This paper presents an analysis of a Total Energy Control System (TECS) introduced by Lambregts to control unmanned aerial vehicle (UAV) velocity and altitude by using the total energy distribution. Furthermore, an extended Kalman filter (EKF) approach was used to predict aircraft response in terms of angular rates and linear acceleration during a test flight campaign. From both approaches, state equations were obtained to model the aircraft using Matlab-Simulink. From an aerodynamic study, airplane characteristics were obtained in terms of non-dimensional derivatives and compared to those obtained from the experimental methods. It was determined that TECS approach was very accurate; however, disturbance errors could be decreased by adjusting some model parameters. On the other hand, it was difficult to obtain a real estimation from the EKF method due to the presence of turbulence during flight and the relatively low inertia of the scale model. Dynamic characteristics were validated using a low-cost inertial sensor that cab be easily integrated in UAV platforms. The gathered data can be used to predict model characteristics by integrating the information into flight simulators for future design development.

Unmanned aerial vehicles (UAVs) require precise system identification for optimal performance and safety, yet sensor noise and signal distortion frequently compromise data quality. Recent studies have explored various approaches to mitigate these issues. However, this study introduces a novel method that utilizes wavelet transform techniques, distinctively enhancing UAV sensor signal processing. Unlike conventional methods that primarily focus on noise reduction, this approach employs multi-resolution wavelet decomposition to denoise and align signals effectively, crucial for accurate system identification. This systematic exploration of various wavelet bases and the application of the output error method for correlating signals provide a unique combination not extensively covered in current literature. The technique was validated using simulated sensor data at 50 Hz from a small UAV platform, the Multiplex®Fun Cub, specifically targeting longitudinal dynamics response. Results demonstrated substantial improvements in signal quality, with significantly enhanced correlation coefficients, showcasing the potential of our wavelet techniques to refine UAV system analysis. This paper presents a comprehensive framework for applying wavelet-based techniques in UAV system identification, significantly advancing the robustness and reliability of identification processes and distinguishing our work from existing methods by its integration of wavelet decomposition and advanced system identification techniques.

The efficiency of Unmanned Aerial Vehicles (UAVs) in precision agriculture significantly depends on their design and functional adaptability. This paper presents a focused study on the lateral-directional dynamics of the HyVprop UAV, featuring a distinctive V-tail inverted configuration, tailored for advanced crop imaging tasks. Building prior wavelet transform-based methodologies that addressed sensor quality issues, this research adapts these techniques to the unique aerodynamic characteristics and challenges posed by the HyVprop’s design. The research uses multi-level wavelet decomposition to reduce sensor noise and synchronize lateral-directional signals accurately. This process is essential for precise system identification, which plays a key role in the UAV’s navigation and operational effectiveness within agricultural settings. Enhanced by the Output Error Method, which is refined to improve signal correlation specifically for the V-tail configuration, this approach is tested with simulated sensor data. The findings demonstrate significant improvements in signal quality and correlation coefficients, establishing a comprehensive framework for UAV system identification that enhances reliability and precision in crop monitoring. The paper not only confirms the methodology’s effectiveness but also highlights the specific advantages of adapting system identification techniques to unconventional UAV designs like the V-tail inverted HyVprop.

In Colombia there are populated areas near cities that show accelerated industrial growth. The demographic distribution divides the residential zones from zones with the presence of industries. However, there are no studies on the impact of emissions on the population. It is necessary to know the behavior of anthropogenic emissions when interacting with air currents. For this reason, AeroTech Research Group of the University of San Buenaventura is developing a two-stage project for pollution research around industrial facilities. First stage determines the capabilities of a platform to fulfill the mission in high altitude and high temperature conditions. This article describes the integration of the automatic control system, the simulation and flights of the AQUILA aircraft. The flights were developed to configure the gains of the PID controllers and thus validate the performance using a pixhawk control system. The results are used to validate stability and control calculations and test the equipment developed by the University of San Buenaventura Medellin and the University of Medellin in the second stage. This research showed that the use of aircraft is essential to support government entities in the evaluation and control of air quality by flying in an air column with a diameter of 300 m and a maximum height of 1km.

The advanced Quantum Information Technologies subject for Ph.D. students in Electronics Engineering and ICT consists of three parts. A few review lectures concentrate on topics which may be of interest for the students due to their fields of research done individually in their theses. The lectures indicate the diversity of the QIT field, resting on physics and applied mathematics, but possessing wide application range in quantum computing, communications and metrology. The individual IQT seminars prepared by Ph.D. students are as closely related to their real theses as possible. Important part of the seminar is a discussion among the students. The task was to enrich, possibly with a quantum layer, the current research efforts in ICT. And to imagine, what value such a quantum enrichment adds to the research. The result is sometimes astonishing, especially in such cases when quantum layer may be functionally deeply embedded. The final part was to write a short paragraph to a common paper related to individual quantum layer addition to the own research. The paper presents some results of such experiment and is a continuation of previous papers of the same style.

This study presents an advanced approach to modeling Unmanned Aerial Vehicle (UAV) dynamics by integrating Artificial Neural Networks (ANNs) and the Output Error Method (OEM). Moreover, the research analyzes the longitudinal flight characteristics of the Multiplex FunCub R/C, using data from designed flight tests complemented by simulations incorporating sensor noise and drift effects. Furthermore, the study captures a comprehensive range of aerodynamic responses essential for precise system identification by employing a multistep elevator input signal. In addition, the OEM approach, a traditional parameter estimation method, offers robust statistical estimation by minimizing the discrepancies between measured and predicted outputs. However, due to the nonlinear complexities inherent in UAV flight dynamics, the study also explores ANNs, leveraging their capability to model intricate nonlinear behaviors without requiring predefined aerodynamic parameters. Subsequently, the performance of both methodologies is critically evaluated against measured aerodynamic coefficients, revealing ANNs’ superior adaptability in accurately predicting complex aerodynamic interactions compared to OEM. Consequently, results indicate notable reductions in relative error, particularly in challenging aerodynamic coefficients. Overall, this research not only highlights the comparative advantages of ANNs in UAV system identification but also lays an initial structure for future advancements in UAV modeling and control systems.