Project Leader: SAKLY Anis
Summary and Objectives
The research carried out within this project focuses on the use of intelligent techniques in various engineering applications. Among these techniques are fuzzy logic, neural networks, and metaheuristic methods such as genetic algorithms, ant colony optimization, and particle swarm optimization.
Our research aims to develop and implement these intelligent methods for two main purposes:
The project is organized around four main research axes:
- The analysis and synthesis of control systems for complex processes, and
- The processing of signals and images.
Axis 1: Analysis and Synthesis of Intelligent Control Laws for Nonlinear Systems
This axis, which belongs to the field of Automatic Control, focuses on the stability analysis of nonlinear systems such as fuzzy systems, switched systems, time-delay systems, and chaotic systems. It also addresses the design of stabilizing and optimal control laws for such systems using non-conventional approaches like neuro-fuzzy and metaheuristic methods.
Objective 1:
Identification and control of nonlinear systems using neuro-fuzzy techniques combined with advanced metaheuristic optimization tools.
The main goal is to simplify the structure of the developed neuro-fuzzy systems while maximizing their performance.
Identification and control of nonlinear systems using neuro-fuzzy techniques combined with advanced metaheuristic optimization tools.
The main goal is to simplify the structure of the developed neuro-fuzzy systems while maximizing their performance.
Objective 2:
Optimal control, with and without constraints, of switched systems based on non-conventional approaches.
The proposed methods combine classical optimal control techniques with non-conventional optimization methods, such as metaheuristics.
Optimal control, with and without constraints, of switched systems based on non-conventional approaches.
The proposed methods combine classical optimal control techniques with non-conventional optimization methods, such as metaheuristics.
Objective 3:
Stability analysis and synthesis of stabilizing control laws for a class of switched and time-delay Takagi-Sugeno (TS) fuzzy systems.
The stability study is based mainly on algebraic approaches leading to matrix norm inequalities.
Stability analysis and synthesis of stabilizing control laws for a class of switched and time-delay Takagi-Sugeno (TS) fuzzy systems.
The stability study is based mainly on algebraic approaches leading to matrix norm inequalities.
Axis 2: Intelligent Approaches for the Control of Electrical Machines and Optimization of Renewable Energy Sources
This axis, related to the field of Electrical Systems, aims to develop intelligent methods for maximum power point tracking (MPPT) in renewable energy sources such as photovoltaic generators and wind turbines, operating under variable weather conditions — either in standalone mode (e.g., pumping systems) or grid-connected mode — using soft computing techniques.
Objective 1:
Optimization of photovoltaic and wind generator management through intelligent approaches.
Enhancing the performance of these generators is crucial for improving the competitiveness and efficiency of renewable energy integration within global energy production systems.
Enhancing the performance of these generators is crucial for improving the competitiveness and efficiency of renewable energy integration within global energy production systems.
Objective 2:
Study of digital MPPT control principles using both conventional and non-conventional methods under variable weather conditions, and experimental validation on a photovoltaic system of the different control strategies developed
Axis 3: Intelligent Approaches for Signal and Image Processing
This axis, which belongs to the field of Signal and Image Processing, focuses on developing algorithms for feature extraction, segmentation, and recognition in telecommunications and image processing systems using metaheuristic optimization techniques.
Objective 1:
Development of multi-level segmentation and 2D/3D image recognition algorithms based on metaheuristic methods, with the goal of designing hardware architectures suitable for FPGA synthesis.
Objective 2:
Design and synthesis of signal detection algorithms for telecommunications systems based on intelligent methods.
The objective is to develop a quasi-optimal detector offering high performance with low computational complexity, making it suitable for FPGA hardware implementation.
The objective is to develop a quasi-optimal detector offering high performance with low computational complexity, making it suitable for FPGA hardware implementation.
Axis 4: Implementation of Intelligent Techniques on Embedded Systems
This axis, related to the Electronics discipline, focuses on the design and implementation of intelligent techniques — including fuzzy inference systems, neural networks, and metaheuristic algorithms — on embedded systems, mainly FPGA-based.
Thanks to their parallel architecture, FPGAs allow for faster execution times, making these implementations ideal for real-time applications, such as video processing.
Objective:
Study of design methodologies and implementation strategies for fuzzy systems, neural networks, and metaheuristics on FPGA-based embedded platforms, in order to optimize execution time and performance.