Aerodynamic parameter estimation involves modelling of force and moment coefficients and computation of stability and control derivatives from recorded flight data. This problem is extensively studied in the past using classical approaches such as output error, filter error and equation error methods. An alternative approach to these model based methods is the machine learning such as artificial neural network. In this paper, radial basis function neural network (RBF NN) is used to model the lateral-directional force and moment coefficients. The RBF NN is trained using k-means clustering algorithm for finding the centers of radial basis function and extended Kalman filter for obtaining the weights in the output layer. Then, a new method is proposed to obtain the stability and control derivatives. The first order partial differentiation is performed analytically on the radial basis function neural network approximated output. The stability and control derivatives are computed at each training data point, thus reducing the post training time and computational efforts compared to hitherto delta method and its variants. The efficacy of the identified model and proposed neural derivative method is demonstrated using real time flight data of ATTAS aircraft. The results from the proposed approach compare well with those from the other.

In this paper, we propose a robust nonlinear position and attitude control method for quadrotor using higher order sliding mode control concept. The control of quadrotor is realized in an inner- and outer-loop structure. Both inner- (attitude control) and outer (position control)-loop controllers are synthesized using third-order sliding mode control. The attitude control is designed in a quaternion framework to avoid gimbal lock and for better computational efficiency. A low-pass filter is used to reduce the effect of chattering in higher order sliding mode. A disturbance observer is designed for disturbance estimation. The robustness of proposed control method is ensured by providing the disturbance compensation term in the control law. Lyapunov stability analysis is provided for both inner- and outer-loop controls. Numerical results show that the proposed method provides effective control solution under continuous disturbance/uncertainties due to unmodeled dynamics, parameter variations, and external disturbance with high position accuracy.

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