In this paper, an adaptive backstepping method is used for a ship course tracking control. This has been done by applying a reference signal derived from a LOS algorithm. The ship model is described by a third order n...
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In this paper, an adaptive backstepping method is used for a ship course tracking control. This has been done by applying a reference signal derived from a LOS algorithm. The ship model is described by a third order nonlinear model whose parameters are unknown. The controldesign uses estimate values of the unknown parameters of the system. Then, adaptive laws of the estimation of these values have been proposed. The stability of the controlled system has been ensured by the use of a Lyapunov function. Simulation results show the effectiveness of the proposed approach and the designed controller can be used to the ship course tracking with good performances.
This paper presents a decision support tool for the effectiveness of a Quality Management System (QMS) in a company. To develop this tool, a new approach PAHP based on the combination of the Pareto Optimality Concept ...
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In this paper, a hybrid evolutionary approach, combining the theory of learning automata (LA) and the steady-state genetic algorithm (SSGA), is proposed for design of TSKtype fuzzy model (TFM). In the proposed memetic...
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In this paper, a hybrid evolutionary approach, combining the theory of learning automata (LA) and the steady-state genetic algorithm (SSGA), is proposed for design of TSKtype fuzzy model (TFM). In the proposed memetic approach, both the number of fuzzy rules and adjustable parameters in the TFM are designed concurrently. A learning automaton, which systematically updates a strategy to enhance the performance in response to the output results, is used to find the optimal number of rules, whereas the SSGA is used to perform the tuning of the TFM parameters. Computer simulations have demonstrated that the proposed hybrid method performs better than some existing methods.
This paper presents a method to compute optimal control strategies of discrete large scale nonlinear systems by using hierarchical fuzzy systems. The method is based on the decomposition principle of the global system...
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This paper presents a method to compute optimal control strategies of discrete large scale nonlinear systems by using hierarchical fuzzy systems. The method is based on the decomposition principle of the global system into interconnected subsystems becoming easier to study. Then, the differential dynamic programming procedure is applied in order to obtain the rule basis. After that, we construct limpid-hierarchical Mamdani fuzzy system in order to compute optimal control laws, for each subsystem. Simulation results of a rotary crane show that the proposed method yields to satisfactory performances. The robustness of the proposed approach is verified.
Obstacle avoidance and path planning are the most important problems in mobile robots. In this paper, a fuzzy logic controller has been constructed in order to train an intelligent robot. Gradient method is used to op...
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Obstacle avoidance and path planning are the most important problems in mobile robots. In this paper, a fuzzy logic controller has been constructed in order to train an intelligent robot. Gradient method is used to optimize consequences of a Sugeno fuzzy logic controller for the mobile robot navigation, in order to reach a target in a clutterd environment. Not only simulation results are shown in this paper, but also the “real-time” implementation has been realized onto the mini robot Khepera II. Simulation results verify successfully the application of the proposed method to real motion situations.
The important role that mammography is playing in breast cancer detection can be attributed largely to the technical improvements and dedication of radiologists to breast imaging. A lot of work is being done to ensure...
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The important role that mammography is playing in breast cancer detection can be attributed largely to the technical improvements and dedication of radiologists to breast imaging. A lot of work is being done to ensure that these diagnosing steps are becoming smoother, faster and more accurate in classifying whether the abnormalities seen in mammogram images are benign or malignant. In this paper, an evolutionary approach for design of TSK-type fuzzy model (TFM) is proposed to solve the breast cancer diagnosis problem. In the proposed method, both the number of fuzzy rules and adjustable parameters in the TFM are designed concurrently combining the compact genetic algorithm (CGA) and the steady-state genetic algorithm (SSGA). The computational experiments show that the presented approach can obtain better generalization than some existing methods reported recently in the literature using the widely accepted Wisconsin breast cancer diagnosis (WBCD) database.
In this paper, we propose a computed torque controller for a dynamic model of nonholonomic mobile manipulator with bounded external disturbances in order to treat the adaptive tracking control. Firstly, a velocity con...
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In this paper, we propose a computed torque controller for a dynamic model of nonholonomic mobile manipulator with bounded external disturbances in order to treat the adaptive tracking control. Firstly, a velocity controller is designed for the kinematic steering system. Secondly, a computed torque controller is designed such that the mobile manipulator velocity converges to the desired velocity controller deduced from the first step. In particular, the mobile manipulator can globally follow any path such as a straight line or a circle. Simulation results are given to demonstrate the effectiveness of the proposed controller.
The important role that mammography is playing in breast cancer detection can be attributed largely to the technical improvements and dedication of radiologists to breast imaging. A lot of work is being done to ensure...
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The important role that mammography is playing in breast cancer detection can be attributed largely to the technical improvements and dedication of radiologists to breast imaging. A lot of work is being done to ensure that these diagnosing steps are becoming smoother, faster and more accurate in classifying whether the abnormalities seen in mammogram images are benign or malignant. This paper takes a step in that direction by introducing a hybrid evolutionary neural network classifier (HENC) combining the evolutionary algorithm, which has a powerful global exploration capability, with gradient-based local search method, which can exploit the optimum offspring to develop a diagnostic aid that accurately differentiates malignant from benign pattern. The computational experiments show that the presented HENC approach can obtain better generalization and much lower computational cost than the existing methods reported recently in the literature using the widely accepted Wisconsin breast cancer diagnosis (WBCD) database with some improvements.
In this article, we investigate the synchronization of robot manipulators group under coordinated and cooperative scheme. In cooperative schemes all agents were fully interconnected, such that all robots have a weight...
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In this article, we investigate the synchronization of robot manipulators group under coordinated and cooperative scheme. In cooperative schemes all agents were fully interconnected, such that all robots have a weight on the overall dynamics. In the coordinated schemes the leader robot or the master robot establishes the synchronized action of all the slave systems. Based on emergent consensus algorithm, the proposed controller works to position synchronization of multiple robot manipulators. The control strategy is to synchronize the angular position and the velocity of each robot in the system with respect to the common desired trajectory and the angular positions and velocities of other robots. Modeled by an undirected graph, the cooperative robots network only requires local neighbor-to-neighbor information exchange between manipulators and does not assume the existence of an explicit leader in the team. However the objective in coordinated scheme is to design interconnections and feedback controllers for the slaves, such that their positions and velocities synchronize to those of the leader robot. It is assumed that network robots have the same number of joints and any configuration made possible by one in the group can be completed by each robot in the cooperative system.
We address the problem of designing decentralized laws to control a team of general fully actuated manipulators which synchronize their movements while following the same desired trajectory. To this effect we investig...
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We address the problem of designing decentralized laws to control a team of general fully actuated manipulators which synchronize their movements while following the same desired trajectory. To this effect we investigate an adaptive control of an identical multiple manipulators, with unknown inertia parameters, tracking a common trajectory. It is also assumed that all the robots in the synchronization system have the same number of joints and equivalent joint work spaces, i.e. any possible configuration of a given robot in the system can be achieved by any other robot in the system. Adaptive controls are derived for group of manipulators using a consensus algorithm. This consensus algorithm is applied on the group level to estimate the time-varying group trajectory information in a distributed manner. The proposed strategy only requires local neighbor-to-neighbor information exchange between manipulators and does not assume the existence of any explicit leaders in the team. The interaction topology of a network of agents is represented using an indirected graph. The coordination strategy is to let each manipulator track its desired trajectory while synchronizing its motion with the others manipulator's motions so that differential position errors amongst robots converge to zero.
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