A Comparative Study on Longitudinal Dynamics Stability Between Two Aircraft Models

The present work presents a comparative study on the longitudinal dynamic’s stability behavior for two aircraft models, namely the Learjet 24 and the Cessna 182. The longitudinal flight dynamics behaviors are evaluated by introducing a disturbance to the elevator. This device uses a single doublet impulse as well as multiple doublet impulses. The governing equation of longitudinal flight motion, which was derived based on a small perturbation theory and a linearized process by dropping the second order and above to the disturbance quantities, allowed one to formulate the governing equation of flight motion in the form of an equation known as the longitudinal equation of flight motion. This equation describes the flight behavior of an aircraft and can be expressed in the disturbance quantity as translational velocity in the x-direction u, angle of attack , and pitch angle . The implementation in the case of the Cessna 182 and the Learjet 24, where the Cessna 182 uses a single doublet impulse or a multiple doublet impulse, demonstrates that the aircraft response in these three variable states is better than that of the Learjet 24

Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks

In this paper, we present the performance analysis of a fully tuned neural network trained with the extended minimal resource allocating network (EMRAN) algorithm for real-time identification of a quadcopter. Radial basis function network (RBF) based on system identification can be utilised as an alternative technique for quadcopter modelling. To prevent the neurons and network parameters selection dilemma during trial and error approach, RBF with EMRAN training algorithm is proposed. This automatic tuning algorithm will implement the network growing and pruning method to add or eliminate neurons in the RBF. The EMRAN’s performance is compared with the minimal resource allocating network (MRAN) training for 1000 input-output pair untrained attitude data. The findings show that the EMRAN method generates a faster mean training time of roughly 4.16 ms for neuron size of up to 88 units compared to MRAN at 5.89 ms with a slight reduction in prediction accuracy

The Design of an Automatic Flight Control System and Dynamic Simulation for Fixed-Wing Unmanned Aerial Vehicle (UAV) using X-Plane and LabVIEW

This research focuses on developing an automatic flight control system for a fixed-wing unmanned aerial vehicle (UAV) using a software-in-the-loop method in which the PID controller is implemented in National Instruments LabVIEW software and the flight dynamics of the fixed-wing UAV are simulated using the X-Plane flight simulator. The fixed-wing UAV model is created using the Plane Maker software and is based on existing geometry and propulsion data from the literature. Gain tuning for the PID controller is accomplished using the pole placement technique. In this approach, the controller gain can be calculated using the dynamic parameters in the transfer function model and the desired characteristic equation. The proposed controller designs' performance is validated using attitude, altitude, and velocity hold simulations. The results demonstrate that the technique can be an effective tool for researchers to validate their UAV control algorithms by utilising the realistic UAV or manned aircraft models available in the X-Plane flight simulator

The Structural Design and Aerodynamics Analysis for a Hybrid VTOL Fixed-Wing Drone for Parcel Delivery Applications

A number of companies are experimenting with multicopter drones to deliver items to clients. Because electric planes have a restricted range, their flight range is usually constrained. However, if propelled by gasoline, electric multicopters drones can only travel a short distance because to high power consumption and noise difficulties. Despite their lower aerodynamic efficiency than fixed-wing aircraft, multicopters' ability to do vertical take-off and landing (VTOL) makes them ideal delivery vehicles. Hybrid fixed-wing VTOL systems with a tilting system that alters the flight mode could be an upgrade. The goal is to effectively manufacture a fixed-wing drone with appropriate structural design and a functional tilting mechanism that can take off vertically. SolidWorks and SIMNET aero were the two approaches used throughout the design software. The drone's aerodynamic qualities were investigated in order to better understand its behaviour, such as range of flight at a given altitude, stall speed, and maximum lift created, in order to determine the maximum parcel weight the drone can carry. The drone was built using SolidWorks 3D-Solid modelling and SIMNET aero design software. Because it is lightweight and sturdy, the tilting mechanism is 3D printed utilising (Polylactic Acid, PLA). The in-fill can also be changed to modify the strength. Following manufacturing, many test flights were done to investigate the drone's genuine behaviour and improve its performance. The drone's theoretical stall speed was found to be 11.43 m/s at free load and 12.74 m/s with a maximum payload of 500g. For maximum glide, the range calculated 1.2 kilometres. During test flights, the drone yaws to the left at 63.43 degrees at 4.879 degrees per second at 50% throttle. It slanted to the front nose down with a weight of 516 g while support was given at the tip of the left wing. The pitch rate was 2.5 degrees per second without payload and 3.12 degrees per second with the 516g payload. Experimental results that are similar to theoretical outcomes would be obtained with further design and calibration improvements

Real-time identification of an unmanned quadcopter flight dynamics using fully tuned radial basis function network

A quadcopter is a four-rotor unmanned aerial vehicle (UAV) with nonlinear and strongly coupled dynamics system. A precise dynamics model is important for developing a robust controller for a quadcopter. NN model capable to obtain the accurate dynamics model from actual data without having any govemmg mathematical model or priori assumptions. Recursive system identification based on neural network (NN) offers an alternative method for quadcopter dynamics modelling. Recursive learning algorithms, such as Constant Trace (CT) can be implemented to solve insufficient training data and over-fitting problems by developing a new model from real-time flight data in each time step. The modelling results from the NN model could be inaccurate due to inappropriate model structure selection, excessive number of hidden neurons and insufficient training data. Typically, the model structures and hidden neuron are determined by using trial and error approach to obtain the best network configuration. This study utilised a fully tuned radial basis function (RBF) neural network to obtain a minimal structure and avoid pre-determining the number of hidden neurons by introducing the adding and pruning neuron strategy. The prediction performance of the proposed fully tuned RBF was compared with Multilayer Perceptron (MLP), Hybrid Multilayer Perceptron (HMLP) and RBF networks trained with CT algorithm. The findings indicated that the fully tuned RBF with minimal resource allocating networks (MRAN) automatically selected seven neurons with 9.5177 % prediction accuracy and 5.89ms mean training time. The results also showed that the proposed extended minimal resource allocating networks (EMRAN) algorithm is capable to adapt with dynamics changes and infer quadcopter model with an even shorter training time (4.16ms) than MRAN and suitable for real-time system identification

Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm

The quadplane has become the favorite UAV platform for drone delivery. The dynamics and control of the quadplane UAV are extremely complex, unstable, and nonlinear due to its required capability to operate in multiple flight modes. The paper presents a method for autotuning attitude PID for a quadplane UAV using differential evolution (DE), X-Plane simulation, and neural network (NN)-based system identification. This study uses the DE algorithm and a radial basis function neural network (RBF NN) to create an autotuning PID and unified dynamics model for quadplanes, with the goal of improving their performance in delivery missions. The unified RBF model is then used for autotuning the PID controller using the DE algorithm. The proposed RBF-NN system identification can successfully predict the attitude dynamics of a quadplane in all flight modes with a coefficient of determination of R2 greater than 0.9 and a computing mean time of less than 5 ms. By iteratively modifying the control settings to achieve optimal performance, the DE algorithm replaces the requirement for manual PID tuning, which can be time-consuming and suboptimal. Comparing DE tuning to manual tuning, the results demonstrate a considerable improvement in quadplane roll and pitch performance of about 58.5%, 65.0%, and 74.0% in the overshoot, peak time, and rising time, respectively

Neural Network Based System Identification for Quadcopter Dynamic Modelling: A Review

A quadcopter is a rotorcraft with simple mechanical construction. It has the same hovering capability, similar to the traditional helicopter, but it is easier to maintain.  The quadcopter is very difficult to control due to its unstable system with highly coupled and non-linear dynamics. To design robust control algorithms, it is crucial to obtain precise quadrotor flight dynamics through system identification, which is a new method of finding the mathematical model of the dynamics system using the input-output data measurement. Neural network (NN) based system identification is excellent alternative modeling because it reduces development costs and time by avoiding governing equations and large aerodynamic database. NN based system identification has successfully identified the quadcopter dynamics with good accuracy. This paper gives an overview of the characteristic of the quadcopter, the first principle modeling, system identification of quadcopter, and implementation of NN based system identification in quadcopter platform