Three different horizontal axis wind turbine (HAWT) blade geometries with the same diameter of 0.72 m using the same NACA4418 airfoil profile have been investigated both experimentally and numerically. The first is an optimum (OPT) blade shape, obtained using improved blade element momentum (BEM) theory. A detailed description of the blade geometry is also given. The second is an untapered and optimum twist (UOT) blade with the same twist distributions as the OPT blade. The third blade is untapered and untwisted (UUT). Wind tunnel experiments were used to measure the power coefficients of these blades, and the results indicate that both the OPT and UOT blades perform with the same maximum power coefficient, C_p = 0.428, but it is located at different tip speed ratio, λ = 4.92 for the OPT blade and λ = 4.32 for the UOT blade. The UUT blade has a maximum power coefficient of C_p = 0.210 at λ = 3.86. After the tests, numerical simulations were performed using a full three-dimensional computational fluid dynamics (CFD) method using the k-ω SST turbulence model. It has been found that CFD predictions reproduce the most accurate model power coefficients. The good agreement between the measured and computed power coefficients of the three models strongly suggest that accurate predictions of HAWT blade performance at full-scale conditions are also possible using the CFD method.

A development procedure for a low-cost attitude and heading reference system (AHRS) with a self-developed three-axis rotating platform has been proposed. The AHRS consists of one 3-axis accelerometer, three single-axis gyroscopes, and one 3-axis digital compass. Both the accelerometer and gyroscope triads are based on micro electro-mechanical system (MEMS) technology, and the digital compass is based on anisotropic-magnetoresistive (AMR) technology. The calibrations for each sensor triad are readily accomplished by using the scalar calibration and the least squares methods. The platform is suitable for the calibration and validation of the low-cost AHRS and it is affordable for most laboratories. With the calibrated parameters and data fusion algorithm for the orientation estimation, the self-developed AHRS demonstrates the capabilities of compensating for the sensor errors and outputting the estimated orientation in real-time. The validation results show that the estimated orientations of the developed AHRS are within the acceptable region. This verifies the practicability of the proposed development procedure.

This paper presents a complete procedure for sensor compatibility correction of a fixed-wing Unmanned Air Vehicle (UAV). The sensors consist of a differential air pressure transducer for airspeed measurement, two airdata vanes installed on an airdata probe for angle of attack (AoA) and angle of sideslip (AoS) measurement, and an Attitude and Heading Reference System (AHRS) that provides attitude angles, angular rates, and acceleration. The procedure is mainly based on a two pass algorithm called the Rauch-Tung-Striebel (RTS) smoother, which consists of a forward pass Extended Kalman Filter (EKF) and a backward recursion smoother. On top of that, this paper proposes the implementation of the Wiener Type Filter prior to the RTS in order to avoid the complicated process noise covariance matrix estimation. Furthermore, an easy to implement airdata measurement noise variance estimation method is introduced. The method estimates the airdata and subsequently the noise variances using the ground speed and ascent rate provided by the Global Positioning System (GPS). It incorporates the idea of data regionality by assuming that some sort of statistical relation exists between nearby data points. Root mean square deviation (RMSD) is being employed to justify the sensor compatibility. The result shows that the presented procedure is easy to implement and it improves the UAV sensor data compatibility significantly.

The linear-quadratic-Gaussian (LQG) control synthesis has the advantage of dealing with the uncertain linear systems disturbed by additive white Gaussian noise while having incomplete system state information available for control-loop feedback. This paper hence explores the feasibility of designing and implementing a stability augmentation autopilot for fixed-wing unmanned air vehicles using the LQG approach. The autopilot is composed of two independently designed LQG controllers which control the longitudinal and lateral motions of the aircraft respectively. The corresponding linear models are obtained through a system identification routine which makes use of the combination of two well-established identification methods, namely the subspace method and prediction error method. The two identification methods complement each other well and this paper shows that the proposed system identification scheme is capable of attaining satisfactory state-space models. A complete autopilot design procedure is devised and it is shown that the design process is simple and effective. Resulting longitudinal and lateral controllers are successfully verified in computer simulations and actual flight tests. The flight test results are presented in the paper and they are found to be consistent with the simulation results.