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International audience; Two novel nonlinear control schemes for Unmanned Aerial Vehicles (UAV) carrying a load and their comparative results are presented in this paper. The goal is to carry the load to a desired position, with oscillation attenuation along the trajectory. The proposed control structures are hierarchical schemes consisting of nonlinear controllers to stabilize the vehicle translational movements and the payload swing together with a well-known state-dependent differential Riccati equation controller to stabilize the rotational dynamics. We present new methodologies where two nonlinear controllers are proposed to obtain precise aerial vehicle positioning and efficient load oscillation reduction by exploiting the natural coupling between the horizontal quadcopter movement and the payload oscillation. It is shown that asymptotic stability can be guaranteed by the use of the Lyapunov approach and La Salle’s invariance principle. Numerical experiments were carried out to validate the nonlinear control behaviors where the results show improvements with respect to a strategy from the literature.

In this brief, the implementation of a suboptimal nonlinear discrete control to optimize the energy consumption in a hybrid exoskeleton for the elbow joint is presented. The exoskeleton is used to amplify the strength of the user and is hybrid in the sense that it combines two types of actuators: pneumatic muscles and Harmonic Drive motors, which give power and precision to the system, respectively. The exoskeleton is autonomous in the energetic sense, and it is driven by compressed air and batteries. The suboptimal control is used to increase the operation time of the exoskeleton. This control law penalizes the energy consumption, and it has a direct effect on the operation time of the prototype.

Computational models of emotional learning observed in the mammalian brain have inspired diverse self-learning control approaches. These architectures are promising in terms of their fast learning ability and low computational cost. In this paper, the objective is to establish performance–guaranteed emotional learning–inspired control (ELIC) strategies for autonomous multi–agent systems (MAS), where each agent incorporates an ELIC structure to support the consensus controller. The objective of each ELIC structure is to identify and compensate model differences between the theoretical assumptions taken into account when tuning the consensus protocol, and the real conditions encountered in the real system to be stabilized. Stability of the closed-loop MAS is demonstrated using a Lyapunov analysis. Simulation results based on the consensus task of a group of inverted pendulums demonstrate the effectiveness of the proposed ELIC for stabilization of nonlinear MAS.

Building inspection is a vital task because infrastructure damage puts people at risk or causes economic losses. Thanks to the technological breakthroughs in regard to Unmanned Aerial Vehicles (UAVs) and intelligent systems, there is a real possibility to implement an inspection by means of these technologies. UAVs allow reaching difficult places and, depending on the hardware carried onboard, take data or compute algorithms to understand the environment. This paper proposes a real-time robust altitude control strategy for a quadrotor aircraft, also a convolutional neuronal network for crack recognition is developed. The main idea of this proposal is to lay the background for an autonomous system for the inspection of structures using a UAV. For the robust control, a combination of two control actions, one linear (PD) and another nonlinear (Sliding Mode) is used. The combination of these control actions allows increasing the system’s performance. To verify the satisfactory performance of proposed control law, simulations and experimental results with a quadrotor, in the presence of disturbances, are presented. For crack recognition in images, several experiments were carried out validating the proposed model. For CNN training, a database of cracks was built from images taken from the Internet.

The problem of controlling Unmanned Aerial Vehicles (UAV) can be simplified in some cases by designing a controller for the Vertical Take-off and Landing (PVTOL) platform and adapting it to other v...

International audience; This paper presents a hybrid adaptive approximation-based control (HAAC) strategy for a class of uncertain robotic joints' system. The proposed control structure consists of a robust sliding mode controller and an adaptive approximation-based controller. The robust sliding mode controller is designed by using the super-twisting algorithm, which is a particularly effective method to decrease the chattering caused by the traditional sliding mode control (SMC) and compensate the disturbances. Another improvement of the robust sliding mode controller is that the robust control parameters only subject to the upper bound of the derivative of the external disturbances, rather than choosing a relatively large value. Moreover, the designed adaptive approximation-based controller has the following two distinctive features: 1) the control parameters are designed to be adjusted in real time and 2) the prior knowledge of actual robotic model is not required to be known. These features contribute to compensating the uncertainties. The stability of the closed-loop system is proved by using the Lyapunov theory, and the simulation results demonstrate the effectiveness of the proposed control method. Finally, the proposed HAAC could apply in the experiments of industrial robotic joints' system.

International audience; This paper presents a control strategy named auxiliary surfaces sliding mode control (AS-SMC) by using Positive Invariant Set (PIS) to control a class of continuous nonlinear systems with state constraints. The PIS can be regarded as a special kind of sliding surface, and its invariance ensures that the system state can satisfy the constraints in the convergence process. The stability analysis and a PIS theory proof are given. The control strategy is successfully tested in numerical simulations and even effectively applied to the coaxial unmanned helicopter flight control system on hardware in the loop (HIL) platform. The results verify the effectiveness of the proposed control strategy for the experimental set-up.

We present an automatic agave detection method for counting plants based on aerial data from a UAV (Unmanned Aerial Vehicle). Our objective is to autonomously count the number of agave plants in an area to aid management of the yield. An orthomosaic is obtained from agave plantations, which is then used to create a database. This database is in turn used to train a Convolutional Neural Network (CNN). The proposed method is based on computer image processing, and the CNN increases the detection performance of the approach. The main contribution of the present paper is to propose a method for agave plant detection with a high level of precision. In order to test the proposed method in a real agave plantation, we develop a UAV platform, which is equipped with several sensors to reach accurate counting. Therefore, our prototype can safely track a desired path to detect and count agave plants. For comparison purposes, we perform the same application using a simpler algorithm. The result shows that our proposed algorithm has better performance reaching an F1 score of 0.96 as opposed to 0.57 for the Haar algorithm. The obtained experimental results suggest that the proposed algorithm is robust and has considerable potential to help farmers manage agave agroecosystems.

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SUMMARYA novel underwater vehicle configuration with an operating principle as the Sepiida animal is presented and developed in this paper. The mathematical equations describing the movements of the vehicle are obtained using the Newton–Euler approach. An analysis of the dynamic model is done for control purposes. A prototype and its embedded system are developed for validating analytically and experimentally the proposed mathematical representation. A real-time characterization of one mass is done to relate the pitch angle with the radio of displacement of the mass. In addition, first validation of the closed-loop system is done using a linear controller.

Disturbance rejection is a major concern nowadays for the development of autonomous aerial vehicles technology. In this way, in the control community, the researchers propose complex and robust nonlinear controllers that are in several cases so difficult to be applied in real time. In this paper, a robust linear control scheme for nonlinear aerial system is proposed. The scheme is based on the structural properties of the system such as parametric uncertainties and external disturbances. The control scheme is validated experimentally in real time to corroborate their properties on disturbance rejection. Experimental results illustrate the easy implementation and feasibility of the proposed scheme when the vehicle faces three different scenarios; wind-gust disturbance, variable mass and motor failure. In addition, the scheme presents high efficiency in disturbance rejection, smoother convergence to the desired reference and a low computational demand.

A hybrid unmanned autonomous vehicle (UAV) combines the high-speed cruise capability of a fixed-wing airplane, with the hovering flight, vertical take-off and landing capabilities of a helicopter. The change between both flight modes refers to as a transition flight process. This paper presents the flight dynamics during the transition stage of a hybrid UAV of Dual Tilt-Wing type. A non-linear mathematical model is developed that includes the UAV complete dynamics, which presents significant changes in aerodynamic characteristics, especially during the transition flight. These changes are analyzed and characterized by flight tests and CFD simulations. The adaptation to the variation of aerodynamic characteristics caused by tilt angle changes of transition system is verified, so a PD controller for altitude is implemented.

International audience; DC motor speed synchronization is a critical problem in industrial and robotic applications. To tackle this problem, we propose to use a multi-agent consensus-based control scheme that guarantees the convergence of the DC motor speeds to either fixed or time-varying reference. A detailed robustness analysis considering parametric uncertainty and time delay in the multi-agent system is performed to guarantee the consensus on the speed of DC motors in actual practice. The results obtained concerning the robustness analysis allowed us to implement experimental tests on a three-motor system using a wireless communication system to achieve satisfactory performance

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This research work focuses on the design of a robust-adaptive control algorithm for a 4DOF Unmanned Underwater Vehicle (UUV). The proposed strategy is based in a Non-Singular Terminal Sliding Mode Control (NTSMC) with adaptive gains, where the proposed adaptation mechanism ensures that the gains remain bounded. In this control strategy a non-singular terminal sliding surface is proposed to obtain a faster convergence of the tracking errors. The NTSMC ensures Practical Finite-Time Stability for the closed-loop system as well as exhibits a chattering reduction. In order to demonstrate the satisfactory performance of the proposed controller, a set of experiments was performed with a Non-Singular Terminal Sliding Mode Controller and an Adaptive Non-Singular Terminal Sliding Mode Control (ANTSMC) in real time for trajectory tracking in the X-Y plane, the graphs showed that the ANTSMC converges faster to a smaller region and reduces oscillations.

Control theory applied to multirotor aerial systems (MAS) has gained attention with the recent increase on the power computation for embedded systems. These systems are now able to perform the calculations needed for a variety of control techniques, with lower cost of sensors and actuators. These types of control algorithms are applied to the position and the attitude of MAS. In this paper, a brief overview evaluation of popular control algorithms for multirotor aerial systems, especially for VTOL - Vertical Take-Off and Landing aircraft, is presented. The main objective is to provide a unified and accessible analysis, placing the classical model of the VTOL vehicle and the studied control methods into a proper context. In addition, to provide the basis for beginner users working in aerial vehicles. In addition, this work contributes in presenting a comprehensive analysis of the implementation for the Nonlinear and Linear Backstepping, Nested Saturation and the Hyperbolic Bounded Controllers. These techniques are selected and compared to evaluate the performance of the aircraft, by simulations and experimental studies.

Hybrid Unmanned Aerial Vehicles (H-UAVs) are currently a very interesting field of research in the modern scientific community due to their ability to perform Vertical Take-Off and Landing (VTOL) and Conventional Take-Off and Landing (CTOL). This paper focuses on the Dual Tilt-wing UAV, a vehicle capable of performing both flight modes (VTOL and CTOL). The UAV complete dynamic model is obtained using the Newton&ndash, Euler formulation, which includes aerodynamic effects, as the drag and lift forces of the wings, which are a function of airstream generated by the rotors, the cruise speed, tilt-wing angle and angle of attack. The airstream velocity generated by the rotors is studied in a test bench. The projected area on the UAV wing that is affected by the airstream generated by the rotors is specified and 3D aerodynamic analysis is performed for this region. In addition, aerodynamic coefficients of the UAV in VTOL mode are calculated by using Computational Fluid Dynamics method (CFD) and are embedded into the nonlinear dynamic model. To validate the complete dynamic model, PD controllers are adopted for altitude and attitude control of the vehicle in VTOL mode, the controllers are simulated and implemented in the vehicle for indoor and outdoor flight experiments.

Inertial stabilised platforms are increasingly popular with a large range of products available mainstream. Most items are controlled using popular algorithms that sometimes do not offer best achie...

SUMMARYSafe and accurate navigation for autonomous trajectory tracking of quadrotors using monocular vision is addressed in this paper. A second order Sliding Mode (2-SM) control algorithm is used to track desired trajectories, providing robustness against model uncertainties and external perturbations. The time-scale separation of the translational and rotational dynamics allows to design position controllers by giving a desired reference in roll and pitch angles, which is suitable for practical validation in quad-rotors equipped with an internal attitude controller. A Lyapunov based analysis proved the closed-loop stability of the system despite the presence of unknown external perturbations. Monocular vision fused with inertial measurements are used to estimate the vehicle's pose with respect to unstructured scenes. In addition, the distance to potential collisions is detected and computed using the sparse depth map coming also from the vision algorithm. The proposed strategy is successfully tested in real-time experiments, using a low-cost commercial quadrotor.

Wind is considered a strong disturbance for quadrotor aircrafts (UAV) when an outdoor task at a fixed point is carried out. The effect of wind produces a distortion on the attitude of the vehicle which is reflected on undesired longitudinal movements. This paper addresses a real-time implementation and design of a robust embedded control-observer based on a type high-gain observer algorithm for on-line estimation and compensation of external disturbances produced by wind gusts on an autonomous quadrotor aircraft. A real-time experimental implementation of embedded Residual High Gain algorithm control is proposed in order to eliminate the effects of real perturbations in the hover position of the UAV. A Lyapunov function was used to practical stability analysis the system. Also numerical simulations were carried out to estimate wind behavior by the use of Drydel mathematical wind model. The main contribution of this work is the implementation of a Residual High Gain Observer in an outdoor real-time experiment in presence of real wind gusts perturbations. The proposed embedded algorithm control improves the stabilization of an UAV in the presence of real wind gusts with average of 8 m/s. The proposed algorithm improved the UAV behavior as shown by the GPS position experimental results, decreasing the wind effect on the translational movement of the aircraft.

This paper deals with the design and implementation of a nonlinear control strategy to solve the path tracking problem for an Autonomous Underwater Vehicle (AUV) under model uncertainties and external disturbances. First, the AUV model is transformed into the so-called regular form by an appropriate selection of state variables. The method is based on the second-order sliding mode technique known as Generalized Super-Twisting Algorithm (GSTA) introducing the design of an auto-adjustable gain controller which offers a way to ensure robustness to modeling errors and bounded external disturbances. The control law is designed to maintain a minimum margin of error in the trajectory tracking of the AUV even in the presence of damping and buoyancy disturbances. Finally, experimental results are also provided to illustrate the performances of the closed-loop system using the proposed controller.

Conventional control strategies for holonomic Unmanned Aircraft Systems (UASs) require that, while executing a mission, the UAS attitude dynamics remains very close to zero in order to satisfy the assumption of small angles approximation. This requirement may be valid for hover flight, but not for translational flights or aggressive maneuvers. To overcome this issue, this paper presents the controllability analysis for a non-linear representation of a holonomic UAS. Under the proposed scheme, it is shown that it is possible to design control schemes for stabilizing the complete UAS state space without the necessity of separating the system in attitude and translation subsystems. A set of simulations are presented to evaluate the effectiveness of the proposed control laws. These results show that the proposed strategy enables the UAS to track a desired trajectory while keeping the attitude close to a desired value.

Admittance control is mostly used for human-robot interaction. When the admittance control is applied in the task space, it needs the Jacobian matrix. When the admittance control is in joint space, it requires inverse kinematics of the robot. In this paper, the admittance control is modified to only use the orientations of the end effector to generate the desired joint angles to avoid inverse kinematics. The classical PD control is also modified with adaptive compensation and sliding mode compensation. The stability of these two PD controllers are proven. Real time experiments are presented for with a 2-DOF pan and tilt robot and a 4-DOF exoskeleton. The experiment results show the effectiveness of the proposed controllers.

Path following is a basic autonomous navigation capability for UAV’s and in this paper we present a nonlinear 3D path following kinematic controller. In order to solve this problem, we use the typical multi-loop control structure in which the navigation controller directs the aircraft to the desired path, while the inner loop acts as an autopilot which stabilizes the nonlinear dynamics and tracks the command generated by the outer loop. Under the assumption that the autopilot is well tuned and the airspeed, fight-path angle, and bank angle states converge to the desired response to their commanded values, we use a kinematic model as a suitable control model for the fixed wing UAV. With the introduction of a virtual target, the proposed Lyapunov based kinematic controller for outer loop can steer the UAV asymptotically to approach the desired path. The proposed controller is validated in a 6-DoF simulation environment platform named MAV3DSim.

This paper addresses the problem of designing and experimentally validating a nonlinear robust control to attitude and altitude of a quadrotor unmanned flying vehicle (UAV). First a disturbance observer is proposed, focus on the attitude regulation control problem of a quadrotor in presence of external disturbances based on the angular velocity measurements and the control inputs. The stability analysis of the nonlinear observer scheme is proven via the use of Lyapunov theory. Later, we focus on the altitude dynamics of a quadrotor in the presence of uncertainty like wind gust are presented. A sliding mode control was proposed, the gain of control can be decreased and, as a result, the chattering amplitude is reduced. The objective is to introduce an adaptation in the control law in order to decrease the gain to the minimal value preserving the sliding mode control and keeping his property of a finite-time convergence. Finally, simulation and experimental results in a quadrotor are presented to show the effectiveness of the proposed nonlinear algorithm in presence of external disturbances.

This contribution presents a modeling technique for an aerial manipulator based on a quad-rotor vehicle provided with a robotic arm. Dual quaternions, which are a little explored but powerful mathematical tool, are proposed as an alternative to the classical Euler angles approach for the kinematic and dynamic modeling. A feedback control law based on dual quaternions is used to validate the proposed model, taking into account the external effects of the robotic limb. Numerical simulations and experiments validate the proposal, opening a path for future research.

This paper presents the control of a quadrotor with a cable-suspended payload. The proposed control structure is a hierarchical scheme consisting of an energy-based control (EBC) to stabilize the vehicle translational dynamics and to attenuate the payload oscillation, together with a nonlinear state feedback controller based on an linear matrix inequality (LMI) to control the quadrotor rotational dynamics. The payload swing control is based on an energy approach and the passivity properties of the system&rsquo, s translational dynamics. The main advantage of the proposed EBC strategy is that it does not require excessive computations and complex partial differential equations (PDEs) for implementing the control algorithm. We present a new methodology for using an LMI to synthesize the controller gains for Lipschitz nonlinear systems with larger Lipschitz constants than other classical techniques based on LMIs. This theoretical approach is applied to the quadrotor rotational dynamics. Stability proofs based on the Lyapunov theory for the controller design are presented. The designed control scheme allows for the stabilization of the system in all its states for the three-dimensional case. Numerical simulations demonstrating the effectiveness of the controller are provided.

In recent years, solar energy has been used as an energy source for many different applications. Currently in the area of Unmanned Aerial Vehicles (UAVs), there are research studies that incorporate this renewable energy technology to increase the vehicle&rsquo, s autonomy. This technique also needs particular construction techniques and electronic boards, designed to reduce weight and increase the efficiency of all solar systems on board the UAV. As is well known, the amount of generated solar energy could be increased throughout a day a sun tracking technique is added. The present paper proves that the roll angle of a fixed wing UAV can be used to track the sun to increase the energy generated by the solar panels placed on the wing. In that case, the plane&rsquo, s attitude must be compensated with the yaw angle control to be able to perform a photogrammetric mission. This will be achieved using a control strategy based on the super-twisting technique that ensures convergence in finite time even in the presence of bounded perturbations. The design of the control laws as well as the numerical simulation and real flight results are shown to validate the use of the sun tracking system.

The use of autonomous vehicles in daily life is more commonly every day, we can find it in security missions, agriculture, windmills inspection and one of its main uses the processes of photogrammetry, because the new systems and technologies to perform the last one could give errors of centimeters in a topographic map. The studies of large areas are carried out with fixed-wing UAV, so it is necessary to ensure that the vehicle could follow a desired path even in presence of external disturbances, in order to avoid uncovered or wrong defined areas during the post-processing. In this article we analyzed and compared by simulation the use of a non-linear control technique with a PID control to stabilize the airplane’s attitude and height, to prove that in some cases only a linear control technique is enough to stabilize the UAV and also probe that the amount of energy to control the UAV is similar in both techniques. So the majority of computational resources can be assigned to object detection and collision avoidance tasks, for vehicles under 5 kilograms.

An hybrid autogyro aircraft with wind energy conversion using autorotation is proposed. The fixed wing addition to classic autogyro assists the aircraft lifting reducing speed minimum wind for the flight. A PID control strategy is used to attitude aircraft stabilization. In order to verify the generation of energy, the relationship between energy and wind speed are recorded during experimental flights.

This paper deals with the problem of enabling a team of two Unmanned Aircraft Systems to perform an autonomous load transportation task. We propose a strategy whose main objective is to ensure a stable flight of the team of agents when cooperatively transporting the load. The strategy is based on the implementation of a neural network controller and a neural network estimator, using wavelet activation functions in combination with a cooperative multi-agent control approach. To show the effectiveness and applicability of the proposed framework, a real time experimental implementation is presented. Additionally, a comparison with respect to a classic control law is also provided, demonstrating the superior performance attained when adopting the proposed controller.

This paper presents the pitching moment stability analysis and adjustment for tilt-wing unmanned aerial vehicle (UAV) in hovering flight and vertical take-off and landing (VTOL). The tilt-wing UAVs combine the features of fixed-wing UAVs with VTOL capabilities by tilting the wing and rotor system for the transition between flight modes. Due to its hybrid configuration, the altitude control study, position and attitude, poses a major problem during the entire flight for a given mission. Namely, in the first flight phase the pitching moment could present instabilities with different disturbances, such as wind, that are difficult to compensate. In this paper this problem is analyzed and possible solutions for the stabilization are presented and evaluated in a test bench. These solutions were implemented in new test prototypes demonstrating satisfactory results in outdoor flight tests.

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...

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.

International audience; This paper is about the collaborative training of a far infrared and a visible spectrum human detector; the idea is to use the strengths of one detector to fill the weaknesses of the other detector and vice versa. At first infrared and visible human detectors are pre-trained using initial training datasets. Then, the detectors are used to collect as many detections as possible. The validity of each detection is tested using a low-level criteria based on an objectness measure. New training data are generated in a coupled way based on these detections and thus reinforce both the infrared and the visible human detectors in the same time. In this paper, we showed that this semi-supervised approach can significantly improve the performance of the detectors. This approach is a good solution to generate infrared training data, this kind of data being rarely available in the community.

This paper proposes a new high gain observer for a class of non-uniformly observable nonlinear systems with coupled structure driven by sampled outputs. The considered class of systems is p...

Precision trajectory tracking problem for Unmanned Aerial Systems (UAS) is addressed in this work. A novel algorithm that combines a Nonsingular Modified Super-Twisting Controller with a High Order Sliding Mode Observer to enable an aerial vehicle tracking a desired trajectory under the assumption that i) its translational velocities are not available and ii) there are unmodeled dynamics and external disturbances. The proposed Sliding Mode Controller is based on a nonlinear sliding mode surface that ensures that the position and velocity tracking errors of all system’s state variables converge to zero in finite time. Moreover, the proposed controller generates a continuous control signal eliminating the chattering phenomenon. Finally, simulation results and an extensive set of experiments are presented in order to illustrate the robustness and effectiveness of the proposed control strategy.

The back pain is the most common injury in human activities where heavy objects must be lifted or must be suspended for a long time. A weight lifting exoskeleton also known as force augmentation exoskeleton is designed to reduce the strain on the back and the limbs and reduce the risk to suffer injuries. On the other hand, different kinds of controllers have been implemented to achieve whit this goal, for example, a conventional PD Control, PD Control with Gravity Compensation, PD Control with Adaptive Desired Gravity Compensation and PD Control with Robust Compensator. This paper aims to evaluate and compare the performance from the previously cited controllers used to reduce the strain in the back, through the implementation of each controller in a three Degrees Of Freedom (DOF) exoskeleton powered by pneumatic muscle actuators; some numerical simulations as well as experimental trials have been conducted and three different performance indices were used in order to determine the effectiveness of each one with respect to the simple PD controller when the mass to be lifted is unknown.

This paper deals with the real-time stabilization of a Quadrotor mini helicopter applying a discrete optimal control. A discrete control strategy is better adapted to be executed in a micro-controller, therefore better results are expected with respect to continuous case. Furthermore, the optimal control law allows to helicopter to save energy and then increase its time of flight. The discrete optimal control law is synthesized considering an infinite horizon together with an exact linearization of the nonlinear dynamics of flying vehicle. At the end of this procedure the optimal control law obtained through an exact linearization is simple and easier to tune compared to the optimal strategy where the exact linearization is not performed. Taking into account the obtained experimental results, the proposed control technique produces an appropriate stabilization of the mini UAV.

In this paper, a Nonsingular Fast Terminal Sliding Mode Control (RNFTSMC) is proposed to regulate the position of a mobile lower limb exoskeleton powered by linear actuators with four degrees of freedom. The developed lower limb exoskeleton has the purpose to assist the sit-to-stand and stand-to-sit tasks for people with spinal cord injury (SCI) or elderly people. Currently most of the developed lower limb exoskeletons are dealing with the presence of uncertainties (unmodeled dynamics) and external disturbances (mass changes, opposite forces), caused by the high complexity of the human limbs dynamics. The RNFTSMC has the purpose of dealing with these uncertainties, ensuring a rapid convergence in finite time to the desired position, and reducing the chattering effect produced by the nature of the controller actuators. The human intention movements have been detected using an sEMG signal classifier performed by the Myo Armband device and processed to activate the preprogrammed routines in the RNFTSMC. The desired paths for the knee and hip exoskeleton joints were obtained by recording data from a volunteer who rises from a chair and sat on it, using a motion capture system. To evaluate the effectiveness of the proposed control scheme, real-time experiments under three different scenarios have been carried out.

A fully functional prototype of a gun-launched micro air vehicle system (GLMAV) is developed for long-distance observation capabilities. A subsonic projectile is launched from an ad hoc tube and transforms into a micro air vehicle (MAV) once arrived over the site to be observed. The ballistic, transient, and operational flight phases of the GLMAV are studied theoretically and experimentally in this paper. Ballistic flight tests prove the stability of the platform in projectile mode and the availability of the inertial measurement unit and GPS measurements for the autopilot. Based on wind-tunnel measurements, a model of the transient phase is built, allowing the design and simulation of control laws for this phase. Indoor and outdoor tests of the system are successfully achieved highlighting the hovering and maneuvering flight capabilities of the GLMAV.

In this paper a novel visual positioning system for slip correction of vehicles in GPS-denied environments based on Kalman filter is presented. Kalman filter fuses 2 sources of data; the first source of data is obtained from odometry, while a computer vision algorithm is used to obtain the second source of data. Using a plane-based pose estimation algorithm that employs four reference points and the Consensus-based Tracking and Matching algorithm (CMT) we were able to obtain pose estimation with centimeter accuracy. The four reference points correspond to the pixel coordinates of the corners of a rectangle closing a region in the roof where the camera should be pointing. Since the employed camera is mounted into the vehicle and it has eye fish lens, an image distortion correction method is used. As the CMT algorithm is robust to scale and rotations, position and orientation (POSE) estimation using computer vision is obtained even if the roof is not completely visible. Autonomous navigation of a ground wheeled vehicle was achieved using the proposed algorithm performing different courses on long period of time and no big slip was observed.

In this paper, the use of a class of robust control technique as a tool to compute the robustness margin for a closed loop multi-agent system is discussed. Our approach is based on a leader/follower structure of the multiple robot systems. It is shown that, for multiple agent system with cyclic, chain and balanced topology of information exchange, the result is achieved. A Lyapunov based robust control design for coordination and trajectory tracking of multiple agent systems with parametric uncertainty is presented. The results are illustrated with examples and simulations.

This paper presents an adaptive PD control law by using a modified MIT rule. The adaptation of the controller gains is based on the adjustment mechanism of the MIT rule by the gradient method with three types of sliding-mode control, i.e., first order sliding-mode control, second order sliding-mode (2SM), and high order sliding-mode control (HOSM). The proposed adjustment mechanism with the PD controller have been designed for the altitude movement, directional and lateral dynamics of a fixed-wing miniture aerial autonomous vehicle (MAV). Several simulations have been carried out in order to analyze the response of the modified MIT rule.

An experimental validation for improving pose estimation using a linear Kalman Filter (KF) is presented in this paper. The procedure is designed to lead with localization data degraded or lost. The methodology is focused on determination, tuning and dynamics changes in the covariance matrices in the KF algorithm. Several simulations are carried out in order to validate the methodology. Similarly several flights tests are conducted in real time for validating the observer scheme. A localization system is used and modified for emulating the GPS performance. Main results show the good behavior of the proposed methodology and a video of them is available for showing the capabilities of the algorithm.

A robust control scheme for aerial path generation and tracking is proposed in this paper. The control structure is based on the MPC - Model Predictive Control- and the UDE -Uncertainty and Disturbance Estimator - approaches. Both algorithms can be used with simple controllers for becoming more robust with respect to nonlinear uncertainties and external disturbances. The proposed scheme is applied to a quadcopter vehicle. Experimental results confirm the well performance of the closed-loop system when tracking trajectories, even in the presence of perturbations.

This work presents a low level control architecture that allows to handle an aircraft with focus on takeoff and landing maneuvers for fixed wing UAV. The architecture consists of an attitude and angle rate control, on ground course angle control, airspeed control as well as auxiliar stages that allow to determine the required attitude to track the desired course and path angle to follow a desired vertical velocity. These controllers are combined in different forms depending on the phase of takeoff or landing maneuver. By means of simulation the performance of this architecture was illustrated during takeoff and landing maneuvers.

Building inspection searching for superficial defects, such as cracks, is a vital task because such damages cause economic losses or put at risk the integrity of people. For this reason, different ways to reduce the costs and risks through the use of robotic systems that allow make inspections have been studied. Among these robotic systems, we have the unmanned aerial vehicles (UAV) that allow reaching difficult access places permitting better inspection. In this work, we propose using convolutional neuronal networks for crack recognition from images captured by an UAV. To carry out the training task of the network, a database of cracks in walls was built from images collected from the Internet. The training of the network prompted encouraging results with a 95% accuracy over the training set. Experimental results of crack recognition in images were carried out validating the application of the proposal.

This paper focuses on the implementation of algorithms for formation flight of quadrotors via teleoperation system. These algorithms of navigation are based on the human gestures using a Kinect sensor. The human gestures act as reference signals in the control loop during the real-time flights. In order to test the performance and feasibility of the developed platform, experimental tests are carried out for indoor of multiple quadrotors with manual movements and with waypoints based on SE(3) geometric navigation for autonomous flights. Real-time experiments of the flights present an acceptable performance of the proposed algorithms.