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.

In this paper, two sets of multisine signals are designed for system identification purposes. The first one is obtained without any information about system dynamics. In the second case, the a priori information is given in terms of dimensional stability and control derivatives. Magnitude Bode plots are obtained to design the multisine power spectrum that is optimized afterwards. A genetic algorithm with linear ranking, uniform crossover and mutation operator has been employed for that purpose. Both designed manoeuvres are used to excite the aircraft model, and then system identification is performed. The estimated parameters are obtained by applying two methods: Equation Error and Output Error. The comparison of both investigated cases in terms of accuracy and manoeuvre time is presented afterwards.

The aim of the project was to determine if including a-priori information in the system identification multisine maneuver design process will improve the quality of the estimated data in the lateral motion. During this study we used a linear aircraft model which was containing a-priori information in terms of aerodynamic derivatives. A-priori knowledge allowed us to obtain a non-uniform power spectrum of the signals that excited the ailerons and rudder. Three cases were analyzed for comparison. The first design was obtained when no a-priori information was available. The second case was designed on the basis of the a-priori model state matrix. We found dominant frequencies of the system and assigned to them more power. In the third case the non-uniform power spectrum was optimized. A genetic algorithm was used for that purpose. The efficiency of the design was optimized thought out Relative Peak Factor. The a-priori information was introduced by using Marchand methodology. All the obtained maneuvers were used to excite the aircraft model. The response of the system was registered. Recorded data was used later to perform a parameter estimation for each case. In the end we compared the results from the identification process in terms of the accuracy.

Aircraft aerodynamic forces and moments can be expressed as a function of the dynamic pressure, aircraft dimensions and flight conditions. They are very important do predict the aircraft behavior and performance. Moreover, they play an important role predicting the model response to control inputs. For these reasons it is important to accurate determine the aerodynamic characteristics in terms of stability and control derivatives. This study presents a methodology to precisely estimate stability and control derivatives through system identification procedure. Navion FAR 23 airplane model was used for this purpose. A bio-inspired optimization method was used to create optimal input control signals to excite the model and obtain an output signal with good frequency content that allowed to properly identify the system. Inclusion of bio-inspired methods increased the accuracy of the estimates. The method can be used to identify fixed wing platforms of similar characteristics. Results can be used to develop flight simulators to collect system information regarding certification evidences and to train pilots.