Determination of Parameters during Quasi-Steady Stall Maneuver Using Genetic Algorithm

The current work offers the determination of longitudinal aerodynamic derivatives during flight manoeuver at angles of attack near the stall. The flight manoeuver near stall is highly non-linear in nature due to separated flow at such elevated angles of attack. Kirchoff’s model for Quasi-Steady Stall Modelling (QSSM) is employed to represent the non-linear nature of aerodynamics during flight manoeuver at elevated angles of attack close to the stall. The Genetic Algorithm (GA) optimized output error method is utilized for estimating the parameters specific to stall characteristics and longitudinal aerodynamics of the ATTAS aircraft. The comparative evaluation of the parameter estimates with the estimates obtained by using Maximum Likelihood technique is employed to assess the efficacy of the proposed method for highly non-linear applications. The comparative assessment of the estimates along with robust statistical analysis evidence that the proposed method can be a suitable parameter estimation alternative method for non-linear application

Sensor Based System Identification in Real Time for Noise Covariance Deficient Models

System identification methods have extensive application in the aerospace industry’s experimental stability and control studies. Accurate aerodynamic modeling and system identification are necessary because they enable performance evaluation, flight simulation, control system design, fault detection, and model aircraft’s complex non-linear behavior. Various estimation methods yield different levels of accuracies with varying complexity and computational time requirements. The primary motivation of such studies is the accurate quantification of process noise. This research evaluates the performance of two recursive parameter estimation methods, viz.; First is the Fourier Transform Regression (FTR). The second approach describes the Extended version of Recursive Least Square (EFRLS), where E.F. refers to the Extended Forgetting factor. Also, the computational viability of these methods was analyzed for real-time application in aerodynamic parameter estimation for both linear and non-linear systems. While the first method utilizes the frequency domain to evaluate aerodynamic parameters, the second method works when noise covariances are unknown. The performance of both methods was assessed by benchmarking against parameter estimates from established methods like Extended Kalman Filter (EKF), Unscented Kalman Filter (UNKF), and Output Error Method (OEM)

Design & Implementation of an Electric Fixed-wing Hybrid VTOL UAV for Asset Monitoring

Fixed-wing unmanned aerial vehicles (UAVs) offer the best aerodynamic efficiency required for long-distance or high-endurance applications, albeit their runway requirement for take-off and landing in comparison with quadcopters, helicopters, and flapping-wing UAVs that can perform vertical take-off and landing (VTOL). Integrating a multirotor system with a fixed-wing UAV imparts VTOL capabilities without significantly compromising fixed-wing aerodynamic efficiency, endurance, payload capacity or range. Documented system design approaches to address various challenges of such fusion processes are sparse. This research proposes a holistic approach for designing, prototyping, and testing an electric-powered fixed-wing hybrid VTOL UAV. The proposed system design approach augments the standard aircraft design process with additional steps to integrate VTOL capabilities. Separate fixed-wing and multirotor designs were derived from the frozen mission requirements, which were then fused. The process used simulation for modeling and evaluating alternatives for the hybrid UAV created using standard aircraft design equations. We prototyped and instrumented the final design to validate operational capabilities through test flights. Multiple flight trials identified the ideal combination of Lithium-Polymer (Li-Po) batteries for VTOL (8000mAh) and fixed-wing (14000mAh) modes to meet the endurance and range requirements. The redundant power supplies also increased the survivability chances of the hybrid UAV during failures

Modeling and prediction of powered parafoil unmanned aerial vehicle throttle and servo controls through artificial neural networks

This study proposes a framework for developing a realistic model for throttle and servo control algorithms for a powered parafoil unmanned aerial vehicle (PPUAV) using artificial neural networks (ANNs). Two servo motors on an L-shaped platform, control and steer the PPUAV. Six degrees of freedom mathematical model of a dynamic parafoil system is built to test the technique's efficacy using a simulation in which disturbances mimic actual flights. A guiding law is then established, including the cross-track error and the line-of-sight approach. Furthermore, a path-following controller is constructed using the proportional-integral derivative, and a simulation platform was created to evaluate numerical data illustrating the route's validity following the technique. PPUAV was developed, built, and instrumented to collect real-time flight data to test the controller. These dynamic characteristics were sent into the ANN for training. A diverging-converging design was identified to obtain the best consistency between predicted and observed throttle and servo control values. For a comparable flight route, the control signal of the simulated model is compared with those of the actual and ANN-predicted models. The comparative findings show that the ANN-predicted and actual control inputs were almost identical, with an 80%–99% match. However, the simulated response showed deviation from the actual control input, with an accuracy of 50%–80%