In this paper, the leaky acoustic microwaves (LAW) in a piezoelectric substrate (Lithium Niobate LiNbO3 Cut Y-X) were studied. The main method for this research was classification using a probabilistic neural network (PNN).The originality of this method is in the accurate values it provides. In our case, this technique was helpful in identifying undetectable waves, which are difficult to identify by classical methods. Moreover, all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity were classified in order to build a model from which we could easily note the Leaky waves. Accurate values of the coefficient attenuation and acoustic velocity for Leaky waves were obtained. Hence, in this study, the focus was on the interesting modeling and realization of acoustic microwave devices (radiating structures) based on the propagation of acoustic microwaves

In this paper, the leaky acoustic microwaves (LAW) in a piezoelectric substrate (Lithium Niobate LiNbO3 Cut Y-X) were studied. The main method for this research was classification using a probabilistic neural network (PNN).The originality of this method is in the accurate values it provides. In our case, this technique was helpful in identifying undetectable waves, which are difficult to identify by classical methods. Moreover, all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity were classified in order to build a model from which we could easily note the Leaky waves. Accurate values of the coefficient attenuation and acoustic velocity for Leaky waves were obtained. Hence, in this study, the focus was on the interesting modeling and realization of acoustic microwave devices (radiating structures) based on the propagation of acoustic microwaves

In this paper, the leaky acoustic microwaves (LAW) in a piezoelectric substrate (Lithium Niobate LiNbO3 Cut Y-X) were studied. The main method for this research was classification using a probabilistic neural network (PNN).The originality of this method is in the accurate values it provides. In our case, this technique was helpful in identifying undetectable waves, which are difficult to identify by classical methods. Moreover, all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity were classified in order to build a model from which we could easily note the Leaky waves. Accurate values of the coefficient attenuation and acoustic velocity for Leaky waves were obtained. Hence, in this study, the focus was on the interesting modeling and realization of acoustic microwave devices (radiating structures) based on the propagation of acoustic microwaves

This paper presents the second-order sliding mode controller (SOSMC) of a micro-positioning stage piezoelectric model actuator (PEA), where the C-H model parameters are adopted to describe the hysteresis behavior and identified through particle swarm optimization. In this technique, two control algorithms are developed. The first one is the so-called twisting algorithm (TA). The control appears explicitly in the second surface derivative, and in a discontinuous control action that ensures a sliding regime mode. The second one, the super twisting algorithms (STA) has been developed and analyzed for systems. The use of both algorithms gives a significant reduction in chattering as compared to the standard sliding mode control. It is shown that the STA case offers better performances than TA. Simulation results are presented to demonstrate the advantage of SOSMC over SMC.

This paper presents the second-order sliding mode controller (SOSMC) of a micro-positioning stage piezoelectric model actuator (PEA), where the C-H model parameters are adopted to describe the hysteresis behavior and identified through particle swarm optimization. In this technique, two control algorithms are developed. The first one is the so-called twisting algorithm (TA). The control appears explicitly in the second surface derivative, and in a discontinuous control action that ensures a sliding regime mode. The second one, the super twisting algorithms (STA) has been developed and analyzed for systems. The use of both algorithms gives a significant reduction in chattering as compared to the standard sliding mode control. It is shown that the STA case offers better performances than TA. Simulation results are presented to demonstrate the advantage of SOSMC over SMC.

This paper presents the second-order sliding mode controller (SOSMC) of a micro-positioning stage piezoelectric model actuator (PEA), where the C-H model parameters are adopted to describe the hysteresis behavior and identified through particle swarm optimization. In this technique, two control algorithms are developed. The first one is the so-called twisting algorithm (TA). The control appears explicitly in the second surface derivative, and in a discontinuous control action that ensures a sliding regime mode. The second one, the super twisting algorithms (STA) has been developed and analyzed for systems. The use of both algorithms gives a significant reduction in chattering as compared to the standard sliding mode control. It is shown that the STA case offers better performances than TA. Simulation results are presented to demonstrate the advantage of SOSMC over SMC.

This paper presents the second-order sliding mode controller (SOSMC) of a micro-positioning stage piezoelectric model actuator (PEA), where the C-H model parameters are adopted to describe the hysteresis behavior and identified through particle swarm optimization. In this technique, two control algorithms are developed. The first one is the so-called twisting algorithm (TA). The control appears explicitly in the second surface derivative, and in a discontinuous control action that ensures a sliding regime mode. The second one, the super twisting algorithms (STA) has been developed and analyzed for systems. The use of both algorithms gives a significant reduction in chattering as compared to the standard sliding mode control. It is shown that the STA case offers better performances than TA. Simulation results are presented to demonstrate the advantage of SOSMC over SMC.

In this work, the modeling and the sliding mode control of a self-excited asynchronous generator integrated in a wind energy conversion system is studied. The dc-link voltage and frequency output by the wind turbine depend on the wind intensity applied to the turbine and load. The goal of the study is to increase energy quality and to achieve a stabilization of dc-link voltage and frequency values based on sliding mode control. This method offers stability and robustness against external disturbances. However, this method is based in the power converter to improve the excellent dynamic of wind energy conversion system to meet the connection to the main grid. The simulation results show the efficiency and reliability of the proposed control method.

In this work, the modeling and the sliding mode control of a self-excited asynchronous generator integrated in a wind energy conversion system is studied. The dc-link voltage and frequency output by the wind turbine depend on the wind intensity applied to the turbine and load. The goal of the study is to increase energy quality and to achieve a stabilization of dc-link voltage and frequency values based on sliding mode control. This method offers stability and robustness against external disturbances. However, this method is based in the power converter to improve the excellent dynamic of wind energy conversion system to meet the connection to the main grid. The simulation results show the efficiency and reliability of the proposed control method.

In this work, the modeling and the sliding mode control of a self-excited asynchronous generator integrated in a wind energy conversion system is studied. The dc-link voltage and frequency output by the wind turbine depend on the wind intensity applied to the turbine and load. The goal of the study is to increase energy quality and to achieve a stabilization of dc-link voltage and frequency values based on sliding mode control. This method offers stability and robustness against external disturbances. However, this method is based in the power converter to improve the excellent dynamic of wind energy conversion system to meet the connection to the main grid. The simulation results show the efficiency and reliability of the proposed control method.