Improving the H2MLVQ algorithm by the Cross Entropy Method

This paper addresses the use of a stochastic optimization method called the Cross Entropy (CE) Method in the improvement of a recently proposed H2MLVQ (Harmonic to minimum LVQ) algorithm, this algorithm was proposed as an initialization insensitive variant of the well known Learning Vector Quantization (LVQ) algorithm. This paper has two aims, the first aim is the use of the Cross Entropy (CE) Method to tackle the initialization sensitiveness problem associated with the original (LVQ) algorithm and its variants and the second aim is to use a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, good competitive numerical results on several datasets are reported

Comments on actuator fault accommodation

International audienceThe present work concerns the problem of progressive accommodation to actuator failure. An optimal nonlinear controller synthesis approach is formulated on the basis of the closed loop stability objective. The authors show the interest of the proposed method even for a local analysis when a linear approximation is used. This work focuses on a solution to ensure stability while accommodating to actuator failure. The approach is illustrated in an academic example

Diagnostic de défauts par l'approche RBC ratio

International audienceDans le cadre de l'ACP, les concepts de détectabilité et d'isolabilité de défauts ont été développés plus particulièrement pour quelques indices de détection. Dans ce papier, nous avons étendu ces concepts afin d'être unifiés et valables pour tout indice ayant une forme quadratique. L'approche RBC a été utilisée pour le diagnostic de défauts unidimensionnels de grandes amplitudes. En revanche, les défauts peuvent être dans plusieurs directions. Pour cala, nous avons proposé une RBC multidimensionnelle. Ce papier présente également une nouvelle approche nommée RBC ratio (RBCr). Elle est dédiée au diagnostic des défauts détectables de faibles amplitudes. Une diagnosabilté qui s'appuie sur cette méthode garantit l'identification de tels défauts. Toutefois, l'isolation de ces derniers n'est garantie que si leurs amplitudes satisfassent une condition suffisante d'isolabilité. Un exemple simulé est présenté afin d'illustrer la théorie d'une telle diagnosabilité

Improving the H2MLVQ algorithm by the Cross Entropy Method

This paper addresses the use of a stochastic optimization method called the Cross Entropy (CE) Method in the improvement of a recently proposed H2MLVQ (Harmonic to minimum LVQ) algorithm, this algorithm was proposed as an initialization insensitive variant of the well known Learning Vector Quantization (LVQ) algorithm. This paper has two aims, the first aim is the use of the Cross Entropy (CE) Method to tackle the initialization sensitiveness problem associated with the original (LVQ) algorithm and its variants and the second aim is to use a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, good competitive numerical results on several datasets are reported

Trajectory tracking and time delay management of 4-mecanum wheeled mobile robots (4-MWMR)

International audienceNowadays, wheeled mobile robots have a very important role in industrial applications, namely in transportation tasks thanks to their accuracy and rapidity. However, meeting obstacles while executing a mission can cause an important time delay, which is not appreciable in industry where production must be optimal. This paper deals with the time delay management, the trajectory generation and the tracking problem applied on four wheeled omnidirectional mobile robots. A strategy is proposed to minimize or compensate the time delay caused by obstacles. The approach is done by updating the reference trajectory. This update helps to track the trajectory in real time, a new control law based on the feedback linearization control theory is synthesized to track perfectly generated or updated trajectories

Detection & isolation of sensor and actuator additive faults in a 4-mecanum wheeled mobile robot (4-MWMR)

International audienceIn this paper, the fault detection and isolation problem regarding actuation and sensing of a 4-mecanum wheeled mobile robot (4-MWMR) is studied. The challenge with respect to the current state of the art lies in detecting and distinguishing wheel sensor from wheel actuator additive faults for this kind of robots. An approach based on generating residuals is proposed. Sensor faults isolation is based on simply analyzing residual signatures which are different under each sensor fault. Due to omni-move properties, actuator faults are, however, more difficult to be isolated. More residual characteristics must be taken into consideration to achieve the isolation

New approach for gas identification using supervised learning methods (SVM and LVQ)

This article proposes a new approach for gas identification, this approach relies on applying supervised learning methods to identify a single gas as well as a mixture of two gases. The gas is trapped in a gas discharge tube, it is then ionized at a relatively low pressure using an HV transformer. The images captured after the ionization of each single gas is then captured and transformed into a database after being treated in order to be classified. The obtained results were very satisfying for SVM as well as for LVQ. For the case of identification of a single gas, the learning rate as well as the validation rate for both methods were 100%. However, for the case of mixture of two gases, a Multi-Layer Perceptron neural network was used to identify the gases, the learning rate as well as the validation rate were 98.59% and 98.77% respectively. The program developed on MATLAB takes the captured image as an input and outputs the identified gases for the user. The gases used in the experiments are Argon (Ar), oxygen (O2), Helium (He) and carbon dioxide (CO2)

Neural Modeling and Control of Diesel Engine with Pollution Constraints

The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.Comment: 15 page