No abstract

In this paper, a novel adaptive controller for quadrotor position and orientation trajectory tracking is introduced. By taking into account the coupling between the position and the orientation dynamics, an adaptive scheme based on an accurate parameterization of the model-based feedforward compensation is presented. The adaptation update laws for the adaptation parameters are designed on Lyapunov's theory so that the stability of the state space origin of the error dynamics is guaranteed. Barbalat's lemma ensures convergence of the tracking errors and bounding of the adaptation parameters. The extensive real-time experimental results show the practical viability of the proposed scheme. More specifically, the performance of the proposed controller is compared with an adaptive controller taken from the literature and the nonadaptive version of the proposed controller. Better results are obtained with the novel adaptive approach.

In this document, the parameter identification of a quadrotor is discussed. More precisely, the aim of this paper is to present results on the application of known methods for estimating the dynamic parameters that capture better the behavior of a quadrotor in comparison with the nominal parameters given by the manufacturer. To take into account the limitations of position, velocity, and acceleration of the quadrotor, an optimized trajectory to excite the quadrotor dynamics adequately is obtained. A proportionalintegral-derivative (PID) control scheme is used to implement experimentally the tracking of the optimized trajectory. The obtained data is processed off-line to construct the standard and filtered regression models from which the parameter identification is achieved. Specifically, the least-squares and gradient descent algorithms are applied to the regression models giving four sets of estimated parameters. The four sets of parameters obtained in this work are compared with the parameters provided by the manufacturer by computing the error between simulations and experiments. In addition, the output prediction errors of the regression models are computed, thus providing another validation form. All the comparisons show that the estimated parameters are more precise than the nominal ones. The given results support the functionality of the described methodology.INDEX TERMS Optimized trajectory, parameter identification, quadrotor, real-time experiments, regression model.

This paper presents a vision-based navigation system for an autonomous underwater vehicle in semistructured environments with poor visibility. In terrestrial and aerial applications, the use of visual systems mounted in robotic platforms as a control sensor feedback is commonplace. However, robotic vision-based tasks for underwater applications are still not widely considered as the images captured in this type of environments tend to be blurred and/or color depleted. To tackle this problem, we have adapted thelαβcolor space to identify features of interest in underwater images even in extreme visibility conditions. To guarantee the stability of the vehicle at all times, a model-free robust control is used. We have validated the performance of our visual navigation system in real environments showing the feasibility of our approach.