Journal of Aerospace Science and Technology
Volume:9 Issue: 2, Summer and Autumn 2012

An accurate calibration of inertial measurement unit errors is increasingly important as the inertial navigation system requirements become more stringent.Developing calibration methods that use as less as possible of IMU signals has 6-DOF gimballed IMU in space-stabilized mode is presented. It is considered as held stationary in the test location incorporating 15 different error sources, including accelerometers bias, scale factor error, gyros drift, initial alignment error, and IMU case installation error. Using kinematic relations between IMU platform, IMU body, and IMU platform centered inertial reference frame, six differential equations of the only system-level IMU velocity and gimbal angle are derived. Then the extracted model is validated for error-free case using Sim-Mechanics MATLAB SIMULINK tools to evaluate the introduced mathematical model. Simulation results for 24 hours point out the correctness of the developed model in error-free case. The IMU error analysis methodology incorporates gimbal angle measurements taken during one and a half hour with 9 platform attitudes test to estimate IMU error sources. Without the need to install IMU at rotating table, different platform attitudes are achieved using consequent rotations of gimbals. IMU error sources estimation is accomplished off-line. This paper describes the design and test results of a new gimballed IMU calibration method without using a rotating table, and error model development methodology formulated to support the design and test of EKF algorithm and two optimal smoothers:forward-backward and RTS. Results obtained from EKF implementation indicate that the technique is comprehensive and accurate, and requires less specialized test equipments. Also, results show that constant states are not smooth-able. Ad-

An extensive experimental investigation is conducted to study the effect of canard position relative to the fuselage reference line on the aerodynamic forces of a fighter type configuration model. Aerodynamic forces at different flight conditions are measured in a subsonic wind tunnel. The wing and the canard have triquetrous shapes. Experiments are conducted at Reynolds number of 342209 and at 0 to 40 degree angles of attack. The results show that canard increases the lift and drag forces while it decreases the static stability of the model. The canard at it’s up position increases the aerodynamic forces and decreases the static stability i.e., superior maneuver capability. Furthermore, when the forward position of the canard is considered, both lift and drag are increased; however, the overall aerodynamic efficiency and also more static stability are improved. The canard at up and forward position respect to the wing-body is an appropriate selection for the best performance at moderate to high angles of attack among the various wing-canard-body configurations.

In this paper, an approximate solution of sensitivity matrix of required velocity with final velocity constraint is derived using a piecewise linear gravity assumption. The total flight time is also fixed for the problem. Simulation results show the accuracy of the method. Increasing the midway points for linearization, increases the accuracy of the solution, which this, in turn, depends on the total flight time.

There is a growing interest in the modeling and control of model helicopters using nonlinear dynamic models and nonlinear control. Application of a new intelligent control approach called Brain Emotional Learning Based Intelligent Controller (BELBIC) to design autopilot for an autonomous helicopter is addressed in this paper. This controller is applied to a nonlinear model of a helicopter. This methodology has been previously proved to present robust characteristics against disturbances and uncertainties existing in the system. The simulation results of this controller has compared with a PID controller. The policies for PID and BELBIC controller are the same. The controller design goal is that the helicopter tracks a special maneuver to reach the commanded height and heading. The performance of the controllers is also evaluated for robustness against perturbations with inserting a high frequency disturbance. Simulation results show a desirable performance in both tracking and improved control signal by using BELBIC controller.

Improvement of the launch phase of a jet powered Unmanned Aerial Vehicle (UAV) with Jet Assisted Take Off (JATO), has been the subject of attention in the UAV industry. Use of flight simulation tools reduces the risk and required some amount of flight testing for complex aerospace systems. Full nonlinear equations of motion are used to study and simulate this maneuver and three case studies of their application to UAV launch phase problems are presented. Attempt was made to explain some aspects that were not definite in the test by simulation. The result of the examination was satisfactory. The second and third examples involve the flight test of the UAV. These two applications are typical of launch phase problems. The second example demonstrated a good applicability of this technique to improve and increase the stability of the UAV during launch. In the third example, the UAV in the presence of headwind showed that simulation and real test had a good coincidence.

This paper investigates the parametric study of the empty and foam-filled end-capped tubes under quasi static and dynamic loadings. The numerical crash analysis of the empty and foam-filled tubes was performed using the explicit finite element code ABAQUS- explicit. Satisfactory agreements were generally achieved between the numerical and experimental results. In order to determine the crash behavior of the empty and foam filled tubes under dynamic impact, the dynamic amplification factor which relates the quasi-static results to dynamic responses was determined. The influence of the various parameters on the dynamic responses is investigated and then compared with the quasi-static results.

In this study, two novel learning algorithms have been applied on Radial Basis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) and Gaussian Mixture Model (GMM) techniques are proposed to significantly minimize the error functions. The main idea is concerning the various strategies to optimize the procedure of Gradient Descent (GD) in terms of the input feature vectors. The probability density of all feature vectors can help to optimize the learning rates of RBFNN by applying GMM. Another possibility is to utilize the Evolutionary Algorithms (EAs) to find the optimum solution. However, EAs often behave randomly which can’t be mathematically controlled. So, a combined RBFNN based on novel PE algorithm has been proposed which has a soft behavior through the learning of non-linear function. The PE algorithm defines the occurrence probability of local minima in the space of extracted features as a Gaussian distribution correspondence to each chromosome. Then, it estimates the entire probabilities of local minima in an iterative procedure. These techniques have been utilized in the application of robust satellites subset selection. Geometric Dilution of Precision (GDOP) is the main factor to estimate the strength of goodness of each satellites subset. Then, the subset with the lowest value has been selected for improving the positioning performance, but it is so non-linear and has computational burden to navigation systems. These techniques have been implemented and the results on measured GPS data demonstrate that it significantly track the non-linearity of GPS GDOP comparison with the other conventional approaches.