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.

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.

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.

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.

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.

This paper deals with the robust power control of a grid-connected brushless doubly-fed induction generator (BDFIG) driven by the variable speed wind turbine. With the using of a super twisting algorithm which is a high-order sliding mode controller (HOSMC). This approach guarantees both the dynamic performance and the same robustness as traditional first order (SMC) algorithm and reduces the chattering phenomenon, which is the biggest disadvantage in the implementation of this technique. The developed algorithm relies on the decoupling control by implementing the strategy of oriented grid flux vector control. In order to enhance the desired performances, an attempt is made by controlling the generated stator active and reactive powers in a linear and decoupled manner to ensure the global asymptotical stability, HOSMC approach is implemented. Therefore, an optimal operation of the BDFIG in sub-synchronous operation is used in addition to the stator power flows where the stator power factor is kept in a unity. The suggested method is examined with the Matlab/Simulink software. The performances and the feasibility of the designed control are illustrated by simulation results.

In this paper, we are interested in the adaptive fuzzy control a technique has been studied and applied, namely adaptive fuzzy control based on theory of Lyapunov. The system based on the stability theory is used to approximate the gains k_e and k_dce to ensure the stability of the control in particular time, simulations results obtained by using MATLAB environment gives that the fuzzy adaptive control more robust, also it has superior dynamics performances. The results and test of robustness will be presented.

This paper presents a fuzzy logic controller destined to the doubly-fed induction motor (DFIM) speed controlling. It solves the problems associated with the conventional IP (Integral Proportional) controller. This fuzzy logic controller is based on the decoupling control to enhance robustness under different operating conditions such as load torque and in the presence of parameters variation. The simulation results for various scenarios show the high performances of the proposed control in terms of piloting effectiveness, precision, rapidity and stability for the high powers DFIM operating at variable speeds.

This study presents analysis, control and comparison of three hybrid approaches for the direct torque control (DTC) of the dual star induction motor (DSIM) drive. Its objective consists of combining three different heuristic optimization techniques including PID-PSO, Fuzzy-PSO and GA-PSO to improve the DSIM speed controlled loop behavior. The GA and PSO algorithms are developed and implemented into MATLAB. As a result, fuzzy-PSO is the most appropriate scheme. The main performance of fuzzy-PSO is reducing high torque ripples, improving rise time and avoiding disturbances that affect the drive performance.

This work deals with the performance improvement study of the direct torque control (DTC) of a Double Star Permanent Magnet Synchronous Machine (DSPMSM) based on Second Order Sliding Mode Control (SOSMC), powered by two voltage source inverters. DTC control using conventional PI regulators has certain disadvantages such as significant flux, torque ripples and sensitivity to parametric variations. To overcome these drawbacks, we apply a new type with more robust regulators such as the second order sliding mode control. Simulation results demonstrate the feasibility and validity of the proposed DTCSOSMC system by effectively accelerating system response, reducing torque and flux ripple and a very satisfactory performance has been achieved.

Robust control often requires some adaptive approach in evaluating systems dynamics to handle parameters variations and external disturbances. Therefore, an error due to dynamics approximation is inevitably added to uncertainties already present in the model. This issue is addressed in this paper, through the combination of two robust techniques, Hinf and synergetic control. These latter are used to ensure reducing tracking error in the overall closed-loop system while guaranteeing stability via Lyapunov synthesis. With the aim of handling parameters variations, an indirect adaptive fuzzy scheme is used to elaborate system model. Simulation studies are conducted to assess the proposed approach on two practical systems, and the results are compared to a sliding mode proportional integral (PI)-based technique. It is to be noted that a large class of systems depicted as control affine systems will be considered in this paper. An induction motor and an inverted pendulum representing, respectively, a linear and a nonlinear system are utilized in this study showing improvement due to the suggested approach, in overall performance over its sliding mode control counterpart.

Finite time convergence based on robust synergetic control (SC) theory and terminal attractor techniques is investigated. To this end a fast terminal synergetic control law (FTSC) is applied to drive a DC–DC Buck converter via simulation and through a dSpace based experimental setup to validate the approach. As robust as sliding mode control, the synergetic approach used is chattering free and provides rapid convergence. Efficacy of the proposed fast terminal synergetic controller is tested for step load change and output voltage variation and results compared to classical synergetic and PI control. Experimental validation using dSpace DS1104 confirms the results obtained in simulation showing the soundness of this approach compared to synergetic and PI controllers.

In this paper, a new control stra¬tegy is deve¬loped; an adaptive fuzzy controller based on Lyapunov's stability theory (AFLC) recalculates the real-time PI-fuzzy gains and combines the advantages of two robust techniques i.e. the fuzzy logic control and the adaptive one. For the new adaptive fuzzy control, we followed two steps: in the first one, a PI-fuzzy controller is designed, in the second step, the gains of a fuzzy regulator are determined. 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.

We present in this paper a comparative study between two control strategies of electrical machines: Direct Torque Control (DTC-SVM), and Predictive Direct Torque Control (MPDTC). The first algorithm based on PI controllers, where the torque and the flux are regulated by a PI controller; we present a conception method of the PI controllers, associated with the flux and the torque regulation loops and gives analytical formulas for the proportional and integral gains. We also present in the second algorithm. The Predictive Direct Torque Control based on the linearization input-output of the machine. The technique of the linearization is used to give a model linearized and uncoupled from the machine for the anticipation of future behavior of the output. Following the formulation of both approaches, their implementation in the Matlab-Simulink environment has been treated. It has been found that the second approach MPDTC yields high dynamic performances in the whole speed range. These performances are characterized by a low torque ripple. However, requires much more CPU time than the first one.

In this work, we introduced a new robust hybrid control to an induction motor (IM), based on the theory of fuzzy logic and variable structure with sliding-mode control (SMC). As the variations of both control system parameters and operating conditions occur, the conventional control methods may not be satisfied further. Fuzzy tuning schemes are employed to improve control performance and to reduce chattering in the sliding mode. The combination of these two theories has given high performance and fast dynamic response with no overshoot. As it is very robust, it is insensitive to process parameters variation and external disturbances.

A new control technique of an induction motor is undertaken through a robust approach tagged synergetic control. Like the sliding mode (SMC) approach the system state trajectories are forced to evolve on a designer chosen manifold according to performance specifications. But unlike SMC, synergetic control relies on a continuous control law thus preventing unwanted chattering to occur. Fuzzy sets are used to approximate unknown system functions and system stability conditions are derived.

This paper investigates the application of the generalized predictive control (GPC), in order to control the speed of the induction motor. This application is based on four main ideas :  reproducing decision basic mechanisms of human behaviour; the creation  of  an  anticipatory  effect  by  operation  of  the  path  to  follow  in  the  future;  defining  a  numerical  prediction  model; minimization of a quadratic criterion with finite horizon and the principle of receding horizon. The results proved that the induction motor with the MPC speed controller has superior transient response, and good robustness in face of uncertainties including load disturbance. Moreover, accurate tracking performance has been achieved.

This study aims are data acquisition, control and online modeling of an oil collection pipeline network using a SCADA «Supervisory Control and Data Acquisition» system, allowing the optimization of this network in real time by creating more exact models of onsite facilities. Indeed, fast development of computing systems makes obsolete usage of old systems for which maintenance became more and more expensive and their performances don't comply any more with modern company operations. SCADA system is a telemetry and control system adapted for particular requirements of an oilfield management. Thanks to its different functions, we take advantage of this system to solve production problems especially those related to oil collecting pipeline network. In fact this network is confronted to some problems, in particular pressure losses which has significant effect on the production. This problem can be taken under control by the awareness of pipeline network operation and all its process data (especially junctions) in real time. This will allow online creation of representative and accurate computerized models for the oil collecting pipeline network including producing wells, collecting pipelines, manifolds and others facilities.

We present, in this paper, a DTC-SVM control algorithm of an induction machine based on PI controllers. Both the torque and the flux are regulated by a PI controller, where the truth table and the hysteresis are eliminated. We present here a conception method of the PI controllers, associated with the flux and the torque regulation loops and gives analytical formulas for the proportional and integral parameters, depending on the parameters of the induction machine. The effectiveness of the proposed approach is shown by simulation results.