A distributed fault-tolerant formation control law is designed in this paper for multi-agent systems under external disturbances and actuator faults including time-varying loss of effectiveness faults. The initial pos...
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In this article, we study the formation problem for a group of mobile agents in a plane, in which the agents are required to maintain a distribution pattern, as well as to rotate around or remain static relative to a ...
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We study the formation control problem for a group of mobile agents in a plane, in which each agent is modeled as a kinematic point and can only use the local measurements in its local frame. The agents are required t...
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The purpose of this paper is the regulation of water level in a photovoltaic pumping system. To reach this objective, we have developed an algorithm with Matlab / simulink which gives as result, the value of the refer...
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This paper proposes a novel method to improve mobile robot robustness with respect to kinematic modeling errors during visual servoing task. Instead of using first-order error-dynamics, as it is usually done, we use t...
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ISBN:
(纸本)9781467364591
This paper proposes a novel method to improve mobile robot robustness with respect to kinematic modeling errors during visual servoing task. Instead of using first-order error-dynamics, as it is usually done, we use the second-order error-dynamics leading to a new control law. The main aim of this approach is to guarantee a robust visual servoing scheme. In fact, the new control law ensures the convergence of the mobile robot to its desired pose even in the presence of modeling errors. Experimental results are presented to validate our approach and to demonstrate its efficiency.
In this paper, we are interested in 3D visual servoïng path planning and path tracking. In fact, in the 3D visual servoïng task, there is no control in the image space and the object may get out of the camer...
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In this paper, we are interested in 3D visual servoïng path planning and path tracking. In fact, in the 3D visual servoïng task, there is no control in the image space and the object may get out of the camera field of view during servoing. To solve this problem, we have used a new approach based on a flatness concept. The 3D visual servoïng suffer from another major problem, is to determine the relative pose of the camera and the object. Generally, the pose estimation is made by correspondences between points of one image and points of the space that is the 2D-3D correspondence. In our work we have used a 3D visual sensor called Kinect. To show the efficiency of the proposed algorithm, we have implemented it on a wheeled Koala robot.
We are interested in this paper in the 2D visual servoïng for a mobile robot type Koala using radial basis function (RBF) neural network (NN). Seen that the interaction matrix, expressing the relationship between...
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We are interested in this paper in the 2D visual servoïng for a mobile robot type Koala using radial basis function (RBF) neural network (NN). Seen that the interaction matrix, expressing the relationship between the camera motion and the consequent changes on the visual features, contains parameters to be estimated (depth) and requires a calibration phase of the camera. In more, the model of the robot can contain uncertainties engendered the movement with sliding. An online identification, using NN was proposed to overcome these problems. The RBF NN is used to estimate the block formed by the interaction matrix and the model inverts of the robot. The considered images are described by objects given by four points. Seen that the variables number of the estimated function is important, what can cause a problem of the use of an excessive number of RBFs. As remedy, we used a new approach consists in considering that a single point is sufficient to solve the problem of the 2D visual servoïng of the mobile robot.
The main objective of this work is to develop the classification application for a new promising method for meat characterization getting information about the state of the vacuum packed meat in supermarket. A supervi...
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ISBN:
(纸本)9781467364591
The main objective of this work is to develop the classification application for a new promising method for meat characterization getting information about the state of the vacuum packed meat in supermarket. A supervised training using neural networks and according to the back error propagation method is used. The training ensure a classification with high precision and with ability to answer correctly the inputs then with ability to classify the erroneous inputs which do not exist in the data base, without creating new classes. Method classification consist to classify the model parameters of the physical model of the meat according to a data base including the model's parameters for different beef muscle in different days.
This paper describes a Predictive control method used for track following control in hard disc drives (HDD). While reading/ writing data, the head must be positioned on the target data track quickly and precisely, and...
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In this work, we use the approach based on observers such as the neural observer in order to introduce the diagnosis of nonlinear systems. There are different techniques for training the neural networks. Among these t...
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