Correction: Julia Programming for Real-Time Flight Control on Embedded Platforms

A high-performance flight control approach for quadrotors using a modified active disturbance rejection technique

This paper presents vision based flight control of an indoor micro helicopter in the corridor of buildings. Since the helicopter has limited payload, a small single wireless camera is employed on a sole sensor device. Visual information obtained by the camera is used to estimate position of the helicopter related to the corridor. We focus on edge lines between walls and floor of the corridor and vanishing points on the shot image. Real time measurement and flight control are performed with the real indoor micro helicopter. At the first step, the experiments are held in the ideal environment which is simulated to the corridor and autonomous hovering flight was succeeded. Finally, we have experiments in the real corridor and achieve hovering flight and straight-ahead fight control.

This brief presents an integrated trajectory planning and attitude control framework for six-degree-of-freedom (6-DOF) hypersonic vehicle (HV) reentry flight. The proposed framework utilizes a bilevel structure incorporating desensitized trajectory optimization and deep neural network (DNN)-based control. In the upper level, a trajectory data set containing optimal system control and state trajectories is generated, while in the lower level control system, DNNs are constructed and trained using the pregenerated trajectory ensemble in order to represent the functional relationship between the optimized system states and controls. These well-trained networks are then used to produce optimal feedback actions online. A detailed simulation analysis was performed to validate the real-time applicability and the optimality of the designed bilevel framework. Moreover, a comparative analysis was also carried out between the proposed DNN-driven controller and other optimization-based techniques existing in related works. Our results verify the reliability of using the proposed bilevel design for the control of HV reentry flight in real time.

In this study, it is examined that simultaneous flight control system and lateral and longitudional state-space model of a Unmanned Aerial Vehicle (UAV) and real time application. For this purpose an UAV whose wing and tail unit can be assembled to fuselage from different points in a prescribed interval and whose wing and tail can move forward and backward independently in tail to nose direction is manufactured. Following this, an autopilot is purchased and it lets change of P, I, D coefficients in certain intervals. First, dynamic model, and longitudinal and lateral state space models of UAV are obtained and then simulation model of UAV is reached. At the same time block diagram of autopilot system and modeling of it in MATLAB/Simulink environment are found. After these, using these two models and also benefiting and adaptive stochastic optimization method namely SPSA, simultaneous design of UAV and autopilot is done in order to minimize a cost function consisting of rise time, settling time and maximum overshoot. Therefore, primarily autonomous performance is maximized in computer environment. Moreover, high performance is observed by looking at simulation responses and real-time flights.

There has been a good aspiration and hence considerable efforts in the development of flapping-wing micro aerial vehicles to increase their applications and utilizations. Owing to their sizes, their ability to carry payloads is stringent, and they are subject to unsteady aerodynamics. Developments of their flight controls are thus required to address instability issues. On-board transmission of flight dynamics data is necessary for appropriate flight control development. This study constructs a communication system for the acquisition of real-time flight telemetry. This is done via an onboard 9 degrees-of-freedom inertial unit, a local area network transmitter, and a motor driver. It overcomes the issues of weight, the center of gravity, and other stringent configuration problems for an agility airborne vehicle that transmits flight dynamic telemetry. This development enables further practical applications for MAV.

In order to develop a new aircraft model for FlightGear, this paper has described the establishing process of the 3D aircraft model and the configuration files which are mainly described in XML format. Aiming at finding an effective way to design and test the control law for the UAV, a real-time visual flight simulation system is constructed based on FlightGear. In this simulation system, the FlightGear visual module is driven to simulate the real-time 3D UAV model which contains flight states, weather condition and geographic environment. And the fight simulation data is sent to the FlightGear simulator through autopilot I/O port by UDP. This simulation system plays an important role in designing and testing the control law, and has made good effect on validating the control law of the UAV. The simulation results have shown that this visual flight simulation system is economic, valid, feasible, and has great progress on enhancing the efficiency of the project.

An investigation is made in this paper of the possibility of enhancing the performance of controllers of unstable systems while retaining safety critical function. In this case, a general dynamics F16 fighter is considered in simulation. A fuzzy logic controller is designed and its membership functions tuned by multiobjective genetic algorithms in order to design an augmented flight controller with enhanced manouverability which still retains safety critical operation. The controller is assessed in terms of pilot effort and thus reduction of pilot fatigue. The controller is incorporated into a six degree of freedom real-time flight simulator, and flight tested by a qualified pilot instructor.

Formation flying techniques, demonstrated by migratory birds, have the potential to significantly save energy when two or more transport aircraft fly the same route at the same time. In fuel-saving formations, aircraft are flying straight trajectories for most of the time. For proof of concept, however, how vertical and lateral maneuvers can be flown has to be investigated. Those investigations require a flight simulator that is equipped with adequate flight mechanical models. Not much research exists on maneuvers of formation. The paper addresses the components that are needed to perform this research with real-time flight simulations. They are, first, a method to model wake vortices on curved trajectories; second, a formation flight control system, which comprises a method for the relative position determination, the design of control laws, and suitable cockpit displays; and third, techniques that allow vertical and lateral flight maneuvers. Two different strategies to position the following aircraft relative to the wake vortex during turns are described, and simulation results show their differences in terms of fuel savings and passenger comfort. Piloted flight simulator tests confirmed the usability of the proposed flight techniques for formation maneuvers, and the participating airline pilots indicated general consent to the concept.

The Electronic Stability Program 32 Spy (ESP32 Drone) is a low-cost, wireless-enabled unmanned aerial vehicle (UAV) designed using the ESP32 microcontroller. The system focuses on achieving real-time flight stabilization through sensor fusion and PID-based control algorithms. By integrating IMU sensors, GPS modules, and wireless communication technologies such as Wi-Fi and Bluetooth, the drone ensures stable flight, remote monitoring, and telemetry transmission. The project aims to provide an affordable alternative to traditional flight controllers while maintaining reliable performance. The implementation includes real-time sensor processing, motor speed control through ESCs, and wireless communication for monitoring and control. This system is suitable for educational, research, and IoT-based drone applications

Autonomous systems, including unmanned aerial vehicles and commercial airplanes, are increasingly integrated into modern aircraft to minimize pilot errors while enhancing flight control. Ensuring flight safety requires accurate detection of anomalies in sensor data that causes error. This study, AnoSense, proposes an autoencoder-based deep neural network designed to detect anomalies in an unmanned aerial vehicle. AnoSense processes 20 flight sensor parameters to identify irregularities that could compromise operational safety. The model is trained and evaluated using NASA’s DASHlink anomaly data set, achieving 97.07% precision, outperforming conventional deep learning methods. Additionally, AnoSense is optimized for deployment on resource-constrained edge devices, with implementation and performance validation conducted on a Raspberry Pi. The experimental results demonstrate the feasibility of real-time flight anomaly detection on embedded systems, making AnoSense a promising solution to improve aircraft safety through edge computing.