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A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challenging due to their irregular shape and low Reynolds number behavior. This work proposes an approach to model the full translational dynamics of a quadrotor UAV by a feedforward neural network, which is adopted as the prediction model in a model predictive controller (MPC) for precise position control. The raw flight data are collected by tracking various pre-designed trajectories with PX4 autopilot. The neural network model is trained to predict the linear accelerations from the flight log. The neural network-based model predictive controller is then implemented with the automatic control and dynamic optimization toolkit (ACADO) to achieve real-time online optimization. Software in the loop (SITL) simulation and indoor flight experiments are conducted to verify the controller performance. The results indicate that the proposed controller leads to a 40% reduction in the average trajectory tracking error compared to the traditional PID controller.

Virtual commands serve as the essential framework for establishing interaction between the virtual pilot and the MCP in autopilot scenarios. Conventional proportional-integral-derivative (PID) controllers are insufficient to ensure accurate flight trajectories due to system hysteresis. To overcome this limitation, a quaternary correlation prediction compensation PID (QCPC-PID) approach is introduced for computing virtual heading commands in autopilot tasks. The method integrates multi-feature statistics, entropy-based predictive compensation, and quaternary correlations. First, flight trajectory error statistics are dynamically calculated using signed error distances to assess deviation levels. Second, a predictive structure based on information entropy is applied to enhance PID compensation. Third, quaternary correlation dependence is established to generate virtual heading commands. The findings confirm the effectiveness of the method in improving flight convergence. The incorporation of predictive structures and quaternary correlations is critical for achieving predictive compensation during PID tuning, thereby reducing flight trajectory deviations. The quaternary correlation prediction compensation method ensures superior performance of PID control in modeling heading adjustment behavior under autopilot conditions.

Virtual commands are significant to model human–computer interactions in autopilot flight missions. However, the huge system hysteresis makes it difficult for proportional–integral–derivative (PID) algorithms to generate the commands that promise better flight convergence. An adaptive trigger compensation neural network method is proposed to dynamically tune the PID parameters, simulating the process of deciding virtual heading commands and performing heading adjustments for virtual pilots. The method consists of trigger filtering, dynamic updating, and compensation synthesis. First, the necessary historical errors are adaptively selected by the threshold trigger filter for better error utilization. Second, error-based initialization is introduced in the neural network PID update process to improve adaptiveness in the initial settings of PID parameters. Third, the parameters are synthesized via error compensation to compute virtual heading commands for acquiring more convergent flight trajectories. The adaptive filter, error-based initialization, and compensation are important to improve the backward propagation neural network in tuning PID parameters. The results demonstrate the advance of the method in simulating heading adjustment behaviors and reducing flight trajectory deviation and fluctuation. The adaptive trigger compensation neural network can enhance the convergent performance of the PID algorithm during autopilot flight scenarios.

The tilt trirotor unmanned aerial vehicle (UAV) is a novel aircraft that has broad application prospects in transportation. However, the development progress of the aircraft is slow due to the complicated control system and the high cost of the flight experiment. This work attempts to overcome the problem by developing a hardware-in-the-loop (HIL) simulation system based on a heavily developed and commercially available flight simulator X-Plane. First, the tilt trirotor UAV configuration and dynamic model are presented, and the parameters are obtained by conducting identification experiments. Second, taking the configuration of the aircraft into account, a control scheme composed of the mode transition strategy, hierarchical controller, and control allocation is proposed. Third, a full-scale flight model of the prototype is designed in X-Plane, and an interface program is completed for connecting the autopilot and X-Plane. Then, the HIL simulation system that consists of the autopilot, ground control station, and X-Plane is developed. Finally, the results of the HIL simulation and flight experiments are presented and compared. The results show that the HIL simulation system can be an efficient tool for verifying the performance of the proposed control scheme for the tilt trirotor UAV. The work contributes to narrowing the gap between theory and practice and provides an alternative verification method for the tilt trirotor UAV.

Unmanned Aerial Vehicles (UAVs) are aircraft that can be manually operated or autonomously guided through an autopilot. In the last case, the system is responsible for stabilising the aircraft and executing guidance tasks such as path-following. In literature, several path-following algorithms were proposed for straight lines and loiter paths. Previous comparisons generally consider straight line algorithms in a 2D space using the kinematic model of an aircraft. In order to complement existing research, this paper compares four 3D path-following algorithms for loiter paths under different wind intensities. Tests are made through Software-in-the-Loop (SiL) simulations using the dynamic model of a fixed-wing UAV. Furthermore, a genetic algorithm is employed to tune the parameters and the analysis is carried out under varying wind conditions. The algorithms compared are four well-known geometric methods: Carrot-Chasing (CC), Non-Linear Guidance Law (NLGL), Pure Pursuit and Line-of-Sight (PLOS) and Vector Field (VF). Results show that Vector Field has the smallest errors, while PLOS is the most resistant to wind disturbance.

This study proposes a ducted-fan flight array (DFA) system that can change the array based on the mission environment and validate the feasibility through ground and flight tests. This DFA can carry out normal formation flight as separated UAV members and can also cooperate as a single entity by physical connection. The ducted-fan unmanned aerial vehicle (UAV) is manufactured in-house and equipped with a connected surface and an assembly device on the side to perform connection and separation tasks. Moreover, the control system was designed using an open-source autopilot environment, and the communication environment for multi-UAV flight was constructed using the Robot Operating System (ROS). Then, ground and preliminary experimental tests verified the feasibility and performance of the DFA system for connected and separated flight.

Inconsistency in the structural strengths of a membrane wing under positive and negative loads has undesirable impacts on the aeroelastic deflections of the wing, which results in more significant flight control system modeling errors and worsens the performance of the aircraft. In this paper, an integrated dynamic model is derived for a membrane-wing aircraft based on the structural dynamics equation of the membrane wing and the flight dynamics equation of the traditional fixed wing. Based on state feedback control theory, an autopilot system is designed to unify the flight and control properties of different flight and wing deformation statuses. The system uses models of different operating regions to estimate the dynamic response of the vehicle and compares the estimation results with the sensor signals. Based on the compared results, the autopilot can identify the overall flight and select the correct operating region for the control system. By switching to the operating region with the minimum modeling error, the autopilot system maintains good flight performance while flying in turbulence. According to the simulation results, compared with traditional rigid aircraft autopilots, the proposed autopilot can reduce the absolute maximum attack angles by nearly 27% and the absolute maximum wingtip twist angles by nearly 25% under gust conditions. This enhanced robustness and stability performance demonstrates the autopilot’s significant potential for practical deployment in micro-aerial vehicles, particularly in applications demanding reliable operation under turbulent conditions, such as military surveillance, environmental monitoring, precision agriculture, or infrastructure inspection.

Urban air mobility is expected to play a role in improving transportation of people and goods in growing urban areas while contributing to sustainable urban growth and zero-emissions future aviation. The research presented herein computationally investigated the performance of control laws for a generic Urban Air Taxi (UAT) subjected to empirically-developed urban airflow disturbances. This involved developing a representative flight dynamics model of a UAT in steady level cruise flight with an inner-loop autopilot. Active Disturbance Rejection Control (ADRC) and Proportional-Integral-Derivative (PID) control laws were implemented to investigate the controlled and uncontrolled acceleration responses and compare them to the acceleration limits in ISO 2631. Using a linear flight dynamics model, ADRC demonstrated improved performance over PID control with equal initial tuning effort. PID was able to reduce passenger accelerations to unharmful, though still uncomfortable, levels while ADRC further reduced the lateral accelerations to comfortable levels.

Modern aviation technology development heavily relies on computer simulations. SIL (Software-In-The-Loop) simulations are essential for evaluating autopilots and control algorithms for multi-rotors, including drones and other UAVs (Unmanned Aerial Vehicle). In such simulations, it is possible to compare the flight parameters achieved by flying platforms using various commercial autopilots widely used in the UAV sector. This research aims to provide objective and comprehensive insights into the effectiveness of different autopilot systems This article examines the simulated flight test results of a drone performing the same mission using different autopilot systems. The X-Plane software was used as an environment to simulate the dynamics of the drone and its surroundings. Matlab/Simulink r2023a provided the interface between autopilot software and X-Plane models. Those methods allowed us to obtain an appropriate comparison of the autopilot systems and indicate the main differences between them. This research focused on analyzing UAV flight characteristics such as stability, trajectory tracking, response time to control changes, and the overall effectiveness of autopilots. Various flight scenarios including take-off, landing, flight at a constant altitude, dynamic manoeuvrers and, flight along a planned trajectory were also examined. In order to obtain the most accurate and realistic results, the tests were carried out in various weather conditions. The aim of this research is to provide objective data and analysis to compare the performance of commercial autopilots. This method offers several advantages, including cost-effective testing, the ability to test in diverse environmental conditions, and the evaluation of autopilot algorithms without the need for real hardware. The findings of this study may have a considerable impact on how autopilot designers and developers choose the best platforms and technologies for their projects. Future research on this topic will compare the obtained data with flight test data.

Despite many research studies focus on strategies to improve autopilot capabilities and bring artificial intelligence onboard Unmanned Aircraft Systems (UAS), there are still few experimental activities related to these vehicle performance under unconventional weather conditions. Air temperature and altitudes directly affect thrust and power coefficients of small scale propeller for UAS applications. Reynolds numbers are usually within the range 10,000 to 100,000 and important aerodynamic effects, such as the laminar separation bubbles , occur with a negative impact on propulsion performance. The development of autonomous UAS platforms to reduce pilot work-load and allow Beyond Visual Line of Sight (BVLOS) operations requires experimental data to validate capabilities of these innovative vehicles. High quality data are needed for a deep understanding of limitations and opportunities of UAS under unconventional flight conditions. The primary objective of this article is to present the characterization of a propeller and a quadrotor capabilities in a pressure-climate-controlled chamber. Mechanical and electrical data are measured with a dedicated test setup over a wide range of temperatures and altitudes. Test results are presented in terms of thrust and power coefficient trends. The experimental data shows low Reynolds numbers are responsible for degraded thrust performance. Moreover, details on brushless motor capabilities are also discussed considering different temperature and pressure conditions. The experimental data collected in the test campaign will be leveraged to improve UAS design, propulsion system modelling as well as to provide guidelines for safe UAS operations in extreme environments.