In this paper a new robust adaptive speed controller algorithm for AC motor drives is presented. The main feature of this algorithm is that minimum synthesis is required to implement the strategy. MCS algorithm is a significant development of MRAC. The stability of the proposed system is achieved through Popov’s Hyperstability criteria. The new algorithm appeared to be robust in the face of totally unknown plant dynamics, external disturbances and parameter variations with the plant. Finally, a new approach has been successfully implemented on DTC-SVM. Extensive simulation results are presented to validate the proposed technique. The system is tested at different speeds and a very satisfactory performance has been achieved.

Robust Wheel Slip for Vehicle Anti-lock Braking System with Fuzzy Sliding Mode Controller (FSMC)

LATRECHE, S, and Said Benaggoune. 2020. “Robust Wheel Slip for Vehicle Anti-lock Braking System with Fuzzy Sliding Mode Controller (FSMC)”. Engineering, Technology & Applied Science Research 10 (5) : 6368-6373. Publisher's Version Abstract

Anti-lock Braking System (ABS) is used in automobiles to prevent slipping and locking of wheels after the brakes are applied. Its control is a rather complicated problem due to its strongly nonlinear and uncertain characteristics. The aim of this paper is to investigate the wheel slip control of the ground vehicle, comprising two new strategies. The first strategy is the Sliding Mode Controller (SMC) and the second one is the Fuzzy Sliding Mode Controller (FSMC), which is a combination of fuzzy logic and sliding mode, to ensure the stability of the closed-loop system and remove the chattering phenomenon introduced by classical sliding mode control. The obtained simulation results reveal the efficiency of the proposed technique for various initial road conditions.

Neuro-fuzzy Sliding Mode Controller Based on a Brushless Doubly Fed Induction Generator

Ouada, L, Said Benaggounea, and Sebti Belkacem. 2020. “Neuro-fuzzy Sliding Mode Controller Based on a Brushless Doubly Fed Induction Generator”. IJE TRANSACTIONS B: Applications 33 (2) : 248-256. Publisher's Version Abstract

The combination of neural networks and fuzzy controllers is considered as the most efficient approach for different functions approximation, and indicates their ability to control nonlinear dynamical systems. This paper presents a hybrid control strategy called Neuro-Fuzzy Sliding Mode Control (NFSMC) based on the Brushless Doubly fed Induction Generator (BDFIG). This replaces the sliding surface of the control to exclude chattering phenomenon caused by the discontinuous control action. This technique offers attractive features, such as robustness to parameter variations. Simulations results of 2.5 KW BDFIG have been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC) and sliding mode control (SMC). We compare the static and dynamic characteristics of the three control techniques under the same operating conditions and in the same simulation configuration. The proposed controller schemes (NFSMC) are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effects of parametric uncertainties not affecting system performance.

Hybrid Fuzzy Reference Signal Tracking Control of a Doubly Fed Induction Generator

Choug, N. 2020. “Hybrid Fuzzy Reference Signal Tracking Control of a Doubly Fed Induction Generator”. International Journal of Engineering (IJE) 33 (4) : 567-574. Publisher's Version Abstract

This paper presents a hybrid scheme for the control of active and reactive powers using the direct vector control with stator flux orientation (SFO) of the DFIG. The hybrid scheme consists of Fuzzy logic, Reference Signal Tracking (F-RST) controllers. The proposed (F-RST) controller is compared with the classical Proportional-Integral (PI) and the Polynomial (RST) based on the pole placement theory. The various strategies are analyzed and compared in terms of tracking, robustness, and sensitivity to the speed variation. Simulations are done using MATLAB software. The simulation results prove that the proposed approach leads to good performances such as the tracking test, the rejection of disturbances and the robustness concerning the parameter variations. The hybrid controller is much more efficient compared to those of PI and RST controller, it also improves the performance of the powers and ensures some important strength despite the parameter variation of the DFIG.

Hybrid Fuzzy Reference Signal Tracking Control of a Doubly Fed Induction Generator

Choug, N., Said Benaggoune, and Sebti Belkacem. 2020. “Hybrid Fuzzy Reference Signal Tracking Control of a Doubly Fed Induction Generator”. International Journal of Engineering (IJE) 33 (4) : 567-574. Publisher's Version Abstract

Two Types of Fuzzy Logic Controllers for the Speed Control of the Doubly-Fed Induction Machine

SAIDI, Abderazak, et al. 2020. “Two Types of Fuzzy Logic Controllers for the Speed Control of the Doubly-Fed Induction Machine”. Advances in Electrical and Computer Engineering 20 (3). Publisher's Version Abstract

The paper presents two fuzzy logic control algorithms: type-1 and type-2. These two nonlinear techniques are used for adjust the speed control with a direct stator flux orientation control of a doubly fed induction motor. The effectiveness of the proposed control strategy is evaluated under different operating conditions such as of reference speed and for load torque step changes at nominal parameters and in the presence of parameter variation (stator resistance, rotor resistance and moment of inertia). The results of the simulation of the doubly fed induction motor velocity control have shown that fuzzy type-2 ensures better dynamic performances with respect to fuzzy type-1 control, even by parametric variations and external disturbances.

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Robust Wheel Slip for Vehicle Anti-lock Braking System with Fuzzy Sliding Mode Controller (FSMC)

Neuro-fuzzy Sliding Mode Controller Based on a Brushless Doubly Fed Induction Generator

Hybrid Fuzzy Reference Signal Tracking Control of a Doubly Fed Induction Generator

Hybrid Fuzzy Reference Signal Tracking Control of a Doubly Fed Induction Generator

Two Types of Fuzzy Logic Controllers for the Speed Control of the Doubly-Fed Induction Machine