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Novel control strategy for the global model of wind turbine El Fadili, Yattou; Berrada, Youssef; Boumhidi, Ismail
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp258-267

Abstract

This paper presents a new nonlinear control for the overall model of a three-blade horizontal axis variable speed wind turbine (VSWT) including mechanical and electrical parts, with the aim of improving its performance and making it more profitable. The proposed control is an extension of the classical sliding mode control (SMC) by converting its sliding surface into a sliding sector. The classical SMC approach is widely used for nonlinear systems due to its stability against parameter variation, it is robustness against modeling uncertainties, its good results against external disturbances, and its ease of implementation in real time. Unfortunately, the SMC has a major drawback related to the chattering phenomenon. This phenomenon is due to the utility of a higher switching gain in the case of large uncertainties, it causes high-frequency oscillations once the sliding regime is reached, and it can cause a loss of accuracy by influencing the input control variables. This is the reason that aims to develop a new control law to eliminate the chattering and to guarantee stability, which is demonstrated by the Lyapunov theory. The effectiveness of the developed control is compared with the SMC and is illustrated by numerical simulations using MATLAB toolboxes.
Adaptive control based neural network sliding mode approach for two links robot Massou, Siham; Boumhidi, Ismail
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i4.pp2546-2556

Abstract

In order to improve the control accuracy of the robot manipulator, the sliding mode control combined with the adaptive neural network (ANNSMC) is proposed. Sliding mode control (SMC) is a nonlinear control recognized for its efficiency, easy tuning and implementation, accuracy and robustness. However, higher amplitude of chattering is produced due to the higher switching gain to handle the large uncertainties. For the purpose of reducing this gain, the uncertain parts of the system are estimated using neural network (NN) with on-line training using back propagation (BP) technique. The results of the online interconnection weights between the input and the hidden layers and between the hidden and the output layers are injected offline in order to improve the network performance in term of the convergence speed. In order to reduce the response time caused by the online training, the obtained output and input weights are updated using the adaptive laws derived from the Lyapunov stability approachthe proposed control ANNSMC has improved the convergence speed with 41.13% for the first link and 40.15% for the second link comparing to NNSMC. The simulation result illustrates the performance of the proposed approach by using MATLAB and the control action suggested did not manifest any chattering behavior.