Experimental validation and intelligent control of a stand-alone solar energy conversion system using dSPACE platform

This paper presents the performances of an artificial intelligent fuzzy logic controller (FLC) based maximum power point tracking (MPPT) and a conventional perturb and observe (P&O) based MPPT controller is presented for a stand-alone PV system and tested in an emulated test bench experimentation. The studied system is composed of a DC power supply emulating the PV panel, a DC/DC boost converter, a variable resistive load and a real-time MPPT controller implemented in the dSPACE DS1104 controller. To verify the performance of the FLC proposed, several simulations have been performed in Matlab/Simulink environment. The proposed method outperforms the P&O method in terms of global search capability and dynamic performance, according to the comparison with the P&O method. To verify the practical implementation of the proposed method, the control of the emulated PV source and the MPPT algorithms are designed using the simulink/Matlab environment and implemented on dSPACE DS1104 controller. Experimental results confirm the efficiency of the proposed method and its high accuracy to handle the resistance varying

Multi-objective optimization of fuzzy MPPT using improved strength Pareto evolutionary algorithm

International audienceWith the exception of a few simple applications, all photovoltaic (PV) conversion systems have a maximum power point tracking (MPPT) unit which allows an optimal extraction of power. In this paper, the fuzzy logic controller and evolutionary methods are combined to obtain an efficient MPPT unit with a fast response in transient state and minimal errors in steady state. The proposed combination shows a good performance in simulation and offers a varied set of MPPT controllers

Adaptive fuzzy controller based MPPT for photovoltaic systems

International audienceThis paper presents an intelligent approach to optimize the performances of photovoltaic systems. The system consists of a PV panel, a DC–DC boost converter, a maximum power point tracker controller and a resistive load. The key idea of the proposed approach is the use of a fuzzy controller with an adaptive gain as a maximum power point tracker. The proposed controller integrates two different rule bases. The first is used to adjust the duty cycle of the boost converter as in the case of a conventional fuzzy controller while the second rule base is designed for an online adjusting of the controller’s gain. The performances of the adaptive fuzzy controller are compared with those obtained using a conventional fuzzy controllers with different gains and in each case, the proposed controller outperforms its conventional counterpart

Optimal design of PID controller by Multi-objective genetic algorithms

International audienceA novel design method for PID controller with optimal parameters is proposed based on the Improved Non-dominated Sorting Genetic Algorithm II(NSGA-II). The design of PID controller is formulated as multi-objective optimization problem where the integral of time multiplied by absolute error and integral of the square of the error (ISE), are optimized simultaneously. By testing two control systems, the proposed method has been able to produce a good performance control

Méthodologie de conception de contrôleurs intelligents par l'approche génétique (application à un bioprocédé)

Dans ce travail, le problème de conception de contrôleurs flous est étudié. Dans une première partie, on présente un état de l'art sur les techniques utilisées à savoir les algorithmes génétiques et ses différentes variantes, les réseaux de neurones, la logique floue et leurs hybridations. Prenant appui sur cet état de l'art nous proposons une première méthode de conception des contrôleurs flous de Mamdani par algorithmes génétiques simples. Cette méthode est en suite améliorée par l'emploi des algorithmes génétiques hiérarchisés. Ces derniers permettent par le biais de la structure de leurs chromosomes, une meilleure optimisation des paramètres du contrôleur tout en éliminant les règles incohérentes qui peuvent se présenter, comme pour la première méthode, à la fin du processus d'optimisation. La dernière méthode proposée concerne la synthèse des contrôleurs flous de Sugeno. Elle est basée sur une procédure d'apprentissage hybride qui se déroule en deux étapes. Durant la première étape, le contrôleur flou est représenté sous forme d'un réseau de neurones multicouches dont les paramètres sont optimisés par l'algorithme de rétropropagation. Dans la deuxième étape, les paramètres obtenus à l'issue de la première phase sont extraits et optimisés par le NSGA-II suivant un codage hiérarchisé. L'ensemble des ces méthodes est appliqué pour la conduite d'un procédé de fermentation alcoolique en mode continu.In this work, the problem of design of fuzzy controllers is studied. In a first part, we present a state of art on the techniques used, namely the genetic algorithms and its various alternatives, the neural networks, fuzzy logic and their hybridizations. Taking support on this state of art, we propose a first design method of the fuzzy controllers of Mamdani by simple genetic algorithms. Thereafter, this method is improved by the use of the hierarchical genetic algorithms. These algorithms allow, by the means of the structure of their chromosomes, a better optimization of the controller parameters while eliminating the incoherent rules which can arise, as well as for the first method, at the end of the optimization process. The last method suggested relates to the synthesis of the fuzzy controllers of Sugeno. It is based on a hybrid procedure of training which proceeds in two stages. During the first stage, the fuzzy controller is represented in the form of a network of multi-layer neural networks, whose parameters are optimized by the algorithm of retro propagation. In the second phase, the parameters obtained at the end of the first phase are extracted and optimized by the NSGA-II according to a coding arranged hierarchically. These methods are applied for control of an alcoholic fermentation process in continuous mode.TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF

Intelligent design of fuzzy logic controller using NSGA-II

International audienceThis work proposes a new design method of fuzzy controllers with well-formed membership functions, minimal number of fuzzy rules and optimal values of scale factors. To achieve this goal, we use multiobjective genetic algorithms that optimize simultaneously the number of fuzzy rules through the control gens, and the parameters related to membership functions, conclusions of fuzzy rules and scale factors through parametric genes. The proposed method is applied to a bio-process control problem