A Simple and Robust Gray Image Encryption Scheme Using Chaotic Logistic Map and Artificial Neural Network

A robust gray image encryption scheme using chaotic logistic map and artificial neural network (ANN) is introduced. In the proposed method, an external secret key is used to derive the initial conditions for the logistic chaotic maps which are employed to generate weights and biases matrices of the multilayer perceptron (MLP). During the learning process with the backpropagation algorithm, ANN determines the weight matrix of the connections. The plain image is divided into four subimages which are used for the first diffusion stage. The subimages obtained previously are divided into the square subimage blocks. In the next stage, different initial conditions are employed to generate a key stream which will be used for permutation and diffusion of the subimage blocks. Some security analyses such as entropy analysis, statistical analysis, and key sensitivity analysis are given to demonstrate the key space of the proposed algorithm which is large enough to make brute force attacks infeasible. Computing validation using experimental data with several gray images has been carried out with detailed numerical analysis, in order to validate the high security of the proposed encryption scheme

Méthodes d'identification pour des systèmes non linéaires avec paramètres variant dans le temps (application aux machines tournantes à induction)

L'objectif de cette thèse est le développement des méthodes d'identification simples à implémenter en temps réel pour des systèmes non linéaires avec paramètres variant dans le temps. Les algorithmes développés sont destinés à l'identification des paramètres afin de permettre leurs mises à jour "en-ligne" dans un schéma de commande. Deux types d'approche sont développés: La première approche est basée sur les observateurs à structure variable (CSV). Dans cette approche, deux types de schéma d'identification des paramètres électriques et du flux rotorique d'un moteur asynchrone (MAS) sont proposés. Le premier et le deuxième schéma sont basés respectivement sur une loi dynamique et une loi d'estimation algébrique convergeant en temps fini. Les résultats en simulation et i1 en implémentation en temps réel ont été comparés à ceux obtenus par la méthode des moindres carrés récursifs (MCR). L'algorithme d'estimation basé sur l'OSV est plus robuste par rapport aux incertitudes paramétriques, aux bruits de mesure et est plus facile à implémenter en temps réel que la méthode MCR. La deuxième approche est basée sur un prédicteur neuronal à base radiale. Nous avons proposé une méthode basée sur ce type de prédicteur pour une classe de systèmes non linéaires plus large. Une application combinant cette méthode avec un observateur à haut gain sur l'identification des paramètres électriques et la vitesse rotorique d'un MAS triphasé a été également étudiée. Les simulations de défaut réalisées permettent l'extension de la méthode aux diagnostics et détection des pannes ainsi qu'à la commande du MAS sans utiliser les capteurs de vitesse. Les résultats expérimentaux obtenus par les deux approches montrent bien la convergence assez rapide des estimés vers leurs vraies valeurs et leurs robustesses par rapport aux variations des paramètres dans le temps, aux bruits de mesure, aux incertitudes paramétriques et du modèle ainsi qu'aux effets d'échantillonnages.This dissertation deals with on-line identification of nonlinear systems with time-varying parameters. The algorithms developed here are potentially useful for the design of the drives that can adjust controller parameters automatically. Another possible application is for the detection of failure. Two approaches have been designed. The first approach is based on the sliding mode observer (SMO). Two identification schemes using this approach have been investigated for electrical parameters and rotor flux estimation of an induction motor. The first and the second scheme are based respectively on the dynamical law and algebraic law which converge in finite time. The simulation and expenmental results have been compared to the results obtained using the recursive least square methods (RLSM). The methods based on the SMO are more robust with respect to parameters uncertainties, measures noises and are more easily implementable than the RLSM method. The second approach is based on the radial basis function neuronal predictor. We proposed parameters estimation scheme using this predictor for a large class of nonlinear systems with time-varying parameters. This method has been combined with high-gain observer to estimate electrical parameters and rotor speed of a three phase induction motor. The method can also be applied to the design of the mot or drives in sensorless control. Real-time implementation results obtained using both approaches show the fast convergence of the estimates to their true values and the robustness of both approaches with respect to time-varying parameters, measures noises, parameters and model uncertainties and sampling effects.ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

Induction Motor Windings Faults Detection Using Active and Reactive Power Based Model Reference Adaptive System Estimator

The paper is concerned with detection of a stator and rotor winding faults in a squirrel-cage induction motor. The idea of the fault detection is based on a hypothesis that each of windings faults results in a sharp increase or decrease of internal parameters’ values of the machine, therefore it can be treated as a suitable fault symptom. Resistances of the stator and rotor windings seem to be adequate quantities due to their direct relationship with the machine windings. An observation and analysis of the parameters’ changes in a real- time domain enables to an incipient detection of the fault. It is evident that internal parameters of the machine can’t be measured directly during operation on the drive system thus the only way is an estimation by specialized algorithms. In the paper two estimators based on active (P) and reactive (Q) power based Model Reference Adaptive System (PQ-MRAS) estimators were utilized to achieve this goal. The estimator employs the active (P) and reactive (Q) power of the machine which is calculated by the only measurable signals, such as stator voltage and current. Two simple algorithms for faults detection are proposed as well. Detailed description of fault detection systems is included in the paper. Proposed systems were tested on computer simulations performed by MATLAB/Simulink software

A novel neural network-based algorithm for wind speed estimation and block-backstepping control of PMSG wind turbine systems for maximum power extraction

International audienceIn this paper, an adaptive control scheme for maximum power point tracking of stand-alone PMSG wind turbine systems (WTS) is presented. A novel procedure to estimate the wind speed is derived. To achieve this, a neural network identifier (NNI) is designed in order to approximate the mechanical torque of the WTS. With this information, the wind speed is calculated based on the optimal mechanical torque point. The NNI approximates in real-time the mechanical torque signal and it does not need off-line training to get its optimal parameter values. In this way, it can really approximates any mechanical torque value with good accuracy. In order to regulate the rotor speed to the optimal speed value, a block-backstepping controller is derived. Uniform asymptotic stability of the tracking error origin is proved using Lyapunov arguments. Numerical simulations and comparisons with a standard passivity based controller are made in order to show the good performance of the proposed adaptive scheme

Adaptive Control for a Class of Uncertain Nonlinear Systems : Application to PhotovoltaÏc Control Systems

International audienceIn this technical note, an adaptive control framework for compensation of uncertainties and perturbations that satisfy the matching condition on a class of nonlinear dynamic systems is presented. The proposed method does not need the explicit knowledge of the bound values on the uncertainties, and the resulting compensatory term is continuous. The application of this formulation for maximum power point tracking and unity power factor on grid-connected photovoltaic systems is presented

A new adaptive control strategy for a class of nonlinear system using RBF neuro-sliding-mode technique: application to SEIG wind turbine control system

International audienceIn this paper, a new hybrid method which combines radial basis function (RBF) neural network with a sliding-mode technique to take advantage of their common features is used to control a class of nonlinear systems. A real-time dynamic nonlinear learning law of the weight vector is synthesized and the closed-loop stability has been demonstrated using Lyapunov theory. The solution presented in this work does not need the knowledge of the perturbation bounds, neither the knowledge of the full state of the nonlinear system. In addition, the bounds of the nonlinear functions are assumed to be unknown and the proposed RBF structure uses reduced number of hidden units. This hybrid control strategy is applied to extract the maximum available energy from a stand-alone self-excited variable low-wind speed energy conversion system and design the dc-voltage and rotor flux controllers as well as the load-side frequency and voltage regulators assuming that the measured outputs are the rotor speed, stator currents, load-side currents and voltages despite large variation of the rotor resistance and uncertainties on the inductances. Finally, simulation results compared with those obtained using the well-known second-order sliding-mode controller are given to show the effectiveness and feasibility of the proposed approach

A Modified ESC Algorithm for MPPT Applied to a Photovoltaic System under Varying Environmental Conditions

Photovoltaic solar energy is one of the most important renewable energy sources. However, the production of this energy is nonlinear and varies depending on atmospheric parameters. Therefore, the operating point of the photovoltaic panel (PV) does not always coincide with the maximum power point (MPP). A mechanism that allows the research of the maximum power point known as maximum power point tracking (MPPT) algorithm is then needed to yield the maximum power permanently. This paper presents an intelligent control technique based on the ESC (Extremum Seeking Control) method for MPPT under varying environmental conditions. The proposed technique is an improvement of the classical ESC algorithm with an additional loop in order to increase the convergence speed. A detailed stability analysis is given not only to ensure a faster convergence of the system towards an adjustable neighborhood of the optimum point but also to confirm a better robustness of the proposed method. In addition, simulation results using Matlab/Simulink environment and experimental results using Arduino board are presented to demonstrate that the proposed modified ESC method performs better than the classical ESC under varying atmospheric conditions

Coexisting attractors and bursting oscillations in IFOC of 3-phase induction motor

The dynamics of indirect field oriented control (IFOC) of 3-phase induction motor is studied in this paper. The dynamical behaviors of the studied system are performed using bifurcation diagrams, maximum Lyapunov exponent plots, phase portraits, and isospike diagram. The numerical simulation results reveal that the IFOC of 3-phase induction motor displays coexistence of attractors for the same set of IFOC of 3-phase induction motor parameters, periodic and chaotic bursting oscillations. Basins of attraction of different competing attractors are plotted showing complex basin boundaries. The numerical simulation finding are validated by the OrCAD-Spice results