Hamed Jabbari Asl

AN ADAPTIVE SCHEME FOR IMAGE-BASED VISUAL SERVOING OF AN UNDERACTUATED UAV

An image-based visual servoing (IBVS) method is proposed for controlling the 3D translational motion and the yaw rotation of a quadrotor. The dynamic model of this unmanned aerial vehicle (UAV) is considered at the design stage to account for its underactuation. In contrast with previous IBVS methods for underactuated UAVs, which used spherical image moments as visual features, the proposed controller makes use of appropriately defined perspective moments. As a consequence, we gain a clear improvement in performance, as satisfactory trajectories are obtained in both image and Cartesian space. In addition, an adaptation mechanism is included in the controller to achieve robust performance in spite of uncertainties related to the depth of the image features and to the dynamics of the robot. Simulation results in both nominal and perturbed conditions are presented to validate the proposed method.

Robust vision-based control of an underactuated flying robot tracking a moving target

In this paper a robust image-based visual servo (IBVS) scheme is presented for a quadrotor tracking a moving target. The objective is to consider the full dynamics of the system to design vision-based controllers for the translational motion and the yaw rotation of this unmanned aerial vehicle. Passivity properties of the dynamics of the image features obtained from perspective image moments in a virtual image plane are used to solve the problem of designing full dynamic IBVS controllers for these robots. Nonlinear robust controllers are designed to deal with uncertainties in the dynamics of the image features and also uncertainties in the dynamics of the robot. The controllers need neither 3D information of the target model nor yaw information of the robot. Stability analysis guarantees that the states of the system are uniformly ultimately bounded. Simulation results are presented to validate the designed controllers.

Output feedback image-based visual servoing control of an underactuated unmanned aerial vehicle

In this article, image-based visual servoing control of an underactuated unmanned aerial vehicle is considered for the three-dimensional translational motion. Taking into account the low quality of accelerometers’ data, the main objective of this article is to only use information of rate gyroscopes and a camera, as the sensor suite, in order to design an image-based visual servoing controller. Kinematics and dynamics of the unmanned aerial vehicle are expressed in terms of visual information, which make it possible to design dynamic image-based visual servoing controllers without using linear velocity information obtained from accelerometers. Image features are selected through perspective image moments of a flat target plane in which no geometric information is required, and therefore, the approach can be applied in unknown environments. Two output feedback controllers that deal with uncertainties in dynamics of the system related to the motion of the target and also unknown depth information of the image are proposed using a linear observer. Stability analysis guarantees that the errors of the system remain uniformly ultimately bounded during a tracking mission and converge to 0 when the target is stationary. Simulation results are presented to validate the designed controllers.

Vision-based control of a quadrotor utilizing artificial neural networks for tracking of moving targets

Abstract This paper investigates the application of an image based visual servoing (IBVS) mechanism for controlling the translational and rotational movements of a quadrotor helicopter; via this scheme, the helicopter can track a moving target by means of a camera installed underneath. The image features chosen for this purpose are the perspective image features. A direct adaptive neural controller was designed to control transitional motion. The controller makes use of neural network controller radial basis function (RBF) to deal with the image dynamic uncertainties. These uncertainties relate to the depth of visual information and the movement of the considered target. Moreover, a sliding mode controller designed by using the image features has been presented for controlling the rotational movement of the helicopter. Also, due to the lack of accurate image velocity and the Euler angles, appropriate observers have been designed and used. The designed scheme needs no geometric information from the object followed by the quadrotor; therefore, this method can be employed in unknown environments. The simulation results for ideal and non-ideal conditions indicate that, in spite of the problems such as the uncertainty of the image depth and the mobility of the target, both in the rotational and translational motions, the helicopter has been able to reach the desired altitude and to properly track the moving target.

Adaptive control of variable-speed wind turbines for power capture optimisation:

Rotor speed control of wind turbines is a key factor in achieving the maximum power of wind. It is known that a high-performance controller can significantly increase the amount of energy that can be captured from this source. The main problem regarding this issue is the lack of information about the correct dynamic model of the system. This uncertainty of the model is generally associated with unknown parameters (structured uncertainty) and/or external disturbances (unstructured uncertainty). Some adaptive and robust control approaches are developed in the literature in order to deal respectively with structured and unstructured uncertainties. In this paper, to compensate for both types of uncertainty, a robust controller, which includes an adaptive feedforward term, is proposed to track the optimal speed. In addition to considering the uncertainties, another advantage of the presented approach is that, using a smooth control effort, it provides global asymptotic tracking. A complete stability proof of the system is presented, and simulation results illustrate the effectiveness of the controller.

Asymptotic Vision-Based Tracking Control of the Quadrotor Aerial Vehicle

This paper proposes an image-based visual servo (IBVS) controller for the 3D translational motion of the quadrotor unmanned aerial vehicles (UAV). The main purpose of this paper is to provide asymptotic stability for vision-based tracking control of the quadrotor in the presence of uncertainty in the dynamic model of the system. The aim of the paper also includes the use of flow of image features as the velocity information to compensate for the unreliable linear velocity data measured by accelerometers. For this purpose, the mathematical model of the quadrotor is presented based on the optic flow of image features which provides the possibility of designing a velocity-free IBVS controller with considering the dynamics of the robot. The image features are defined from a suitable combination of perspective image moments without using the model of the object. This property allows the application of the proposed controller in unknown places. The controller is robust with respect to the uncertainties in the translational dynamics of the system associated with the target motion, image depth, and external disturbances. Simulation results and a comparison study are presented which demonstrate the effectiveness of the proposed approach.

Adaptive vision-based control of an unmanned aerial vehicle without linear velocity measurements

In this paper, an image-based visual servo controller is designed for an unmanned aerial vehicle. The main objective is to use flow of image features as the velocity cue to compensate for the low quality of linear velocity information obtained from accelerometers. Nonlinear observers are designed to estimate this flow. The proposed controller is bounded, which can help to keep the target points in the field of view of the camera. The main advantages over the previous full dynamic observer-based methods are that, the controller is robust with respect to unknown image depth, and also no yaw information is required. The complete stability analysis is presented and asymptotic convergence of the error signals is guaranteed. Simulation results show the effectiveness of the proposed approach.

Vision-based control of a flying robot without linear velocity measurements

In this paper, image-based visual servo (IBVS) control of the quadrotor unmanned aerial vehicle (UAV) is considered. The main purpose of this paper is to use flow of image features as the velocity cue to compensate the low quality of linear velocity information obtained from accelerometers. Image features are selected from a combination of suitable perspective image moments without requiring geometric model of the observed target. Using respectively a linear and a nonlinear observer, two output feedback controllers are proposed. The main advantage of these approaches is that they are robust with respect to unknown depth information of the image. Stability analyses show that the states of the system are bounded and the error signals converge to zero during the positioning task. Simulation results are presented to illustrate the effectiveness of the proposed approaches.

Vision-based control of an underactuated flying robot with input delay:

In this paper a vision-based tracking controller is designed for the quadrotor vertical take-off and landing of an unmanned aerial vehicle. An imaged-based visual servoing approach is utilised to localise the quadrotor with respect to a moving target. Perspective image moments are used to define the visual features, which are projected on a rotated image plane to simplify the image dynamics. Attitude information and angular velocities are assumed to be available and the controller uses the flow of image features as the linear velocity cue. Presence of delay in processing and communication is modelled as a constant time delay in the force input of the translational dynamics, where a controller is designed for theses dynamics to compensate the delay effect. This controller is saturated in order to meet the quadrotor model constraint. A dynamic surface control approach is utilised for the rotational dynamics to track the desired attitude, defined through the position control loop. The stability properties of the complete control scheme are analysed using a theory of nonlinear cascaded systems. Simulation examples are provided in both nominal and perturbed conditions which show the effectiveness of the proposed theoretical results.

An assist-as-needed control scheme for robot-assisted rehabilitation

This paper addresses the assist-as-needed (AAN) control problem for robotic orthoses. The objective is to design a stable AAN controller with an adjustable assistance level. The controller aims to follow a desired trajectory while allowing an adjustable tracking error with low control effort to provide a freedom zone for the user. By ensuring the stability of the system and providing the freedom zone, the controller combines the advantages of both model-based and non-model-based AAN controllers existing in the literature. Furthermore, the controller provides a priori bounded control command, and includes an adaptive neural network term to compensate for the uncertainties of dynamic model of the system, mainly when a precise tracking is of interest. The stability of the closed-loop system is well analysed based on the Lyapunov method. The effectiveness of the proposed control scheme is validated through experiments using a lower extremity robotic exoskeleton.