, and performance test results validate the efficacy and robustness of the proposed control scheme. It can be verified through performance tests that the quadrotor successfully positions itself at the desired position under uncertain dynamic parameters, and the attitude angles converge to the expected values. Full article

Ensuring the safe operation of Unmanned Aerial Vehicles (UAVs) is crucial for both mission-critical and safety-critical tasks. In scenarios where UAVs must track airborne targets, they need to follow the target’s path while maintaining a safe distance, even in the presence of unmodeled dynamics and environmental disturbances. This paper presents a novel collision avoidance strategy for dynamic quadrotor UAVs during target-tracking missions. We propose a safety controller that combines a learning-based Control Barrier Function (CBF) with standard sliding mode feedback. Our approach employs a neural network that learns the true CBF constraint, accounting for wind disturbances, while the sliding mode controller addresses unmodeled dynamics. This unified control law ensures safe leader-following behavior and precise trajectory tracking. By leveraging a learned CBF, the controller offers improved adaptability to complex and unpredictable environments, enhancing both the safety and robustness of the system. The effectiveness of our proposed method is demonstrated through the AirSim platform using the PX4 flight controller. Full article

This paper addresses the problem of controlling quadcopters operating in an environment characterized by unpredictable disturbances such as wind gusts. From a control point of view, this is a nonstandard, highly challenging problem. Fundamentally, these quadcopters are high-order dynamical systems characterized by an under-actuated and highly nonlinear model with coupling between several state variables. The main objective of this work is to achieve a trajectory by tracking desired altitude and attitude. The problem was tackled using a robust control approach with a multi-loop nonlinear controller combined with extended Kalman filtering (EKF). Specifically, the flight control system consists of two regulation loops. The first one is an outer loop based on the backstepping approach and allows for control of the elevation as well as the yaw of the quadcopter, while the second one is the inner loop, which allows the maintenance of the desired attitude by adjusting the roll and pitch, whose references are generated by the outer loop through a standard PID, to limit the 2D trajectory to a desired set path. The investigation integrates EKF technique for sensor signal processing to increase measurements accuracy, hence improving robustness of the flight. The proposed control system was formally developed and experimentally validated through indoor tests using the well-known Parrot Mambo unmanned aerial vehicle (UAV). The obtained results show that the proposed flight control system is efficient and robust, making it suitable for advanced UAV navigation in dynamic scenarios with disturbances. Full article