Laboratoire franco-mexicain d'informatique et d'automatique

In this paper, we present a controller design and its implementation on a mini rotorcraft having four rotors. The dynamic model of the four-rotor rotorcraft is obtained via a Lagrange approach. The proposed controller is based on Lyapunov analysis using a nested saturation algorithm. The global stability analysis of the closed-loop system is presented. Real-time experiments show that the controller is able to perform autonomously the tasks of taking off, hovering, and landing.

Users and Internet service providers (ISPs) are constantly affected by denial-of-service (DoS) attacks. This cyber threat continues to grow even with the development of new protection technologies. Developing mechanisms to detect this threat is a current challenge in network security. This article presents a machine learning- (ML-) based DoS detection system. The proposed approach makes inferences based on signatures previously extracted from samples of network traffic. The experiments were performed using four modern benchmark datasets. The results show an online detection rate (DR) of attacks above 96%, with high precision (PREC) and low false alarm rate (FAR) using a sampling rate (SR) of 20% of network traffic.

In this technical note, a universal formula is proposed for event-based stabilization of general nonlinear systems affine in the control. The feedback is derived from the original one proposed by E. Sontag in the case of continuous time stabilization. Under the assumption of the existence of a smooth Control Lyapunov Function, it is proved that an event-based static feedback, smooth everywhere except at the origin, can be designed so to ensure the global asymptotic stability of the origin. Moreover, the inter-sampling time can be proved not to contract at the origin. More precisely, it is proved that for any initial condition within any given closed set the minimal inter-sampling time is proved to be below bounded avoiding the infinitely fast sampling phenomena. Moreover, under homogeneity assumptions the control can be proved to be smooth anywhere and the inter-sampling time bounded below for any initial condition. In that case, we retrieve a control approach previously published for continuous time stabilization of homogeneous systems.

In the present work a mathematical model and a control methodology for a special class of underactuated mechanical system, composed by an Unmanned Aerial Vehicle (UAV) kind quadrotor transporting a cable-suspended payload, is proposed. The Euler-Lagrange formulation is used to derive the dynamic model of the system, where the integrated dynamics of the quadrotor, cable and payload are considered. An Interconnection and Damping Assignment-Passivity Based Control (IDA-PBC) for a quadrotor UAV transporting a cable-suspended payload is developed. The control law designed does not depend on the swing angle of the cable. The control objective is to transport the payload from point to point, with swing suppression along trajectory. Experimental results are shown to evaluate the proposed control law.

The identification problem incorporated in feedback control of uncertain nonlinear systems exhibiting complex behavior has been solved in different ways. Some of these solutions have used artificial intelligence methods like fuzzy logic and neural networks. However, their individual implementation suffers from certain drawbacks, such as the black-box nature of neural network and the problem of finding suitable membership functions for fuzzy systems. These weaknesses can be avoided by implementing a hybrid structure combining these two approaches, the so-called neuro-fuzzy system. In this paper, a neuro-fuzzy system that implements differential neural networks (DNNs) as consequences of Takagi-Sugeno (T-S) fuzzy inference rules is proposed. The DNNs substitute the local linear systems that are used in the common T-S method. In this paper, DNNs are used to provide an effective instrument for dealing with the identification of the uncertain nonlinear system, while the T-S rules are used to provide the framework of previous knowledge of the system. The main idea is to carry out an online identification process of an uncertain nonlinear system with the aim to design a closed-loop trajectory tracking controller. The methodology developed in this study that supports the identification and trajectory control designs is based on the Lyapunov formalism. The DNN implementation results in a time-varying T-S system. As a consequence, the solution of two time-varying Riccati equations was used to adjust the learning laws in the DNN as well as to adjust the gains of the controller. Two results were provided to justify the existence of positive-definite solutions for the class of Riccati equations used in the learning laws of DNNs. A complete description of the learning laws used for the set of DNN identifiers is also obtained. An autonomous underwater vehicle system is used to demonstrate the performance of the controller on tracking a desired 3-D path by this combination of the DNN and the T-S system.

A path generator is proposed for a fixed-wing Unmaned Aereal Vehicle (UAV). Assuming that the vehicle maintain a constant altitude, and airspeed, and that the UAV is constrained by a turning rate. The Dubins paths serve as a strategy to find the shortest path for the non-holonomic model of the UAV. Dubins paths consist of three path segments which are based on straight lines or arcs of circle of a given radius. The Dubins path generation is combined with a nonlinear Lypaunov-based path-following control. Finally we present a complete simulation environment in which the path generator and path following strategy are validated. As an example of application we propose the scenario in which a missing person is located in some known area and we use the path generator along with this path-following strategy applied to the fixed wing UAV to search and find this person.

In this paper we present a controller design and implementation on a mini-rotorcraft having four rotors. A Lagrangian model of the helicopter was used for the controller synthesis. The proposed controller is based on Lyapunov analysis. Experimental results show that the controller is able to perform autonomously the tasks of taking-off, hovering and landing.

Abstract In this paper, we present a heuristic inspired on the T‐Cell model of the immune system (i.e. an artificial immune system). The proposed approach (called T‐Cell) is used for solving constrained (numerical) optimization problems, and is validated using several test functions taken from the specialized literature on evolutionary optimization. Additionally, several engineering optimization problems are also used for assessing the performance of the proposed approach. The results are compared with respect to approaches representative of the state‐of‐the‐art in constrained evolutionary optimization. Copyright © 2010 John Wiley & Sons, Ltd.

This letter studies the problem of autonomous navigation for unmanned underwater vehicles, using computer vision for localization. Parallel tracking and mapping is employed to localize the vehicle with respect to a visual map, using a single camera, whereas an extended Kalman filter (EKF) is used to fuse the visual information with data from an inertial measurement unit, in order to recover the scale of the map and improve the pose estimation. A proportional integral derivative controller controller with compensation of the restoring forces is proposed to accomplish trajectory tracking, where a pressure sensor and a magnetometer provide feedback for depth control and yaw, respectively, while the remaining states are provided by the EKF. Real-time experiments are presented to validate the navigation strategy, using a commercial remotely operated vehicle (ROV), the BlueROV2, which was adapted to perform as an autonomous underwater vehicle with the help of the robot operative system.

This article presents a comparison of three control techniques: nested saturations, backstepping, and sliding modes. The control objective consists of obtaining the best control strategy to stabilize the position of a quad-rotor unmanned aerial vehicle (UAV) when using visual feedback. We propose a vision-based method to measure translational speed as well as the UAV 3D position in a local frame. The three selected controllers were implemented and tested in real-time experiments. The obtained results demonstrate the performance of such methodologies applied to the quad-rotor system.

This paper presents the design and practical implementation of a quaternion control scheme to globally stabilize a quadrotor aerial vehicle. First an attitude control law is proposed to stabilize the vehicle's heading, then a position control law is proposed to stabilize the vehicle in all its states. Using the position references, a smooth trajectory is calculated for the attitude controller to follow so that the position of the vehicle becomes stable. The proposed control law is such that the quadrotor system can be analyzed and controlled as a linear system. This model and control law are then numerically simulated to corroborate the closed-loop system's stability. At last, experimental flight tests were performed to validate practical results.

This paper presents a model of a particular class of a convertible MAV with fixed wings. This vehicle can operate as a helicopter as well as a conventional airplane, i.e. the aircraft is able to switch their flight configuration from hover to level flight and vice versa by means of a transition maneuver. The paper focuses on finding a controller capable of performing such transition via the tilting of their four rotors. The altitude should remain on a predefined value throughout the transition stage. For this purpose a nonlinear control strategy based on saturations and Lyapunov design is given. The use of this control law enables to make the transition maneuver while maintaining the aircraft in flight. Numerical results are presented, showing the effectiveness of the proposed methodology to deal with the transition stage.

In this article, two types of actuators are applied for a lower limb exoskeleton. They are DC motors with the harmonic drive and the pneumatic artificial muscles. This combination takes advantages of both the harmonic drive and the pneumatic artificial muscle. It provides both high accuracy position control and high ratio of strength and weight. The shortcomings of the two actuators are overcome by the hybrid actuation, for example, low control accuracy and modeling difficult of pneumatic artificial muscle, compactness, and structural flexibility of DC motors. The design and modeling processes are discussed to show the proposed exoskeleton can increase the strength of human lower limbs. Experiments and analysis of the exoskeleton are given to evaluate the effectiveness of the design and modeling.

Summary We propose a control strategy based on distributed adaptive leader‐follower consensus algorithms for multi‐agent systems (MAS) affected by switching network events. The strategy allows each agent in the MAS to compute its own control input based on local information and information coming from its neighbors. In this sense, MAS distributed control laws are obtained where the coupling gain of the associated communication graph is adapted dynamically in real‐time. The consensus algorithm is extended with a switching network topology approach, which ensures appropriate performance even when the MAS network topology is prone to arbitrary switching. A real‐time experimental application is presented, where a MAS consisting of four rotorcraft UAS successfully performed the tasks of autonomously approaching and escorting a leader, even in the situation when the network topology was arbitrarily changing. Additionally, a Lyapunov stability analysis is included, which demonstrates that the tracking errors between leader and follower agents converge asymptotically to zero. Copyright © 2016 John Wiley & Sons, Ltd.

This paper addresses the tracking control of quadrotors flying outdoors. Two control laws are combined and tested in real-time experiments. The aircraft attitude and the translational displacement are controlled using the backstepping approach, while the altitude is controlled using the sliding mode control strategy. In both cases, new modifications are introduced with respect to the existing classical algorithms. Concerning the backstepping algorithm, we introduce the dynamical model of the quadrotor in the controller design and this guarantees that the virtual input is bounded. On the other hand, the proposed sliding mode control assures that the vehicle's altitude converges in finite time to the desired reference, even when uncertainties are considered in the system. The proposed controller is tested in an outdoor environment and the experiments highlighted the controllers' reliability. Additionally, the performance of the closed-loop plant with the proposed controllers is compared with the performance given by a proportional-derivative controller.

In this paper, we stabilize the planar vertical takeoff and landing (PVTOL) aircraft using a camera. The camera is used for measuring the position and orientation of the PVTOL moving on a inclined plane. We have used a simple control strategy to stabilize the system in order to simplify the real experiments. The proposed control law ensures convergence of the state to the origin.

This research presents a way to improve the autonomous maneuvering capability of a four-degrees-of-freedom (4DOF) autonomous underwater vehicle (AUV) to perform trajectory tracking tasks in a disturbed underwater environment. This study considers four second-order input-affine nonlinear equations for the translational (x,y,z) and rotational (heading) dynamics of a real AUV subject to hydrodynamic parameter uncertainties (added mass and damping coefficients), unknown damping dynamics, and external disturbances. We proposed an identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain. Several numerical simulations validate the satisfactory performance of the proposed DNCS tracking reference trajectories in comparison with a conventional feedback controller with no adaptive compensation. Some graphics showing dynamic approximation of the lumped disturbance as well as estimation of the parametric uncertainty are depicted, validating effective operation of the proposed DNCS when the system is almost completely unknown.

In this paper, we assess the convenience of applying a previously proposed interactive method to solve three aero dynamic airfoil shape optimization problems with 2, 3, and 6 objectives, respectively. The expensive simulations required to evaluate the objective functions makes these problems an excellent example in which the use of interactive methods is very advantageous. First, the search can be focused on the decision maker's region of interest, saving this way, valuable function evaluations. Second, the preference relation used in the interactive method helps to deal with a large number of objectives since it is able to rank incomparable nondominated solutions. The experimental evaluation reveals that in the three problems studied, the interactive method achieved a better final solution than a traditional a posteriori method with no preferences. Nevertheless, in the problem with 6 objectives, only 3 of them were improved. A possible explanation for this is that local optima become harder to overcome when the size of the region of interest is very small. Additional experiments confirmed that the convergence is deteriorated if very small regions of interest are used.

This article addresses the problem of altitude tracking for unmanned aircraft system when the altitude velocity is unknown, because in a practical implementation, the available sensors used to measure the vehicle’s altitude (barometer, global positioning system, laser, etc.) do not provide the altitude velocity. We propose a control strategy based on both the super-twisting sliding mode controller as well as a high-order sliding mode observer to control and to estimate the altitude velocity, respectively. A comprehensive stability analysis for the combined controller–observer based on the Lyapunov stability theory is presented, ensuring the asymptotic convergence of the tracking error under external bounded disturbances. To demonstrate the performance and the effectiveness of the proposed solution, an extensive set of real-time experimental tests performed at outdoor environments is presented.

Conception and development of an Unmanned Aerial Vehicle (UAV) capable of detecting, tracking and following a moving object with unknown dynamics is presented in this work, considering a human face as a case of study. Object detection is accomplished by a Haar cascade classifier. Once an object is detected, it is tracked with the help of a Kalman Filter (KF), and an estimation of the relative position with respect to the target is obtained. A linear controller is used to validate the proposed vision scheme and for regulating the aerial robot's position in order to keep a constant distance with respect to the mobile target, employing as well the extra available information from the embedded sensors. The proposed system was extensively tested in real-time experiments, through different conditions, using a commercial quadcopter connected via wireless to a ground station running under the Robot Operative System (ROS). The proposed overall strategy shows a good performance even under disadvantageous conditions as outdoor flight, being robust against illumination changes, image noise and the presence of other people in the scene.