Alexandra Moutinho

Bio: Alexandra Moutinho is an academic researcher from Instituto Superior Técnico. The author has contributed to research in topics: Backstepping & Nonlinear control. The author has an hindex of 12, co-authored 43 publications receiving 640 citations. Previous affiliations of Alexandra Moutinho include Technical University of Lisbon & University of Lisbon.

Hover Control of an UAV With Backstepping Design Including Input Saturations

Abstract: This brief presents a backstepping-based controller with input saturations, applicable for the hover flight of an unmanned aerial vehicle (UAV). A dynamic model for a generic UAV is introduced that is valid for quasi-stationary conditions, with quaternion formulation of the kinematics equations. Based on this model, a backstepping design formulation is deduced for UAV hover control, and its global asymptotic stability is demonstrated. In order to cope with limitations due to reduced actuation, saturations are introduced in the control design, and the stability of the modified control solution is verified. Simulation results are presented for the hover stabilization of an airship UAV, which are demonstrative of the excellent performance of the proposed controller and illustrate its robustness in face of wind disturbances.

A backstepping controller for path‐tracking of an underactuated autonomous airship

Abstract: In this paper we propose a nonlinear control approach for the path-tracking of an autonomous underactuated airship. A backstepping controller is designed from the airship nonlinear dynamic model including wind disturbances, and further enhanced to consider actuators saturation. Control implementation issues related to airship underactuation are also addressed, namely control allocation and an attitude reference shaping to obtain a faster error correction with smoother input requests. The results obtained demonstrate the capacity of an underactuated unmanned airship to execute a realistic mission including vertical take-off and landing, stabilization and path-tracking, in the presence of wind disturbances, with a single robust control law. Copyright © 2008 John Wiley & Sons, Ltd.

Stability and Robustness Analysis of the AURORA Airship Control System using Dynamic Inversion

Abstract: This paper presents a stability and robustness analysis of a nonlinear control system for the autonomous airship of the AURORA project. A Dynamic Inversion controller is implemented with desired dynamics given by a linear optimal compensator. The stability analysis of the nonlinear system is done applying Lyapunov’s stability theory. Robustness tests are performed in order to verify the nonlinear controller performance in face of disturbances and model parameters errors. The results obtained illustrate the overall system robustness, and point at the most sensitive model parameters of the AURORA airship, for which a more careful identification/determination should be carried.

Airship Hover Stabilization Using a Backstepping Control Approach

Abstract: The present paper introduces a novel approach for the airship hover stabilization problem. A synthetic modeling of the airship dynamics is introduced using a quaternion formulation of the kinematics equations. Based on this model, a backstepping design formulation is deduced for the aircraft hovering control. To deal with limitations caused by reduced actuation, saturations are introduced in the control design, and the global asymptotic stability of the system under saturation is demonstrated. The control objective is finally modified to cope with the strong lateral underactuation. Simulation results are presented for the hover stabilization of an unmanned robotic airship, with wind and turbulence conditions selected to demonstrate the behavior and robustness of the proposed solution.

Project AURORA: Infrastructure and flight control experiments for a robotic airship

Abstract: Project AURORA aims at the development of unmanned robotic airships capable of autonomous flight over user-defined locations for aerial inspection and environmental monitoring missions. In this article, the authors report a successful control and navigation scheme for a robotic airship flight path following. First, the AURORA airship, software environment, onboard system, and ground station infrastructures are described. Then, two main approaches for the automatic control and navigation system of the airship are presented. The first one shows the design of dedicated controllers based on the linearized dynamics of the vehicle. Following this methodology, experimental results for the airship flight path following through a set of predefined points in latitude/longitude, along with automatic altitude control are presented. A second approach considers the design of a single global nonlinear control scheme, covering all of the aerodynamic operational range in a sole formulation. Nonlinear control solutions under investigation for the AURORA airship are briefly described, along with some preliminary simulation results. © 2006 Wiley Periodicals, Inc.

Density-based clustering based on hierarchical density estimates

Abstract: We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.

Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation

Abstract: In this paper, a dynamic surface control (DSC) scheme is proposed for a class of uncertain strict-feedback nonlinear systems in the presence of input saturation and unknown external disturbance. The radial basis function neural network (RBFNN) is employed to approximate the unknown system function. To efficiently tackle the unknown external disturbance, a nonlinear disturbance observer (NDO) is developed. The developed NDO can relax the known boundary requirement of the unknown disturbance and can guarantee the disturbance estimation error converge to a bounded compact set. Using NDO and RBFNN, the DSC scheme is developed for uncertain nonlinear systems based on a backstepping method. Using a DSC technique, the problem of explosion of complexity inherent in the conventional backstepping method is avoided, which is specially important for designs using neural network approximations. Under the proposed DSC scheme, the ultimately bounded convergence of all closed-loop signals is guaranteed via Lyapunov analysis. Simulation results are given to show the effectiveness of the proposed DSC design using NDO and RBFNN.

Direct Adaptive Neural Control for a Class of Uncertain Nonaffine Nonlinear Systems Based on Disturbance Observer

Abstract: In this paper, the direct adaptive neural control is proposed for a class of uncertain nonaffine nonlinear systems with unknown nonsymmetric input saturation. Based on the implicit function theorem and mean value theorem, both state feedback and output feedback direct adaptive controls are developed using neural networks (NNs) and a disturbance observer. A compounded disturbance is defined to take into account of the effect of the unknown external disturbance, the unknown nonsymmetric input saturation, and the approximation error of NN. Then, a disturbance observer is developed to estimate the unknown compounded disturbance, and it is established that the estimate error converges to a compact set if appropriate observer design parameters are chosen. Both state feedback and output feedback direct adaptive controls can guarantee semiglobal uniform boundedness of the closed-loop system signals as rigorously proved by Lyapunov analysis. Numerical simulation results are presented to illustrate the effectiveness of the proposed direct adaptive neural control techniques.