Ngoc Phi Nguyen

Nano quadcopter unmanned aerial vehicles (nQUAVs) offer easy deployment, high manoeuvrability, scalability, and low cost, but their small size and lightweight structure make them highly susceptible to external disturbances and system uncertainties, while limited onboard computation constrains the complexity of control algorithms. This paper presents an Active Adaptive Bidirectional Fuzzy Brain Emotional Learning (AA-BFBEL) controller for precise trajectory tracking and disturbance rejection in uncertain environments. Unlike conventional passive methods, the AA-BFBEL integrates a computationally efficient disturbance observer based on an Extended State Observer (ESO) into the BFBEL framework to estimate wind forces and system uncertainties in real time, which are then actively compensated without prior training. This design is particularly suited to nQUAVs with short flight durations and limited processing resources, in contrast to reinforcement or deep learning-based controllers. The controller was validated through simulations and real-world experiments on the Crazyflie nano quadcopter, tracking complex three-dimensional figure-8 trajectories-a demanding test scenario for small UAVs. Across all cases, the AA-BFBEL significantly outperformed the baseline BFBEL, conventional PID, sliding mode control (SMC), and active disturbance rejection control (ADRC), achieving up to 74% higher tracking accuracy and nearly 61% faster settling time under strong wind conditions. These results demonstrate that the AA-BFBEL delivers high precision, robustness, and adaptability, providing an effective and reliable control solution for nQUAV operations in dynamic and challenging environments.

This study outlines an approach to design an active fault tolerance control (FTC) system for satellite attitude systems that handle multiple actuator faults. First, a model is provided for the nonlinear attitude system of rigid satellites. Next, an actuator fault detection observer and a fault estimation observer are presented, which detects the time unknown actuator faults that occur and obtains estimated values. Using adaptive sliding mode control techniques, a fault tolerance attitude controller is designed that stabilizes the closed‐loop attitude system of rigid satellites in the case of multiple actuator faults. Finally, an experiment is provided demonstrating the superior performance of the active FTC system proposed in this study.

This paper proposes an innovative single image dehazing framework, termed Regional Saturation-Value Translation (RSVT), to address the color distortion problems commonly encountered in bright regions by conventional dehazing approaches. The proposed RSVT framework is developed based on two key insights derived from the HSV color space: first, the hue component shows negligible variation between corresponding hazy and haze-free points; and second, in the 2D saturation-value coordinate system, the majority of lines connecting hazy-clean point pairs tend to converge near the atmospheric light coordinates. Consequently, haze removal can be achieved through appropriate translations within the saturation-value coordinates. Additionally, a robust soft segmentation method that employs a morphological min-max channel is integrated into the framework. By combining the soft segmentation mask with the RSVT prior, a comprehensive single image dehazing framework is established. Experimental evaluations across various datasets demonstrate that the proposed approach effectively mitigates color distortion and successfully restores visually appealing images. Moreover, a case study involving actual flight test demonstrates the feasibility and effectiveness of the proposed approach in real-world scenarios. The code is available at https://github.com/tranleanh/rsvt.

This paper investigates the simultaneous design of a controller and Luenberger state observer for systems with time-delays, external disturbances, uncertainties, modeling errors, and unknown nonlinear perturbations. The state-feedback control approach and state-observer existence conditions are formulated using the Linear Matrix Inequalities (LMIs). By defining the estimation error, the equations of the closed-loop system are rewritten. External disturbances, uncertainties, unknown nonlinear perturbations, and constant time-delays are considered in system modeling. By using LMI techniques, the estimation error is converged to zero. Therefore, the time-delays, uncertainties, and external disturbance effects on the system output, which have not been considered simultaneously before, are minimized, and the closed-loop system is stabilized. The performance of the proposed approach is verified by simulation of two examples, Flexible-Link Manipulator (FLM) dynamics, and Continuous Stirred Tank Reactor (CSTR) system. These examples illustrate the reliability of the suggested method.

Nowadays, quadcopters are used widely in different applications, but their flight time is limited during operation. In this paper, a precision landing method based on a Kalman filter is proposed for an autonomous indoor persistent drone system that aims to increase the flight time of quadcopters. First, a local positioning system is used for tracking performance. Second, instead of using this local positioning system during the landing phase, a multi-ranger sensor is proposed to increase the accuracy of horizontal errors. Next, based on the relative position provided by the multi-ranger sensor, a Kalman filter technique is applied to estimate the relative velocity of the system, which is then applied to control the position of the quadcopter during the landing phase. Finally, a charging state machine law is proposed to charge the battery of three quadcopters sequentially. The experimental results demonstrate that the proposed concept based on a multi-ranger sensor can enhance the accuracy of the landing phase in comparison with the conventional method.

Unmanned aerial vehicles, especially quadcopters, play key roles in many real-world applications and the related quadcopter autonomous control algorithms have attracted a great deal of attention. In this paper, we address the vision-based autonomous landing problem of a quadcopter on a ground moving target. Firstly, we propose a disturbance observer-based control algorithm, consisting of a nonlinear disturbance observer and robust altitude and attitude controllers. This algorithm is based on the quadcopter dynamics model, and its stability is strictly proved using Lyapunov’s theory. Secondly, we develop an autonomous landing planner which we test for various landing scenarios to deliver improved reliability and accuracy of the landing mission. These theoretical studies are complemented by a numerical feasibility study, before demonstrating the effectiveness of our approach under actual flight conditions with an experimental quadcopter platform.

This article presents a formation tracking control method for the operation of multi-agent systems under disturbances. This study aims to ensure that the followers of a quadcopter converge into the desired formation while the center formation of the follower quadcopters tracks the leader’s trajectory within a finite time. The distributed finite-time formation control problem is first investigated using the fast terminal sliding mode control (FTSMC) theory. A disturbance observer is then integrated into the FTSMC to overcome the model uncertainties and bounded disturbances. Subsequently, the Lyapunov function is proposed to ensure the stability of the system. It is shown that formation tracking control can be achieved even in the presence of disturbances. Simulation and experimental results verify the effectiveness of the proposed formation tracking control method compared to existing ones.

A trajectory tracking control for quadcopter unmanned aerial vehicle (UAV) based on a nonlinear robust backstepping algorithm and extended state/disturbance observer (ESDO) is presented in this paper. To obtain robust attitude stabilization and superior performance of three-dimension position tracking control, the construction of the proposed algorithm can be separated into three parts. First, a mathematical model of UAV negatively influenced by exogenous disturbances is established. Following, an extended state/disturbance observer using a general second-order model is designed to approximate undesirable influences of perturbations on the UAVs dynamics. Finally, a nonlinear robust controller is constructed by an integration of the nominal backstepping technique with ESDO to enhance the performance of attitude and position control mode. Robust stability of the closed-loop disturbed system is obtained and guaranteed through the Lyapunov theorem without precise knowledge of the upper bound condition of perturbations. Lastly, a numerical simulation is carried out and compared with other previous controllers to demonstrate the great advantage and effectiveness of the proposed control method.

In this paper, a fault-tolerant control method is proposed for a hexacopter under uncertainties. The proposed method is based on adaptive-sliding-mode control (ASMC) and a control allocation scheme. First, a mathematical model of the hexacopter is employed with model uncertainties. Next, the control allocation strategy is combined with ASMC to handle actuator faults, which can distribute the virtual control signal to redundant actuators. A modified fault-tolerant control is proposed to overcome this virtual input saturation. Finally, the system stability is validated using the Lyapunov theory. The performance of the proposed method is compared with that of normal ASMC. The simulation results show that the suggested strategy can realize quicker compensation under faulty conditions.