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, a method of designing a nonlinear predictive controller based on a fuzzy model of the system is presented. The Takagi-Sugeno fuzzy model is used as a powerful structure for representing nonlinear dynami...
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In this paper, a method of designing a nonlinear predictive controller based on a fuzzy model of the system is presented. The Takagi-Sugeno fuzzy model is used as a powerful structure for representing nonlinear dynamic systems. So, the strategy of the fuzzy predictive control based on a fuzzy Takagi-Sugeno model is applied to the control of a chemical reactor. Indeed, the work is consists to develop, in a first step, a fuzzy model from a merger of a number of local models obtained by the principle of linearization around an operating point, or by learning through the gradient algorithm. In a second stage and basis on local models already developed, a fuzzy predictive control is synthesized with different approaches. The principal aim is to apply local generalized predictive control.
This paper deals with the problem of neural modeling of nonlinear systems. In a first place, we will discuss the neural modeling process. The approach adopted of neural modeling will be presented in a second place; th...
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This paper deals with the problem of neural modeling of nonlinear systems. In a first place, we will discuss the neural modeling process. The approach adopted of neural modeling will be presented in a second place; this method is based on mono-network neural modeling and multi-network neural modeling. The results of simulation obtained will be illustrated by a nonlinear system of second order and by a chemical process.
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 control design 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.
In this paper, we have proposed a new architecture of RBFNN. Neural network efficiency in embedded systems offers the possibility of reconfiguration and the genericity of the solution. Indeed, the same integrated syst...
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In this paper, we have proposed a new architecture of RBFNN. Neural network efficiency in embedded systems offers the possibility of reconfiguration and the genericity of the solution. Indeed, the same integrated system can approximate any input-output function thanks to the parameters update on the chip. RBF neural networks constitute a subset of the neuronal networks, which has a great potential in reducing the size of the network. Pulse mode neural networks reduce significantly hardware resources by replacing the conventional huge multiplier by a simple frequency multiplier. As application, we approximate with the proposed RBF network, a Canny operator based edge detection, which is an important step in image processing. Acceptable edge detection approximation was done, with a mean generalization error of (4,604 %) on the Wang image database. Moreover, a design synthesis on FPGA virtex V platform was done, the results of implementation lead to an operating frequency of 445,295 MHz, which offers real time application performances.
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 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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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.
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