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This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Robotics and Automation (IJRA)
Khaddouj Ben Meziane
Higher Institute of Engineering and Business (ISGA)
Automotive Engineering Control & Systems Engineering Electrical & Electronics Engineering

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Optimized fuzzy PI controller for variable speed wind turbine using DE algorithm Khaddouj Ben Meziane; Faiza Dib; Nabil Benaya; Ismail Boumhidi
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i3.pp1684-1693

Abstract

To design an optimal fuzzy proportional-integral (PI) controller for the variable speed wind turbine systems, a new differential evolution (DE) algorithm approach is developed in this paper. We have investigated a fuzzy PI controller, in which fuzzy rules are applied to adapt the parameters of the PI controller founded on the error and its first-time derivative. The fuzzy PI controller's inputs and outputs are tuned using the DE optimization method. The superiority of the suggested (DE fuzzy PI) controller has been proved by comparing the results with (fuzzy PI) and only the PI controller applied to the wind turbine system. In comparison to a fuzzy controller with parameters selected by a human operator, the numerical validation results of the suggested approach (DE fuzzy PI) have shown good performance in terms of robustness, pursuit, and response time.
Design and implementation of NMPC for a two-DOF robotic arm using CasADi Lahcen Boulbalah; Faiza Dib; Nabil Benaya; Khaddouj Ben Meziane
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i2.pp307-318

Abstract

Achieving accurate joint-space tracking in multi-link robotic arms is complicated by strong configuration-dependent nonlinearities and mandatory actuator limits that classical controllers are structurally unable to enforce. This paper presents a nonlinear model predictive control (NMPC) scheme for a two-degree-of-freedom (2-DOF) serial robotic arm, implemented within the CasADi symbolic computing environment to leverage automatic differentiation and sparse interior-point solving. The complete set of Lagrangian equations of motion-inertia, Coriolis, and gravity terms-is incorporated directly into the optimizer's prediction model through fourth-order Runge-Kutta (RK4) integration, eliminating the need for linearization. Torque, velocity, and angle bounds are imposed as native hard inequality constraints at every step of the finite-horizon optimization. Systematic simulations pit the proposed NMPC against a Ziegler-Nichols-tuned decentralized PID at two distinct sampling periods. The NMPC achieved a 95% reduction in peak tracking error relative to PID (0.0058 rad vs. 0.1347 rad for Joint 1), with mean error decreases of 64.65% and 57.58% for Joints 1 and 2 respectively, at an average solver time of 0.053 s-comfortably within the 0.1 s control cycle. The findings demonstrate that online NMPC with unabridged nonlinear dynamics is computationally practical for real-time joint control on standard computing hardware.
Co-Authors Faiza Dib Ismail Boumhidi Lahcen Boulbalah Nabil Benaya