Yassin Farhat- Contractual Assistant
- University of Gabès
Yassin Farhat
- Contractual Assistant
- University of Gabès
About
10
Publications
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Introduction
Yassin FARHAT received the Engineering Diploma in Electrical and Automatic Engineering, in 2018, from the Graduate School of Applied Sciences and Technology of Gabes-Tunisia. He is received the PhD (Doctoral degree) in Electrical Engineering from the National School of Engineers of Gabes-Tunisia, in 2022. He is with Research laboratory of numerical Control of Industrial Processes since 2018. His areas of interest include Nonlinear Process Identification, Neural Emulation and Adaptive Neural Cont
Current institution
Publications
Publications (10)
This paper proposes an indirect adaptive control method based on recurrent neural networks. To achieve satisfactory closed-loop performances, a neural emulator (NE) and a neural controller (NC) adapting rates are established using the multiobjective particle swarm optimization (MOPSO) algorithm. The proposed MOPSO algorithm has been designed to min...
Searching an optimal value of the neural emulator adaptive rate presents a great problem. Indeed, a new scheme of neural emulators based on the Particle Swarm Optimization (PSO) algorithm for nonlinear systems is adopted in this paper. The main goal of this approach consists in adjusting effectively the neural emulator adaptive rate in order to acc...
This paper deals with a new weight-updating algorithm using Lyapunov stability theory (LST) for the training of a neural emulator (NE), of nonlinear systems, connected by an autonomous algorithm inspired from the real-time recurrent learning (RTRL). The proposed method is formulated by an inequality-constraint optimization problem where the Lagrang...
In the present work, an indirect adaptive neural control method for nonlinear systems having unknown dynamics is proposed. The proposed control architecture is composed by a neural emulator (NE) and a neural controller (NC) where a new decoupled variable learning rates (VLRs) combined with Taylor development (TD) are used to train the NE and the NC...
An arbitrary choice of the neural controller adaptive rate can have a negative effect on the performance of the closed-loop system. In this study, we propose a novel methodology for neural controller adaptive rate using Particle Swarm Optimization algorithm. The developed control scheme is composed of a recurrent neural networks emulator and contro...
The application of neural networks can present some limitations for the control of strongly nonlinear systems. In this paper, a new control scheme based on a neural multicontroller (NMC) is proposed. Indeed, the developed strategy considers a set of local neural controllers which adapt their parameters thanks to an online adaptation algorithm. The...
In this paper, a stability analysis strategy of nonlinear control systems is proposed. An adaptive neural control scheme composed of an emulator, and a controller with decoupled adaptive rates is considered. A Lyapunov function based on tracking error dynamic is retained and an online adjusting technique of the neural controller adaptive rate is ad...
